perf(deepseek): Multi-stream overlap of indexer wk+weights_proj with QKV-A for DeepSeek V3.2 NSA

Signed-off-by: Alexander Matveev <amatveev@redhat.com>

.buildkite/hardware_tests/cpu.yaml

@@ -46,7 +46,7 @@ steps:
   - tests/models/language/pooling/
   commands:
     - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
       pytest -x -v -s tests/models/language/generation -m cpu_model
       pytest -x -v -s tests/models/language/pooling -m cpu_model"
 
@@ -99,7 +99,7 @@ steps:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
       pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
-  parallelism: 2
+  parallelism: 3
 
 - label: "Arm CPU Test"
   depends_on: []

.buildkite/release-pipeline.yaml

@@ -98,8 +98,15 @@ steps:
     commands:
       - "bash .buildkite/scripts/generate-and-upload-nightly-index.sh"
 
+  - block: "Unblock to build release Docker images"
+    depends_on: ~
+    key: block-build-release-images
+    if: build.env("NIGHTLY") != "1"
+
   - group: "Build release Docker images"
     key: "build-release-images"
+    depends_on: block-build-release-images
+    allow_dependency_failure: true
     steps:
       - label: "Build release image - x86_64 - CUDA 12.9"
         depends_on: ~
@@ -617,6 +624,8 @@ steps:
   - label: ":docker: Build release image - x86_64 - ROCm"
     id: build-rocm-release-image
     depends_on:
+      - step: block-build-release-images
+        allow_failure: true
       - step: build-rocm-base-wheels
         allow_failure: false
     agents:

.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh

@@ -51,6 +51,7 @@ function cpu_tests() {
     set -e
     pytest -x -v -s tests/kernels/test_onednn.py
     pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+    pytest -x -v -s tests/kernels/core/test_cpu_activation.py
     pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
 
   # basic online serving

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -16,5 +16,5 @@ echo "--- :docker: Building Docker image"
 docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g "$IMAGE_NAME" \
+docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 -e VLLM_CPU_ATTN_SPLIT_KV=0 --shm-size=4g "$IMAGE_NAME" \
         timeout "$TIMEOUT_VAL" bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"

.buildkite/test-amd.yaml

@@ -769,7 +769,7 @@ steps:
   - tests/kernels/helion/
   - vllm/platforms/rocm.py
   commands:
-  - pip install helion==0.3.3
+  - pip install helion==1.0.0
   - pytest -v -s kernels/helion/
 
 

.buildkite/test_areas/distributed.yaml

@@ -196,7 +196,6 @@ steps:
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
     - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
-    - TP_SIZE=1 DP_SIZE=2 pytest -v -s tests/v1/distributed/test_eagle_dp.py
 
 - label: Distributed Tests (2 GPUs)(B200)
   device: b200

.buildkite/test_areas/kernels.yaml

@@ -155,7 +155,7 @@ steps:
   - vllm/utils/import_utils.py
   - tests/kernels/helion/
   commands:
-    - pip install helion==0.3.3
+    - pip install helion==1.0.0
     - pytest -v -s kernels/helion/
 
  

.buildkite/test_areas/models_basic.yaml

@@ -4,7 +4,6 @@ depends_on:
 steps:
 - label: Basic Models Tests (Initialization)
   timeout_in_minutes: 45
-  device: h200_18gb
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -73,3 +72,18 @@ steps:
     - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
+
+- label: Transformers Backward Compatibility Models Test
+  working_dir: "/vllm-workspace/"
+  optional: true
+  soft_fail: true
+  commands:
+    - pip install transformers==4.57.5
+    - pytest -v -s tests/models/test_initialization.py
+    - pytest -v -s tests/models/test_transformers.py
+    - pytest -v -s tests/models/multimodal/processing/
+    - pytest -v -s tests/models/multimodal/test_mapping.py
+    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    # Whisper needs spawn method to avoid deadlock
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

.buildkite/test_areas/spec_decode.yaml

@@ -42,3 +42,16 @@ steps:
     - tests/v1/e2e/spec_decode/
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
+
+- label: DFlash Speculators Correctness
+  timeout_in_minutes: 30
+  device: h100
+  optional: true
+  num_devices: 1
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/model_executor/models/qwen3_dflash.py
+    - tests/v1/spec_decode/test_speculators_dflash.py
+  commands:
+    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
+    - pytest -v -s v1/spec_decode/test_speculators_dflash.py -m slow_test

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,7 +15,6 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTT
 - [ ] The test plan, such as providing test command.
 - [ ] The test results, such as pasting the results comparison before and after, or e2e results
 - [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
-- [ ] (Optional) Release notes update. If your change is user facing, please update the release notes draft in the [Google Doc](https://docs.google.com/document/d/1YyVqrgX4gHTtrstbq8oWUImOyPCKSGnJ7xtTpmXzlRs/edit?tab=t.0).
 </details>
 
 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)

cmake/cpu_extension.cmake

@@ -360,6 +360,7 @@ set(VLLM_EXT_SRC
 if (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND)
     set(VLLM_EXT_SRC
         "csrc/cpu/shm.cpp"
+        "csrc/cpu/activation_lut_bf16.cpp"
         ${VLLM_EXT_SRC})
 endif()
 

csrc/cpu/activation_lut_bf16.cpp

@@ -0,0 +1,71 @@
+#include "cpu_types.hpp"
+
+#include <array>
+#include <cstdint>
+#include <mutex>
+#include <string>
+
+#include <ATen/ops/empty.h>
+#include <ATen/ops/gelu.h>
+#include <c10/util/BFloat16.h>
+
+constexpr uint32_t ActivationLutSize = 1u << 16;
+
+at::Tensor gelu_reference(const at::Tensor& x) { return at::gelu(x, "none"); }
+
+void maybe_init_activation_lut_bf16(
+    uint16_t* lut, std::once_flag& once,
+    at::Tensor (*activation)(const at::Tensor&)) {
+  std::call_once(once, [&]() {
+    auto lut_input =
+        at::empty({static_cast<int64_t>(ActivationLutSize)},
+                  at::TensorOptions().device(at::kCPU).dtype(at::kFloat));
+    auto* lut_input_ptr = lut_input.data_ptr<float>();
+#pragma omp parallel for
+    for (uint32_t i = 0; i < ActivationLutSize; ++i) {
+      lut_input_ptr[i] = c10::detail::f32_from_bits(static_cast<uint16_t>(i));
+    }
+
+    auto lut_output = activation(lut_input);
+    const auto* lut_output_ptr = lut_output.data_ptr<float>();
+#pragma omp parallel for
+    for (uint32_t i = 0; i < ActivationLutSize; ++i) {
+      lut[i] = c10::detail::round_to_nearest_even(lut_output_ptr[i]);
+    }
+  });
+}
+
+void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
+                         const uint16_t* lut, const char* op_name) {
+  TORCH_CHECK(input.scalar_type() == at::kBFloat16, op_name,
+              ": input must be bfloat16");
+  TORCH_CHECK(out.scalar_type() == at::kBFloat16, op_name,
+              ": out must be bfloat16");
+  TORCH_CHECK(input.is_contiguous(), op_name, ": input must be contiguous");
+  TORCH_CHECK(out.is_contiguous(), op_name, ": out must be contiguous");
+
+  const auto* src =
+      reinterpret_cast<const uint16_t*>(input.data_ptr<at::BFloat16>());
+  auto* dst = reinterpret_cast<uint16_t*>(out.data_ptr<at::BFloat16>());
+  const int64_t n = input.numel();
+
+  CPU_KERNEL_GUARD_IN(activation_lut_bf16_impl)
+#pragma omp parallel for
+  for (int64_t i = 0; i < n; ++i) {
+    dst[i] = lut[src[i]];
+  }
+  CPU_KERNEL_GUARD_OUT(activation_lut_bf16_impl)
+}
+
+void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
+                         const std::string& activation) {
+  if (activation == "gelu") {
+    static std::array<uint16_t, ActivationLutSize> lut{};
+    static std::once_flag once;
+    maybe_init_activation_lut_bf16(lut.data(), once, gelu_reference);
+    activation_lut_bf16(out, input, lut.data(), "gelu_lut");
+    return;
+  }
+
+  TORCH_CHECK(false, "Unsupported activation: ", activation);
+}

csrc/cpu/cpu_attn_impl.hpp

@@ -147,6 +147,9 @@ struct AttentionMetadata {
       case ISA::NEON:
         ss << "NEON, ";
         break;
+      case ISA::VXE:
+        ss << "VXE, ";
+        break;
     }
     ss << "workitem_group_num: " << workitem_group_num
        << ", reduction_item_num: " << reduction_item_num

csrc/cpu/torch_bindings.cpp

@@ -85,6 +85,9 @@ at::Tensor int4_scaled_mm_cpu(at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros,
                               at::Tensor& w_scales,
                               std::optional<at::Tensor> bias);
 
+void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
+                         const std::string& activation);
+
 torch::Tensor get_scheduler_metadata(
     const int64_t num_req, const int64_t num_heads_q,
     const int64_t num_heads_kv, const int64_t head_dim,
@@ -231,6 +234,15 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_quick", torch::kCPU, &gelu_quick);
 
+#if (defined(__aarch64__) && !defined(__APPLE__))
+
+  ops.def(
+      "activation_lut_bf16(Tensor! out, Tensor input, str activation)"
+      " -> ()");
+  ops.impl("activation_lut_bf16", torch::kCPU, &activation_lut_bf16);
+
+#endif  // (defined(__aarch64__) && !defined(__APPLE__))
+
   // Layernorm
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(

csrc/cpu/utils.hpp

@@ -54,12 +54,34 @@ struct Counter {
 };
 
 inline int64_t get_available_l2_size() {
+#if defined(__s390x__)
+  static int64_t size = []() {
+    uint32_t l2_cache_size = 0;
+    auto caps = at::cpu::get_cpu_capabilities();
+    auto it = caps.find("l2_cache_size");
+    if (it != caps.end()) {
+      l2_cache_size = static_cast<uint32_t>(it->second.toInt());
+    }
+    if (l2_cache_size == 0) {
+      long sys_l2 = sysconf(_SC_LEVEL2_CACHE_SIZE);
+      if (sys_l2 > 0) {
+        l2_cache_size = static_cast<uint32_t>(sys_l2);
+      }
+    }
+    if (l2_cache_size == 0) {
+      l2_cache_size = 256 * 1024;
+    }
+    return static_cast<int64_t>(l2_cache_size) >> 1;  // use 50% of L2 cache
+  }();
+  return size;
+#else
   static int64_t size = []() {
     auto caps = at::cpu::get_cpu_capabilities();
     const uint32_t l2_cache_size = caps.at("l2_cache_size").toInt();
     return l2_cache_size >> 1;  // use 50% of L2 cache
   }();
   return size;
+#endif
 }
 
 template <int32_t alignment_v, typename T>

csrc/ops.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include <optional>
+#include <string>
 #include <torch/library.h>
 #include <tuple>
 

docker/Dockerfile

@@ -642,7 +642,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     else \
         BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_X86}"; \
     fi; \
-    uv pip install --system accelerate hf_transfer modelscope \
+    uv pip install --system accelerate modelscope \
         "bitsandbytes>=${BITSANDBYTES_VERSION}" "timm${TIMM_VERSION}" "runai-model-streamer[s3,gcs,azure]${RUNAI_MODEL_STREAMER_VERSION}"
 
 # ============================================================
@@ -756,9 +756,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
-ENV HF_HUB_ENABLE_HF_TRANSFER 1
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
 
 # Copy in the v1 package for testing (it isn't distributed yet)
 COPY vllm/v1 /usr/local/lib/python${PYTHON_VERSION}/dist-packages/vllm/v1

docker/Dockerfile.cpu

@@ -197,6 +197,12 @@ ADD ./.buildkite/ ./.buildkite/
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install -e tests/vllm_test_utils
 
+# enable fast downloads from hf (for testing)
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
+
 ######################### RELEASE IMAGE #########################
 FROM base AS vllm-openai
 

docker/Dockerfile.nightly_torch

@@ -272,9 +272,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
-ENV HF_HUB_ENABLE_HF_TRANSFER 1
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -r requirements/test/nightly-torch.txt

docker/Dockerfile.rocm

@@ -365,9 +365,10 @@ RUN cd /vllm-workspace \
     && python3 -m pip install pytest-shard
 
 # enable fast downloads from hf (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
-ENV HF_HUB_ENABLE_HF_TRANSFER=1
+ENV HF_XET_HIGH_PERFORMANCE=1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
 
 # install audio decode package `torchcodec` from source (required due to 
 # ROCm and torch version mismatch) for tests with datasets package

docker/Dockerfile.s390x

@@ -42,7 +42,7 @@ FROM python-install AS pyarrow
 # Build Apache Arrow
 WORKDIR /tmp
 RUN --mount=type=cache,target=/root/.cache/uv \
-    git clone https://github.com/apache/arrow.git && \
+    git clone https://github.com/apache/arrow.git  -b maint-19.0.1 && \
     cd arrow/cpp && \
     mkdir release && cd release && \
     cmake -DCMAKE_BUILD_TYPE=Release \
@@ -68,19 +68,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install -r requirements-build.txt && \
     python setup.py build_ext --build-type=$ARROW_BUILD_TYPE --bundle-arrow-cpp bdist_wheel
 
-FROM python-install AS numa-build
-# Install numactl (needed for numa.h dependency)
-WORKDIR /tmp
-RUN curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.16.tar.gz && \
-    tar -xvzf v2.0.16.tar.gz && \
-    cd numactl-2.0.16 && \
-    ./autogen.sh && \
-    ./configure && \
-    make
-
-# Set include path
-ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
-
 FROM python-install AS rust
 ENV CARGO_HOME=/root/.cargo
 ENV RUSTUP_HOME=/root/.rustup
@@ -91,6 +78,18 @@ RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && \
     rustup default stable && \
     rustup show
 
+FROM python-install AS numa-build
+WORKDIR /tmp
+RUN 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
+
+# Set include path
+ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
+
 FROM python-install AS torch-vision
 # Install torchvision
 ARG TORCH_VISION_VERSION=v0.26.0
@@ -133,7 +132,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     git clone --recursive https://github.com/numba/llvmlite.git -b v0.44.0 && \
     git clone --recursive https://github.com/numba/numba.git -b ${NUMBA_VERSION} && \
     cd llvm-project && mkdir build && cd  build && \
-    uv pip install 'cmake<4' setuptools numpy && \
+    uv pip install 'cmake<4' 'setuptools<70' numpy && \
     export PREFIX=/usr/local && CMAKE_ARGS="${CMAKE_ARGS} -DLLVM_ENABLE_PROJECTS=lld;libunwind;compiler-rt" \
     CFLAGS="$(echo $CFLAGS | sed 's/-fno-plt //g')" \
     CXXFLAGS="$(echo $CXXFLAGS | sed 's/-fno-plt //g')" \
@@ -193,27 +192,22 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     cd opencv-python && \
     python -m build --wheel --installer=uv --outdir /tmp/opencv-python/dist
 
-# Build Outlines Core
-FROM python-install AS outlines-core-builder
+## Todo(r3hankhan123): Remove guidance-builder stage once vLLM upgrades to new version of llguidance that fixes s390x issues. See https://github.com/guidance-ai/llguidance/issues/330
+FROM python-install AS guidance-builder
 WORKDIR /tmp
 ENV CARGO_HOME=/root/.cargo
 ENV RUSTUP_HOME=/root/.rustup
 ENV PATH="$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"
-COPY requirements/common.txt /tmp/requirements/common.txt
-ARG OUTLINES_CORE_VERSION
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,from=rust,source=/root/.cargo,target=/root/.cargo,rw \
     --mount=type=bind,from=rust,source=/root/.rustup,target=/root/.rustup,rw \
-    OUTLINES_CORE_VERSION=${OUTLINES_CORE_VERSION:-$(grep -E '^outlines_core\s*==\s*[0-9.]+' /tmp/requirements/common.txt | grep -Eo '[0-9.]+')} && \
-    if [ -z "${OUTLINES_CORE_VERSION}" ]; then echo "ERROR: Could not determine outlines_core version"; exit 1; fi && \
-    git clone https://github.com/dottxt-ai/outlines-core.git && \
-    cd outlines-core && \
-    git checkout tags/${OUTLINES_CORE_VERSION} && \
-    sed -i "s/version = \"0.0.0\"/version = \"${OUTLINES_CORE_VERSION}\"/" Cargo.toml && \
+    git clone https://github.com/guidance-ai/llguidance.git && \
+    cd llguidance && \
+    git checkout s390x-fix-v2  && \
     uv pip install maturin && \
-    python -m maturin build --release --out dist
+    python -m maturin build --release --out dist --compatibility linux
 
-# Final build stage
+# # Final build stage
 FROM python-install AS vllm-cpu
 ARG PYTHON_VERSION
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
@@ -229,10 +223,12 @@ ENV PKG_CONFIG_PATH="/opt/rh/gcc-toolset-14/root/usr/lib64/pkgconfig:/usr/local/
 ENV PATH="${VIRTUAL_ENV:+${VIRTUAL_ENV}/bin}:/opt/rh/gcc-toolset-14/root/usr/bin:/usr/local/bin:$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"
 ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
+# Force pure Python protobuf to avoid s390x C++ extension crashes
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 COPY . /workspace/vllm
 WORKDIR /workspace/vllm
 
-RUN --mount=type=bind,from=numa-build,src=/tmp/numactl-2.0.16,target=/numactl \
+RUN --mount=type=bind,from=numa-build,src=/tmp/numactl-2.0.19,target=/numactl \
     make -C /numactl install
 
 # Install dependencies, including PyTorch and Apache Arrow
@@ -245,22 +241,22 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,from=numba-builder,source=/tmp/llvmlite/dist,target=/tmp/llvmlite-wheels/ \
     --mount=type=bind,from=numba-builder,source=/tmp/numba/dist,target=/tmp/numba-wheels/ \
     --mount=type=bind,from=opencv-builder,source=/tmp/opencv-python/dist,target=/tmp/opencv-wheels/ \
-    --mount=type=bind,from=outlines-core-builder,source=/tmp/outlines-core/dist,target=/tmp/outlines-core/dist/ \
-     ARROW_WHL_FILE=$(ls /tmp/arrow-wheels/pyarrow-*.whl) && \
+    --mount=type=bind,from=guidance-builder,source=/tmp/llguidance/dist,target=/tmp/guidance-wheels/ \
+     ARROW_WHL_FILE=$(ls /tmp/arrow-wheels/*.whl) && \
      VISION_WHL_FILE=$(ls /tmp/vision-wheels/*.whl) && \
      HF_XET_WHL_FILE=$(ls /tmp/hf-xet-wheels/*.whl) && \
      LLVM_WHL_FILE=$(ls /tmp/llvmlite-wheels/*.whl) && \
      NUMBA_WHL_FILE=$(ls /tmp/numba-wheels/*.whl) && \
      OPENCV_WHL_FILE=$(ls /tmp/opencv-wheels/*.whl) && \
-     OUTLINES_CORE_WHL_FILE=$(ls /tmp/outlines-core/dist/*.whl) && \
-     uv pip install -v \
-        $ARROW_WHL_FILE  \
+     GUIDANCE_WHL_FILE=$(ls /tmp/guidance-wheels/*.whl) && \
+     uv pip install -v \    
+        $ARROW_WHL_FILE \
         $VISION_WHL_FILE \
         $HF_XET_WHL_FILE \
         $LLVM_WHL_FILE \
         $NUMBA_WHL_FILE \
         $OPENCV_WHL_FILE \
-        $OUTLINES_CORE_WHL_FILE \
+        $GUIDANCE_WHL_FILE \
         --index-strategy unsafe-best-match \
         -r requirements/build/cpu.txt \
         -r requirements/cpu.txt
@@ -271,6 +267,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     VLLM_TARGET_DEVICE=cpu VLLM_CPU_MOE_PREPACK=0 python setup.py bdist_wheel && \
     uv pip install "$(echo dist/*.whl)[tensorizer]"
 
+# Remove protobuf C++ extension that crashes on s390x
+RUN rm -rf /opt/vllm/lib64/python${PYTHON_VERSION}/site-packages/google/_upb/*.so \
+           /opt/vllm/lib64/python${PYTHON_VERSION}/site-packages/google/protobuf/pyext/*.so 2>/dev/null || true
+
 # setup non-root user for vllm
 RUN umask 002 && \
     /usr/sbin/useradd --uid 2000 --gid 0 vllm && \

docs/benchmarking/cli.md

@@ -37,6 +37,7 @@ th {
 | HuggingFace-Blazedit | ✅ | ✅ | `vdaita/edit_5k_char`, `vdaita/edit_10k_char` |
 | HuggingFace-ASR | ✅ | ✅ | `openslr/librispeech_asr`, `facebook/voxpopuli`,  `LIUM/tedlium`, `edinburghcstr/ami`,        `speechcolab/gigaspeech`,        `kensho/spgispeech` |
 | Spec Bench | ✅ | ✅ | `wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl` |
+| SPEED-Bench | ✅ | ✅ | `curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -` |
 | Custom | ✅ | ✅ | Local file: `data.jsonl` |
 | Custom MM | ✅ | ✅ | Local file: `mm_data.jsonl` |
 
@@ -239,6 +240,69 @@ vllm bench serve \
     --spec-bench-category "summarization"
 ```
 
+#### SPEED-Bench Benchmark with Speculative Decoding
+
+[SPEED-Bench](https://huggingface.co/datasets/nvidia/SPEED-Bench) is a unified and diverse dataset for speculative decoding, supporting acceptance rate and length measurements using the Qualitative split and throughput measurements using the Throughput splits in 5 configuration of input sequence length (1k, 2k, 8k, 16k, 32k).
+
+!!! note
+    This dataset is governed by the [NVIDIA Evaluation Dataset License Agreement](https://huggingface.co/datasets/nvidia/SPEED-Bench/blob/main/License.pdf). For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose. The `prepare.py` script automatically fetches data from all the source datasets.
+
+First, download the dataset to a folder, using this one liner:
+
+```bash
+curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -
+```
+
+The command supports also the following arguments:
+
+- `--config`: download only a subset of the dataset: `qualitative`, `throughput_1k`, `throughput_2k`, `throughput_8k`, `throughput_16k` and `throughput_32k`. By default, it will download all subsets.
+- `--output_dir`: download to a specified folder. By default, it will download to the current directory.
+
+Start a server with speculative decoding:
+
+```bash
+vllm serve meta-llama/Llama-3.3-70B-Instruct \
+    --speculative-config $'{"method": "eagle3",
+    "num_speculative_tokens": 3,
+    "model": "nvidia/Llama-3.3-70B-Instruct-Eagle3"}'
+```
+
+Run all categories in the Qualitative split:
+
+```bash
+vllm bench serve \
+    --model meta-llama/Llama-3.3-70B-Instruct \
+    --dataset-name speed_bench \
+    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
+    --num-prompts -1
+```
+
+Available categories include `[writing, roleplay, reasoning, math, coding, stem, humanities, multilingual, summarization, qa, rag]`.
+
+Run only a specific category like "multilingual":
+
+```bash
+vllm bench serve \
+    --model meta-llama/Llama-3.3-70B-Instruct \
+    --dataset-name speed_bench \
+    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
+    --num-prompts -1
+    --speed-bench-category "multilingual"
+```
+
+Run all categories in the Throughput split (2k ISL):
+
+```bash
+vllm bench serve \
+    --model meta-llama/Llama-3.3-70B-Instruct \
+    --dataset-name speed_bench \
+    --speed-bench-dataset-subset throughput_2k
+    --dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench/" \
+    --num-prompts -1
+```
+
+Available categories include `[high_entropy, mixed, low_entropy]`, where high entropy data contains unstructued data such as creative writing while low entropy data contains more structured data such as coding, more details are in the dataset card.
+
 #### Other HuggingFaceDataset Examples
 
 ```bash

docs/features/quantization/README.md

@@ -16,6 +16,7 @@ The following are the supported quantization formats for vLLM:
 - [INT8 W8A8](int8.md)
 - [FP8 W8A8](fp8.md)
 - [NVIDIA Model Optimizer](modelopt.md)
+- [Online Quantization](online.md)
 - [AMD Quark](quark.md)
 - [Quantized KV Cache](quantized_kvcache.md)
 - [TorchAO](torchao.md)

docs/features/quantization/online.md

@@ -0,0 +1,94 @@
+# Online Quantization
+
+Online quantization lets you take a BF16/FP16 model and quantize its Linear
+and MoE weights to lower precision (such as FP8) at load time, without needing
+a pre-quantized checkpoint or calibration data. Weights are converted during
+model loading and activations are dynamically scaled during each forward pass.
+
+## Quick Start
+
+Pass a scheme name to the `quantization` parameter:
+
+```python
+from vllm import LLM
+
+# Per-tensor FP8 quantization (one scale per weight tensor)
+llm = LLM("meta-llama/Llama-3.1-8B", quantization="fp8_per_tensor")
+
+# Per-block FP8 quantization (128x128 block scaling for weights and 1x128 block scaling for activations)
+llm = LLM("meta-llama/Llama-3.1-8B", quantization="fp8_per_block")
+```
+
+Or with the CLI:
+
+```bash
+vllm serve meta-llama/Llama-3.1-8B --quantization fp8_per_tensor
+vllm serve meta-llama/Llama-3.1-8B --quantization fp8_per_block
+```
+
+## Supported Schemes
+
+| Scheme | Weight recipe | Activation recipe | Notes |
+| ------ | ------------- | ------------------ | ----- |
+| `fp8_per_tensor` | fp8_e4m3 data, fp32 per-tensor scale | fp8_e4m3 data, fp32 per-tensor scale | On some GPUs (Ada, Hopper) linear activations use per-token scaling for better performance |
+| `fp8_per_block` | fp8_e4m3 data, fp32 per-128x128-block scale | fp8_e4m3 data, fp32 per-1x128-block scale | |
+
+Support for additional schemes will be added in future versions of vllm.
+
+## Advanced Configuration
+
+For fine-grained control, use a `quantization_config` dictionary.
+
+### Separate Schemes for Dense and MoE Layers
+
+You can apply different quantization schemes to dense linear layers and MoE expert layers:
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    "ibm-granite/granite-3.0-1b-a400m-base",
+    quantization="fp8_per_tensor",
+    quantization_config={
+        "linear_scheme_override": "fp8_per_block",
+    },
+)
+```
+
+Or,
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    "ibm-granite/granite-3.0-1b-a400m-base",
+    quantization="fp8_per_tensor",
+    quantization_config={
+        "moe_scheme_override": "fp8_per_block",
+    },
+)
+```
+
+### Excluding Layers from Quantization
+
+Use the `ignore` parameter to skip specific layers. It accepts exact layer names and regex patterns (prefixed with `re:`):
+
+```python
+from vllm import LLM
+
+llm = LLM(
+    "ibm-granite/granite-3.0-1b-a400m-base",
+    quantization="fp8_per_tensor",
+    quantization_config={
+        "ignore": [
+            # exact layer name
+            "model.layers.1.self_attn.o_proj",
+            # regex: skip all QKV projections
+            "re:.*[qkv]_proj",
+        ],
+    },
+)
+```
+
+!!! note
+    For fused layers (e.g., `qkv_proj` which fuses `q_proj`, `k_proj`, `v_proj`), the ignore pattern must match the **unfused** shard names (`q_proj`, `k_proj`, `v_proj`), not the fused name.

docs/getting_started/installation/cpu.s390x.inc.md

@@ -3,15 +3,15 @@
 
 vLLM has experimental support for s390x architecture on IBM Z platform. For now, users must build from source to natively run on IBM Z platform.
 
-Currently, the CPU implementation for s390x architecture supports FP32 datatype only.
+Currently, the CPU implementation for s390x architecture supports FP32, BF16 and FP16.
 
 --8<-- [end:installation]
 --8<-- [start:requirements]
 
 - OS: `Linux`
-- SDK: `gcc/g++ >= 12.3.0` or later with Command Line Tools
+- 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: `pyarrow`, `torch` and `torchvision`
+- Build install python packages: `torchvision`, `llvmlite`, `numba`, `pyarrow (for testing)`, `opencv-headless`
 
 --8<-- [end:requirements]
 --8<-- [start:set-up-using-python]
@@ -24,13 +24,14 @@ Currently, there are no pre-built IBM Z CPU wheels.
 --8<-- [end:pre-built-wheels]
 --8<-- [start:build-wheel-from-source]
 
-Install the following packages from the package manager before building the vLLM. For example on RHEL 9.4:
+Install the following packages from the package manager before building the vLLM. For example on RHEL 9.6:
 
 ```bash
 dnf install -y \
-    which procps findutils tar vim git gcc g++ make patch make cython zlib-devel \
+    which procps findutils tar vim git 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 wget autoconf automake libtool cmake numactl-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.
@@ -43,13 +44,13 @@ 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`, `pyarrow` from source before building vLLM.
+    Please build the following dependencies, `torchvision`, `llvmlite`, `numba`, `llguidance`, `pyarrow`, `opencv-headless` from source before building vLLM.
 
 ```bash
-    sed -i '/^torch/d' requirements/build/cuda.txt    # remove torch from requirements/build/cuda.txt since we use nightly builds
     uv pip install -v \
+        --extra-index-url https://download.pytorch.org/whl/cpu \
         --torch-backend auto \
-        -r requirements/build/cuda.txt \
+        -r requirements/build/cpu.txt \
         -r requirements/cpu.txt \
     VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
         uv pip install dist/*.whl
@@ -57,10 +58,9 @@ Execute the following commands to build and install vLLM from source.
 
 ??? console "pip"
     ```bash
-        sed -i '/^torch/d' requirements/build/cuda.txt    # remove torch from requirements/build/cuda.txt since we use nightly builds
         pip install -v \
-            --extra-index-url https://download.pytorch.org/whl/nightly/cpu \
-            -r requirements/build/cuda.txt \
+            --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

docs/getting_started/installation/gpu.rocm.inc.md

@@ -240,7 +240,7 @@ uv pip install vllm==${VLLM_VERSION} \
         # Install dependencies
         pip install --upgrade numba \
             scipy \
-            huggingface-hub[cli,hf_transfer] \
+            huggingface-hub[cli] \
             setuptools_scm
         pip install -r requirements/rocm.txt
 

docs/models/pooling_models/README.md

@@ -59,6 +59,16 @@ please refer to [IO Processor Plugins](../../design/io_processor_plugins.md).
     Within classification tasks, there is a specialized subcategory: Cross-encoder (aka reranker) models. These models
 are a subset of classification models that accept two prompts as input and output num_labels equal to 1.
 
+### Pooling Types
+
+| Pooling Tasks  | Granularity   | Description                                                                                                                                                                                       |
+|----------------|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `CLS` pooling  | Sequence-wise | For BERT‑like (bidirectional self‑attention) models, CLS pooling is used by default. This means the last_hidden_states corresponding to the first token (the [CLS] token) is taken as the output. |
+| `LAST` pooling | Sequence-wise | For GPT‑like (causal self‑attention) models, LAST pooling is used by default. This means the last_hidden_states corresponding to the last token is taken as the output.                           |
+| `MEAN` pooling | Sequence-wise | Many studies have shown that averaging the last_hidden_states over all input tokens performs better on certain downstream tasks. Therefore, more and more models are using MEAN pooling.          |
+| `ALL` pooling  | Token-wise    | Outputs the last_hidden_states for all input tokens.                                                                                                                                              |
+| `STEP` pooling | Token-wise    | Filters and outputs the last_hidden_states corresponding to the token IDs returned by returned_token_ids.                                                                                         |
+
 ### Score Types
 
 The scoring models is designed to compute similarity scores between two input prompts. It supports three model types

docs/models/pooling_models/embed.md

@@ -45,6 +45,7 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 | `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ |
 | `GteModel` | Arctic-Embed-2.0-M | `Snowflake/snowflake-arctic-embed-m-v2.0`. | | |
 | `GteNewModel` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. | | |
+| `JinaEmbeddingsV5Model`<sup>C</sup> | Qwen3-based with task-specific LoRA adapters | `jinaai/jina-embeddings-v5-text-small` (see note) | ✅︎ | ✅︎ |
 | `LlamaBidirectionalModel`<sup>C</sup> | Llama-based with bidirectional attention | `nvidia/llama-nemotron-embed-1b-v2`, etc. | ✅︎ | ✅︎ |
 | `LlamaModel`<sup>C</sup>, `LlamaForCausalLM`<sup>C</sup>, `MistralModel`<sup>C</sup>, etc. | Llama-based | `intfloat/e5-mistral-7b-instruct`, etc. | ✅︎ | ✅︎ |
 | `ModernBertModel` | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. | | |
@@ -73,6 +74,12 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 !!! note
     `jinaai/jina-embeddings-v3` supports multiple tasks through LoRA, while vllm temporarily only supports text-matching tasks by merging LoRA weights.
 
+!!! note
+    `jinaai/jina-embeddings-v5-text-small` ships with four task-specific LoRA adapters
+    (`retrieval`, `text-matching`, `classification`, `clustering`). vLLM merges the
+    selected adapter into the base weights at load time. Choose the task with
+    `--hf-overrides '{"jina_task": "<task>"}'`; the default is `retrieval`.
+
 ### Multimodal Models
 
 !!! note

docs/models/pooling_models/scoring.md

@@ -160,6 +160,8 @@ The following Score API parameters are supported:
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-extra-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:classify-extra-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:scoring-common-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:score-request-params"
 ```
 
 #### Examples
@@ -370,6 +372,8 @@ The following rerank api parameters are supported:
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:pooling-common-extra-params"
 --8<-- "vllm/entrypoints/pooling/base/protocol.py:classify-extra-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:scoring-common-params"
+--8<-- "vllm/entrypoints/pooling/scoring/protocol.py:rerank-request-params"
 ```
 
 #### Examples

docs/models/pooling_models/token_classify.md

@@ -68,7 +68,7 @@ If your model is not in the above list, we will try to automatically convert the
     Forced alignment usage requires `--hf-overrides '{"architectures": ["Qwen3ASRForcedAlignerForTokenClassification"]}'`.
     Please refer to [examples/pooling/token_classify/forced_alignment_offline.py](../../../examples/pooling/token_classify/forced_alignment_offline.py).
 
-### As Reward Models
+### Reward Models
 
 Using token classification models as reward models. For details on reward models, see [Reward Models](reward.md).
 

docs/serving/openai_compatible_server.md

@@ -467,28 +467,11 @@ It consists of two endpoints:
 - `/tokenize` corresponds to calling `tokenizer.encode()`.
 - `/detokenize` corresponds to calling `tokenizer.decode()`.
 
-### Score API
-
-#### Score Template
-
-Some scoring models require a specific prompt format to work correctly. You can specify a custom score template using the `--chat-template` parameter (see [Chat Template](#chat-template)).
-
-Score templates are supported for **cross-encoder** models only. If you are using an **embedding** model for scoring, vLLM does not apply a score template.
-
-Like chat templates, the score template receives a `messages` list. For scoring, each message has a `role` attribute—either `"query"` or `"document"`. For the usual kind of point-wise cross-encoder, you can expect exactly two messages: one query and one document. To access the query and document content, use Jinja's `selectattr` filter:
-
-- **Query**: `{{ (messages | selectattr("role", "eq", "query") | first).content }}`
-- **Document**: `{{ (messages | selectattr("role", "eq", "document") | first).content }}`
-
-This approach is more robust than index-based access (`messages[0]`, `messages[1]`) because it selects messages by their semantic role. It also avoids assumptions about message ordering if additional message types are added to `messages` in the future.
-
-Example template file: [examples/pooling/score/template/nemotron-rerank.jinja](../../examples/pooling/score/template/nemotron-rerank.jinja)
-
 ### Generative Scoring API
 
 The `/generative_scoring` endpoint uses a CausalLM model (e.g., Llama, Qwen, Mistral) to compute the probability of specified token IDs appearing as the next token. Each item (document) is concatenated with the query to form a prompt, and the model predicts how likely each label token is as the next token after that prompt. This lets you score items against a query — for example, asking "Is this the capital of France?" and scoring each city by how likely the model is to answer "Yes".
 
-This endpoint is automatically available when the server is started with a generative model (task `"generate"`). It is separate from the pooling-based [Score API](#score-api), which uses cross-encoder, bi-encoder, or late-interaction models.
+This endpoint is automatically available when the server is started with a generative model (task `"generate"`). It is separate from the pooling-based [Score API](../models/pooling_models/scoring.md#score-api), which uses cross-encoder, bi-encoder, or late-interaction models.
 
 **Requirements:**
 

requirements/common.txt

@@ -7,7 +7,7 @@ requests >= 2.26.0
 tqdm
 blake3
 py-cpuinfo
-transformers >= 4.56.0, < 5
+transformers >= 4.56.0, != 5.0.*, != 5.1.*, != 5.2.*, != 5.3.*, != 5.4.*, != 5.5.0
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.* # Required by LlamaTokenizer, gRPC. CVE-2026-0994
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
@@ -19,7 +19,7 @@ pillow  # Required for image processing
 prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer == 0.11.3
-llguidance >= 1.3.0, < 1.4.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64" or platform_machine == "s390x" or platform_machine == "ppc64le"
+llguidance >= 1.3.0, < 1.4.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64" or platform_machine == "ppc64le"
 outlines_core == 0.2.11
 # required for outlines backend disk cache
 diskcache == 5.6.3
@@ -32,12 +32,14 @@ pyzmq >= 25.0.0
 msgspec
 gguf >= 0.17.0
 mistral_common[image] >= 1.11.0
+av  # required for audio in video IO
 opencv-python-headless >= 4.13.0    # required for video IO
+soundfile  # required for audio IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
 setuptools>=77.0.3,<81.0.0; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12
 einops # Required for Qwen2-VL.
-compressed-tensors == 0.14.0.1 # required for compressed-tensors
+compressed-tensors == 0.15.0.1 # required for compressed-tensors
 depyf==0.20.0 # required for profiling and debugging with compilation config
 cloudpickle # allows pickling lambda functions in model_executor/models/registry.py
 watchfiles # required for http server to monitor the updates of TLS files

requirements/kv_connectors.txt

@@ -1,5 +1,5 @@
 lmcache >= 0.3.9
-nixl[cu13] >= 0.7.1, < 0.10.0 # Required for disaggregated prefill
-nixl-cu12 >= 0.7.1, < 0.10.0
-nixl-cu13 >= 0.7.1, < 0.10.0
+nixl[cu13] >= 0.7.1, <= 0.10.1 # Required for disaggregated prefill
+nixl-cu12 >= 0.7.1, <= 0.10.1
+nixl-cu13 >= 0.7.1, <= 0.10.1
 mooncake-transfer-engine >= 0.3.8

requirements/test/cuda.in

@@ -18,10 +18,9 @@ httpx
 librosa # required for audio tests
 vector_quantize_pytorch # required for minicpmo_26 test
 vocos # required for minicpmo_26 test
-peft>=0.15.0 # required for phi-4-mm test
+peft>=0.18.1 # required for phi-4-mm test
 pqdm
 ray[cgraph,default]>=2.48.0 # Ray Compiled Graph, required by pipeline parallelism tests
-resampy # required for audio tests
 sentence-transformers>=5.2.0 # required for embedding tests
 soundfile # required for audio tests
 jiwer # required for audio tests
@@ -39,8 +38,8 @@ opencv-python-headless >= 4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.11 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==4.57.5
-tokenizers==0.22.0
+transformers==5.5.3
+tokenizers==0.22.2
 schemathesis>=3.39.15 # Required for openai schema test.
 # quantization
 bitsandbytes==0.49.2

requirements/test/cuda.txt

@@ -4,7 +4,7 @@ absl-py==2.1.0
     # via
     #   rouge-score
     #   tensorboard
-accelerate==1.0.1
+accelerate==1.13.0
     # via peft
 aenum==3.1.16
     # via lightly
@@ -248,7 +248,6 @@ filelock==3.16.1
     #   huggingface-hub
     #   ray
     #   torch
-    #   transformers
     #   virtualenv
 fiona==1.10.1
     # via torchgeo
@@ -331,7 +330,7 @@ h5py==3.13.0
     # via terratorch
 harfile==0.3.0
     # via schemathesis
-hf-xet==1.1.7
+hf-xet==1.4.3
     # via huggingface-hub
 hiredis==3.0.0
     # via tensorizer
@@ -345,9 +344,10 @@ httpx==0.27.2
     # via
     #   -r requirements/test/cuda.in
     #   diffusers
+    #   huggingface-hub
     #   perceptron
     #   schemathesis
-huggingface-hub==0.36.2
+huggingface-hub==1.10.2
     # via
     #   accelerate
     #   datasets
@@ -555,7 +555,6 @@ numba==0.61.2
     #   -c requirements/cuda.txt
     #   -r requirements/test/cuda.in
     #   librosa
-    #   resampy
 numpy==2.2.6
     # via
     #   -r requirements/test/cuda.in
@@ -596,7 +595,6 @@ numpy==2.2.6
     #   pyogrio
     #   pywavelets
     #   rasterio
-    #   resampy
     #   rioxarray
     #   rouge-score
     #   runai-model-streamer
@@ -756,7 +754,7 @@ pathvalidate==3.2.1
     # via pytablewriter
 patsy==1.0.1
     # via statsmodels
-peft==0.16.0
+peft==0.18.1
     # via -r requirements/test/cuda.in
 perceptron==0.1.4
     # via -r requirements/test/cuda.in
@@ -982,7 +980,7 @@ referencing==0.35.1
     # via
     #   jsonschema
     #   jsonschema-specifications
-regex==2024.9.11
+regex==2026.2.28
     # via
     #   diffusers
     #   nltk
@@ -1002,7 +1000,6 @@ requests==2.32.3
     #   google-api-core
     #   google-cloud-storage
     #   gpt-oss
-    #   huggingface-hub
     #   lightly
     #   lm-eval
     #   mistral-common
@@ -1015,10 +1012,7 @@ requests==2.32.3
     #   starlette-testclient
     #   tacoreader
     #   tiktoken
-    #   transformers
     #   wandb
-resampy==0.4.3
-    # via -r requirements/test/cuda.in
 responses==0.25.3
     # via genai-perf
 rfc3339-validator==0.1.4
@@ -1216,7 +1210,7 @@ timm==1.0.17
     #   segmentation-models-pytorch
     #   terratorch
     #   torchgeo
-tokenizers==0.22.0
+tokenizers==0.22.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
@@ -1295,7 +1289,7 @@ tqdm==4.67.3
     #   tacoreader
     #   terratorch
     #   transformers
-transformers==4.57.5
+transformers==5.5.3
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/cuda.in
@@ -1317,7 +1311,9 @@ typepy==1.3.2
 typer==0.15.2
     # via
     #   fastsafetensors
+    #   huggingface-hub
     #   perceptron
+    #   transformers
 types-python-dateutil==2.9.0.20241206
     # via arrow
 typeshed-client==2.8.2

requirements/test/nightly-torch.txt

@@ -29,8 +29,8 @@ opencv-python-headless >= 4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.11 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==4.57.5
-tokenizers==0.22.0
+transformers==5.5.3
+tokenizers==0.22.2
 schemathesis>=3.39.15 # Required for openai schema test.
 # quantization
 bitsandbytes>=0.49.2

requirements/test/rocm.in

@@ -23,7 +23,6 @@ vocos # required for minicpmo_26 test
 peft>=0.15.0 # required for phi-4-mm test
 pqdm
 ray[cgraph,default]>=2.48.0 # Ray Compiled Graph, required by pipeline parallelism tests
-resampy # required for audio tests
 sentence-transformers>=5.2.0 # required for embedding tests
 soundfile # required for audio tests
 jiwer # required for audio tests
@@ -38,8 +37,8 @@ opencv-python-headless>=4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.11 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==4.57.5
-tokenizers==0.22.0
+transformers==5.5.3
+tokenizers==0.22.2
 schemathesis>=3.39.15 # Required for openai schema test
 # quantization
 bitsandbytes==0.49.2
@@ -82,4 +81,3 @@ plotly # required for perf comparison html report
 rapidfuzz
 torchgeo==0.7.0
 multiprocess==0.70.16
-huggingface-hub==0.36.2

requirements/test/rocm.txt

@@ -39,7 +39,7 @@ annotated-doc==0.0.4
     #   typer
 annotated-types==0.7.0
     # via pydantic
-anthropic==0.89.0
+anthropic==0.93.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -76,7 +76,9 @@ attrs==26.1.0
 audioread==3.0.1
     # via librosa
 av==16.1.0
-    # via -r requirements/test/rocm.in
+    # via
+    #   -r requirements/test/../common.txt
+    #   -r requirements/test/rocm.in
 azure-core==1.39.0
     # via
     #   azure-identity
@@ -172,7 +174,7 @@ colorful==0.5.8
     # via ray
 colorlog==6.10.1
     # via optuna
-compressed-tensors==0.14.0.1
+compressed-tensors==0.15.0.1
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -269,9 +271,9 @@ fastapi==0.135.2
     #   model-hosting-container-standards
 fastapi-cli==0.0.24
     # via fastapi
-fastapi-cloud-cli==0.15.1
+fastapi-cloud-cli==0.16.1
     # via fastapi-cli
-fastar==0.9.0
+fastar==0.10.0
     # via fastapi-cloud-cli
 fastparquet==2026.3.0
     # via genai-perf
@@ -290,7 +292,6 @@ filelock==3.25.2
     #   python-discovery
     #   ray
     #   torch
-    #   transformers
     #   virtualenv
 fiona==1.10.1
     # via torchgeo
@@ -384,7 +385,7 @@ h5py==3.16.0
     # via terratorch
 harfile==0.4.0
     # via schemathesis
-hf-xet==1.4.2
+hf-xet==1.4.3
     # via huggingface-hub
 hiredis==3.3.1
     # via tensorizer
@@ -403,6 +404,7 @@ httpx==0.27.2
     #   diffusers
     #   fastapi
     #   fastapi-cloud-cli
+    #   huggingface-hub
     #   mcp
     #   model-hosting-container-standards
     #   openai
@@ -410,9 +412,8 @@ httpx==0.27.2
     #   schemathesis
 httpx-sse==0.4.3
     # via mcp
-huggingface-hub==0.36.2
+huggingface-hub==1.10.2
     # via
-    #   -r requirements/test/rocm.in
     #   accelerate
     #   datasets
     #   diffusers
@@ -484,7 +485,7 @@ jinja2==3.1.6
     #   genai-perf
     #   lm-eval
     #   torch
-jiter==0.13.0
+jiter==0.14.0
     # via
     #   anthropic
     #   openai
@@ -631,7 +632,7 @@ msgpack==1.1.2
     # via
     #   librosa
     #   ray
-msgspec==0.20.0
+msgspec==0.21.0
     # via -r requirements/test/../common.txt
 mteb==2.11.5
     # via -r requirements/test/rocm.in
@@ -663,7 +664,6 @@ numba==0.61.2
     #   -c requirements/rocm.txt
     #   -r requirements/test/rocm.in
     #   librosa
-    #   resampy
 numkong==7.1.1
     # via albucore
 numpy==2.2.6
@@ -709,7 +709,6 @@ numpy==2.2.6
     #   pytrec-eval-terrier
     #   pywavelets
     #   rasterio
-    #   resampy
     #   rioxarray
     #   rouge-score
     #   runai-model-streamer
@@ -742,7 +741,7 @@ omegaconf==2.3.0
     #   lightning
 open-clip-torch==2.32.0
     # via -r requirements/test/rocm.in
-openai==2.30.0
+openai==2.31.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1093,7 +1092,7 @@ python-dotenv==1.2.2
     #   uvicorn
 python-json-logger==4.1.0
     # via -r requirements/test/../common.txt
-python-multipart==0.0.22
+python-multipart==0.0.26
     # via
     #   fastapi
     #   mcp
@@ -1180,7 +1179,6 @@ requests==2.32.5
     #   google-api-core
     #   google-cloud-storage
     #   gpt-oss
-    #   huggingface-hub
     #   lightly
     #   lm-eval
     #   mistral-common
@@ -1194,10 +1192,7 @@ requests==2.32.5
     #   starlette-testclient
     #   tacoreader
     #   tiktoken
-    #   transformers
     #   wandb
-resampy==0.4.3
-    # via -r requirements/test/rocm.in
 responses==0.26.0
     # via genai-perf
 rfc3339-validator==0.1.4
@@ -1338,6 +1333,7 @@ sortedcontainers==2.4.0
     # via hypothesis
 soundfile==0.13.1
     # via
+    #   -r requirements/test/../common.txt
     #   -r requirements/test/rocm.in
     #   genai-perf
     #   librosa
@@ -1428,7 +1424,7 @@ timm==1.0.17
     #   segmentation-models-pytorch
     #   terratorch
     #   torchgeo
-tokenizers==0.22.0
+tokenizers==0.22.2
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1471,7 +1467,7 @@ tqdm==4.67.3
     #   tacoreader
     #   terratorch
     #   transformers
-transformers==4.57.5
+transformers==5.5.3
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1498,7 +1494,9 @@ typer==0.24.1
     #   fastapi-cli
     #   fastapi-cloud-cli
     #   fastsafetensors
+    #   huggingface-hub
     #   perceptron
+    #   transformers
 typeshed-client==2.9.0
     # via jsonargparse
 typing-extensions==4.15.0

requirements/test/xpu.in

@@ -13,7 +13,6 @@ pytest-shard
 absl-py
 accelerate
 arctic-inference
-hf_transfer
 lm_eval[api]
 modelscope
 

requirements/test/xpu.txt

@@ -19,7 +19,9 @@ aiosignal==1.4.0
 albumentations==1.4.6
     # via -r requirements/test/xpu.in
 annotated-doc==0.0.4
-    # via fastapi
+    # via
+    #   fastapi
+    #   typer
 annotated-types==0.7.0
     # via pydantic
 anyio==4.13.0
@@ -64,6 +66,7 @@ click==8.3.1
     #   jiwer
     #   nltk
     #   schemathesis
+    #   typer
     #   uvicorn
 colorama==0.4.6
     # via sacrebleu
@@ -112,7 +115,6 @@ filelock==3.25.2
     #   huggingface-hub
     #   modelscope
     #   torch
-    #   transformers
 frozenlist==1.8.0
     # via
     #   aiohttp
@@ -133,9 +135,7 @@ h11==0.16.0
     #   uvicorn
 harfile==0.4.0
     # via schemathesis
-hf-transfer==0.1.9
-    # via -r requirements/test/xpu.in
-hf-xet==1.4.2
+hf-xet==1.4.3
     # via huggingface-hub
 html2text==2025.4.15
     # via gpt-oss
@@ -144,8 +144,9 @@ httpcore==1.0.9
 httpx==0.28.1
     # via
     #   datasets
+    #   huggingface-hub
     #   schemathesis
-huggingface-hub==0.36.2
+huggingface-hub==1.10.2
     # via
     #   accelerate
     #   datasets
@@ -515,7 +516,6 @@ requests==2.33.1
     #   docker
     #   evaluate
     #   gpt-oss
-    #   huggingface-hub
     #   lm-eval
     #   mistral-common
     #   modelscope
@@ -524,11 +524,11 @@ requests==2.33.1
     #   schemathesis
     #   starlette-testclient
     #   tiktoken
-    #   transformers
 rich==14.3.3
     # via
     #   mteb
     #   schemathesis
+    #   typer
 rouge-score==0.1.2
     # via lm-eval
 rpds-py==0.30.0
@@ -572,6 +572,8 @@ setuptools==80.10.2
     #   modelscope
     #   pytablewriter
     #   torch
+shellingham==1.5.4
+    # via typer
 six==1.17.0
     # via
     #   -c requirements/common.txt
@@ -665,7 +667,7 @@ tqdm==4.67.3
     #   pqdm
     #   sentence-transformers
     #   transformers
-transformers==4.57.6
+transformers==5.5.3
     # via
     #   -c requirements/common.txt
     #   sentence-transformers
@@ -676,6 +678,10 @@ typepy==1.3.4
     #   dataproperty
     #   pytablewriter
     #   tabledata
+typer==0.24.1
+    # via
+    #   huggingface-hub
+    #   transformers
 typing-extensions==4.15.0
     # via
     #   -c requirements/common.txt

setup.py

@@ -693,6 +693,12 @@ class precompiled_wheel_utils:
                 flash_attn_regex = re.compile(
                     r"vllm/vllm_flash_attn/(?:[^/.][^/]*/)*(?!\.)[^/]*\.py"
                 )
+                # __init__.py and flash_attn_interface.py are source-controlled
+                # in vllm and should not be overwritten (matches cmake exclusions)
+                flash_attn_files_to_skip = {
+                    "vllm/vllm_flash_attn/__init__.py",
+                    "vllm/vllm_flash_attn/flash_attn_interface.py",
+                }
                 triton_kernels_regex = re.compile(
                     r"vllm/third_party/triton_kernels/(?:[^/.][^/]*/)*(?!\.)[^/]*\.py"
                 )
@@ -705,7 +711,11 @@ class precompiled_wheel_utils:
                     filter(lambda x: x.filename in files_to_copy, wheel.filelist)
                 )
                 file_members += list(
-                    filter(lambda x: flash_attn_regex.match(x.filename), wheel.filelist)
+                    filter(
+                        lambda x: flash_attn_regex.match(x.filename)
+                        and x.filename not in flash_attn_files_to_skip,
+                        wheel.filelist,
+                    )
                 )
                 file_members += list(
                     filter(
@@ -1082,10 +1092,7 @@ setup(
         "instanttensor": ["instanttensor >= 0.1.5"],
         "runai": ["runai-model-streamer[s3,gcs,azure] >= 0.15.7"],
         "audio": [
-            "av",
-            "resampy",
             "scipy",
-            "soundfile",
             "mistral_common[audio]",
         ],  # Required for audio processing
         "video": [],  # Kept for backwards compatibility
@@ -1094,7 +1101,7 @@ setup(
         # NOTE: When updating helion version, also update CI files:
         #   - .buildkite/test_areas/kernels.yaml
         #   - .buildkite/test-amd.yaml
-        "helion": ["helion==0.3.3"],
+        "helion": ["helion==1.0.0"],
         # Optional deps for gRPC server (vllm serve --grpc)
         "grpc": ["smg-grpc-servicer[vllm] >= 0.5.0"],
         # Optional deps for OpenTelemetry tracing

tests/config/base_model_arch_groundtruth.json

@@ -356,6 +356,23 @@
         "is_multimodal_model": false,
         "dtype": "torch.float32"
     },
+    "stepfun-ai/Step-3.5-Flash": {
+        "architectures": [
+            "Step3p5ForCausalLM"
+        ],
+        "model_type": "step3p5",
+        "text_model_type": "step3p5",
+        "hidden_size": 4096,
+        "total_num_hidden_layers": 45,
+        "total_num_attention_heads": 64,
+        "head_size": 128,
+        "vocab_size": 128896,
+        "total_num_kv_heads": 8,
+        "num_experts": 288,
+        "is_deepseek_mla": false,
+        "is_multimodal_model": false,
+        "dtype": "torch.bfloat16"
+    },
     "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16": {
         "architectures": [
             "NemotronHForCausalLM"

tests/config/test_model_arch_config.py

@@ -16,6 +16,7 @@ BASE_TRUST_REMOTE_CODE_MODELS = {
     "nvidia/Llama-3_3-Nemotron-Super-49B-v1",
     "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
     "XiaomiMiMo/MiMo-7B-RL",
+    "stepfun-ai/Step-3.5-Flash",
     # Excluded: Not available online right now
     # "FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1",
     "meituan-longcat/LongCat-Flash-Chat",

tests/conftest.py

@@ -364,6 +364,7 @@ class HfRunner:
         model_name: str,
         dtype: str = "auto",
         *,
+        revision: str | None = None,
         model_kwargs: dict[str, Any] | None = None,
         trust_remote_code: bool = True,
         is_sentence_transformer: bool = False,
@@ -383,6 +384,7 @@ class HfRunner:
             self._init(
                 model_name=model_name,
                 dtype=dtype,
+                revision=revision,
                 model_kwargs=model_kwargs,
                 trust_remote_code=trust_remote_code,
                 is_sentence_transformer=is_sentence_transformer,
@@ -396,6 +398,7 @@ class HfRunner:
         model_name: str,
         dtype: str = "auto",
         *,
+        revision: str | None = None,
         model_kwargs: dict[str, Any] | None = None,
         trust_remote_code: bool = True,
         is_sentence_transformer: bool = False,
@@ -410,6 +413,15 @@ class HfRunner:
             model_name,
             trust_remote_code=trust_remote_code,
         )
+        # HF runner should use the HF config so that it's consistent with the HF model
+        if self.config.__module__.startswith("vllm.transformers_utils.configs"):
+            from transformers.models.auto.configuration_auto import CONFIG_MAPPING
+
+            del CONFIG_MAPPING._extra_content[self.config.model_type]
+            self.config = AutoConfig.from_pretrained(
+                model_name,
+                trust_remote_code=trust_remote_code,
+            )
         self.device = self.get_default_device()
         self.dtype = dtype = _get_and_verify_dtype(
             self.model_name,
@@ -428,6 +440,7 @@ class HfRunner:
 
             self.model = SentenceTransformer(
                 model_name,
+                revision=revision,
                 device=self.device,
                 model_kwargs=model_kwargs,
                 trust_remote_code=trust_remote_code,
@@ -438,6 +451,7 @@ class HfRunner:
 
             self.model = CrossEncoder(
                 model_name,
+                revision=revision,
                 device=self.device,
                 automodel_args=model_kwargs,
                 trust_remote_code=trust_remote_code,
@@ -447,6 +461,7 @@ class HfRunner:
                 nn.Module,
                 auto_cls.from_pretrained(
                     model_name,
+                    revision=revision,
                     trust_remote_code=trust_remote_code,
                     **model_kwargs,
                 ),

tests/entrypoints/llm/test_generate.py

@@ -91,6 +91,12 @@ def test_multiple_priority(llm: LLM):
         outputs = llm.generate(PROMPTS, sampling_params=None, priority=[])
 
 
+def test_single_prompt_priority(llm: LLM):
+    # Single string prompts should be normalized to one request.
+    outputs = llm.generate(PROMPTS[0], sampling_params=None, priority=[0])
+    assert len(outputs) == 1
+
+
 def test_max_model_len():
     max_model_len = 20
     llm = LLM(

tests/kernels/core/test_cpu_activation.py

@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from tests.kernels.allclose_default import get_default_atol, get_default_rtol
+from tests.kernels.utils import opcheck
+from vllm.platforms import CpuArchEnum, current_platform
+from vllm.utils.torch_utils import set_random_seed
+
+if not current_platform.is_cpu():
+    pytest.skip("skipping CPU-only tests", allow_module_level=True)
+
+from vllm.model_executor.layers.activation import (
+    GELU,
+    FastGELU,
+    GeluAndMul,
+    NewGELU,
+    QuickGELU,
+    SiluAndMul,
+)
+
+DTYPES = [torch.bfloat16, torch.float32]
+NUM_TOKENS = [7, 83]
+D = [512, 2048]
+SEEDS = [0]
+
+
+@pytest.mark.parametrize(
+    ("activation_cls", "fn"),
+    [
+        (SiluAndMul, torch.ops._C.silu_and_mul),
+        (GeluAndMul, torch.ops._C.gelu_and_mul),
+        (GeluAndMul, torch.ops._C.gelu_tanh_and_mul),
+    ],
+)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@torch.inference_mode()
+def test_cpu_act_and_mul(
+    default_vllm_config,
+    activation_cls: type[torch.nn.Module],
+    fn: object,
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    set_random_seed(seed)
+    x = torch.randn(num_tokens, 2 * d, dtype=dtype)
+
+    layer = activation_cls()
+    out = layer(x)
+    ref_out = layer.forward_native(x)
+
+    torch.testing.assert_close(
+        out, ref_out, atol=get_default_atol(out), rtol=get_default_rtol(out)
+    )
+
+    output_shape = x.shape[:-1] + (x.shape[-1] // 2,)
+    raw_out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+    opcheck(fn, (raw_out, x))
+
+
+@pytest.mark.parametrize(
+    ("activation_cls", "fn", "op_args"),
+    [
+        (NewGELU, torch.ops._C.gelu_new, ()),
+        (FastGELU, torch.ops._C.gelu_fast, ()),
+        (QuickGELU, torch.ops._C.gelu_quick, ()),
+        pytest.param(
+            GELU,
+            getattr(torch.ops._C, "activation_lut_bf16", None),
+            ("gelu",),
+            marks=pytest.mark.skipif(
+                current_platform.get_cpu_architecture() != CpuArchEnum.ARM,
+                reason="activation_lut_bf16 is only built on Arm CPU",
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@torch.inference_mode()
+def test_cpu_unary_activation(
+    default_vllm_config,
+    activation_cls: type[torch.nn.Module],
+    fn: object,
+    op_args: tuple[str, ...],
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    set_random_seed(seed)
+    x = torch.randn(num_tokens, d, dtype=dtype)
+    layer = activation_cls()
+    out = layer(x)
+    ref_out = layer.forward_native(x)
+    torch.testing.assert_close(
+        out, ref_out, atol=get_default_atol(out), rtol=get_default_rtol(out)
+    )
+    # gelu with activation_lut_bf16 only makes sense for BF16
+    if not (activation_cls is GELU and dtype != torch.bfloat16):
+        raw_out = torch.empty_like(x)
+        opcheck(fn, (raw_out, x, *op_args))

tests/kernels/helion/test_register.py

@@ -36,9 +36,11 @@ from vllm.kernels.helion.register import (
 )
 
 if _HOP_AVAILABLE:
+    from helion._compat import supports_torch_compile_fusion
     from helion._compiler._dynamo.higher_order_ops import (
         helion_kernel_wrapper_mutation,
     )
+    from torch._inductor.utils import run_and_get_code
 
 
 def _add_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
@@ -1003,3 +1005,49 @@ class TestTorchCompileHOP:
             "Compiled execution result doesn't match eager execution. "
             f"Max difference: {torch.max(torch.abs(compiled_result - eager_result))}"
         )
+
+    @pytest.mark.skipif(
+        not (_HOP_AVAILABLE and supports_torch_compile_fusion()),
+        reason="Requires PyTorch with Helion inductor fusion support",
+    )
+    def test_inductor_backend_compiles_helion_hop(self):
+        """Test torch.compile with inductor backend and Helion fusion enabled."""
+
+        configs = {"default": helion.Config(block_sizes=[4, 4])}
+
+        with dummy_kernel_registry(configs=configs) as register:
+            add_helion_kernel = register(
+                op_name="test_inductor_add_kernel",
+                config_picker=lambda args, keys: "default",
+                helion_settings=helion.Settings(
+                    torch_compile_fusion=True, static_shapes=False
+                ),
+            )(_add_kernel)
+
+        def f(x, y):
+            x = x * 2.0
+            y = y + 1.0
+            out = add_helion_kernel(x, y)
+            return out.relu()
+
+        torch._dynamo.reset()
+        compiled_f = torch.compile(f, backend="inductor", fullgraph=True)
+
+        x = torch.randn(4, 4, device="cuda")
+        y = torch.randn(4, 4, device="cuda")
+
+        compiled_result, source_codes = run_and_get_code(compiled_f, x, y)
+        eager_result = f(x, y)
+
+        assert torch.allclose(compiled_result, eager_result, atol=1e-5, rtol=1e-5), (
+            "Inductor-compiled result doesn't match eager execution. "
+            f"Max difference: {torch.max(torch.abs(compiled_result - eager_result))}"
+        )
+
+        # With fusion enabled, prologue/epilogue ops should be fused into
+        # a single triton kernel rather than generating separate kernels.
+        kernel_count = sum(code.count("@triton.jit") for code in source_codes)
+        assert kernel_count == 1, (
+            f"Expected 1 fused triton kernel, got {kernel_count}. "
+            "Prologue/epilogue ops were not fused into the Helion kernel."
+        )

tests/lora/conftest.py

@@ -3,6 +3,7 @@
 
 import tempfile
 from collections import OrderedDict
+from importlib import reload
 from unittest.mock import MagicMock
 
 import pytest
@@ -47,6 +48,11 @@ def cleanup_fixture(should_do_global_cleanup_after_test: bool):
 def maybe_enable_lora_dual_stream(monkeypatch: pytest.MonkeyPatch):
     if current_platform.is_cuda():
         monkeypatch.setenv("VLLM_LORA_ENABLE_DUAL_STREAM", "1")
+        import vllm.lora.layers.base_linear
+
+        if not hasattr(vllm.lora.layers.base_linear, "lora_linear_async"):
+            # Reload the module to ensure the environment variable takes effect.
+            reload(vllm.lora.layers.base_linear)
     yield
 
 

tests/lora/test_minicpmv_tp.py

@@ -1,7 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from importlib.metadata import version
+
 import pytest
+from packaging.version import Version
 
 import vllm
 from vllm.assets.image import ImageAsset
@@ -10,6 +13,14 @@ from vllm.platforms import current_platform
 
 from ..utils import multi_gpu_test
 
+pytestmark = pytest.mark.skipif(
+    Version("5.0") <= Version(version("transformers")),
+    reason=(
+        "MiniCPMV custom processor uses tokenizer.im_start_id which is not "
+        "available on TokenizersBackend in transformers v5.0+"
+    ),
+)
+
 MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
 
 PROMPT_TEMPLATE = (

tests/model_executor/model_loader/test_reload.py

@@ -164,6 +164,34 @@ def test_reload_weights(base_model, mul_model, add_model, tp_size, vllm_runner):
         assert add_perp < mul_perp
 
 
+def test_kv_scale_reload(vllm_runner):
+    """Test reloading a checkpoint that contains k_scale/v_scale weights."""
+    if not current_platform.supports_fp8():
+        pytest.skip(reason="Requires FP8 support")
+
+    model = "nm-testing/Llama-3.2-1B-Instruct-FP8-KV"
+
+    # Load dummy weights, then reload real checkpoint
+    with vllm_runner(
+        model_name=model,
+        load_format="dummy",
+        enable_prefix_caching=False,
+        max_model_len=16,
+        max_num_seqs=1,
+    ) as llm:
+        llm.collective_rpc(
+            "update_config",
+            kwargs={"overrides": {"load_config": {"load_format": "auto"}}},
+        )
+        llm.collective_rpc("reload_weights", kwargs={"weights_path": model})
+        reloaded_perp = llm.generate_prompt_perplexity(
+            ["The capital of France is the city of Paris"],
+            mask=["The capital of France is"],
+        )[0]
+
+    assert reloaded_perp < 10
+
+
 @pytest.mark.parametrize(
     "tp_size", [pytest.param(1), pytest.param(2, marks=[pytest.mark.slow_test])]
 )

tests/model_executor/test_keye_mrope.py

@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, field
+
+import pytest
+import torch
+
+from vllm.model_executor.models.keye import KeyeForConditionalGeneration
+from vllm.multimodal.inputs import (
+    MultiModalFeatureSpec,
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    PlaceholderRange,
+)
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+@pytest.fixture(autouse=True, scope="module")
+def _force_cpu_default_device():
+    original = torch.get_default_device()
+    torch.set_default_device("cpu")
+    yield
+    torch.set_default_device(original)
+
+
+@dataclass
+class DummyVisionConfig:
+    spatial_merge_size: int = 2
+
+
+@dataclass
+class DummyConfig:
+    vision_config: DummyVisionConfig = field(default_factory=DummyVisionConfig)
+
+
+def make_model(config: DummyConfig) -> KeyeForConditionalGeneration:
+    model = object.__new__(KeyeForConditionalGeneration)
+    model.config = config
+    return model
+
+
+def make_mm_feature(
+    *,
+    modality: str,
+    offset: int,
+    length: int,
+    grid_thw: tuple[int, int, int] | list[tuple[int, int, int]],
+) -> MultiModalFeatureSpec:
+    field_name = "image_grid_thw" if modality == "image" else "video_grid_thw"
+    return MultiModalFeatureSpec(
+        data=MultiModalKwargsItem(
+            {
+                field_name: MultiModalFieldElem(
+                    data=torch.tensor(grid_thw),
+                    field=None,  # HACK.
+                ),
+            }
+        ),
+        modality=modality,
+        identifier="DUMMY",
+        mm_position=PlaceholderRange(offset=offset, length=length),
+    )
+
+
+def test_get_mrope_input_positions_text_only():
+    model = make_model(DummyConfig())
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[11, 12, 13, 14, 15],
+        mm_features=[],
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == 0
+
+
+def test_get_mrope_input_positions_single_image():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="image",
+            offset=1,
+            length=4,
+            grid_thw=(1, 4, 4),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 20, 21, 22, 23, 30, 31],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4],
+            [0, 1, 1, 2, 2, 3, 4],
+            [0, 1, 2, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == -2
+
+
+def test_get_mrope_input_positions_interleaved_image_and_video():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="image",
+            offset=1,
+            length=4,
+            grid_thw=(1, 4, 4),
+        ),
+        make_mm_feature(
+            modality="video",
+            offset=7,
+            length=4,
+            grid_thw=[(2, 4, 2)],
+        ),
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 20, 21, 22, 23, 30, 31, 40, 41, 42, 43, 50, 51],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4, 5, 5, 7, 7, 9, 10],
+            [0, 1, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            [0, 1, 2, 1, 2, 3, 4, 5, 5, 7, 7, 9, 10],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == -2

tests/model_executor/test_keye_vl1_5_mrope.py

@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, field
+
+import pytest
+import torch
+
+from vllm.model_executor.models.keye_vl1_5 import KeyeVL1_5ForConditionalGeneration
+from vllm.multimodal.inputs import (
+    MultiModalFeatureSpec,
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    PlaceholderRange,
+)
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+@pytest.fixture(autouse=True, scope="module")
+def _force_cpu_default_device():
+    original = torch.get_default_device()
+    torch.set_default_device("cpu")
+    yield
+    torch.set_default_device(original)
+
+
+@dataclass
+class DummyVisionConfig:
+    spatial_merge_size: int = 2
+
+
+@dataclass
+class DummyConfig:
+    vision_config: DummyVisionConfig = field(default_factory=DummyVisionConfig)
+
+
+def make_model(config: DummyConfig) -> KeyeVL1_5ForConditionalGeneration:
+    model = object.__new__(KeyeVL1_5ForConditionalGeneration)
+    model.config = config
+    return model
+
+
+def make_mm_feature(
+    *,
+    modality: str,
+    offset: int,
+    length: int,
+    grid_thw: tuple[int, int, int] | list[tuple[int, int, int]],
+    is_embed: list[bool] | None = None,
+) -> MultiModalFeatureSpec:
+    field_name = "image_grid_thw" if modality == "image" else "video_grid_thw"
+    return MultiModalFeatureSpec(
+        data=MultiModalKwargsItem(
+            {
+                field_name: MultiModalFieldElem(
+                    data=torch.tensor(grid_thw),
+                    field=None,  # HACK.
+                ),
+            }
+        ),
+        modality=modality,
+        identifier="DUMMY",
+        mm_position=PlaceholderRange(
+            offset=offset,
+            length=length,
+            is_embed=None if is_embed is None else torch.tensor(is_embed),
+        ),
+    )
+
+
+def test_get_mrope_input_positions_text_only():
+    model = make_model(DummyConfig())
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[11, 12, 13, 14, 15],
+        mm_features=[],
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == 0
+
+
+def test_get_mrope_input_positions_single_image():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="image",
+            offset=1,
+            length=4,
+            grid_thw=(1, 4, 4),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 20, 21, 22, 23, 30, 31],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4],
+            [0, 1, 1, 2, 2, 3, 4],
+            [0, 1, 2, 1, 2, 3, 4],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == -2
+
+
+def test_get_mrope_input_positions_video_uses_embed_ranges():
+    model = make_model(DummyConfig())
+    mm_features = [
+        make_mm_feature(
+            modality="video",
+            offset=1,
+            length=8,
+            grid_thw=[(2, 4, 2)],
+            is_embed=[False, False, True, True, False, False, True, True],
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=[10, 101, 102, 20, 21, 103, 104, 30, 31, 40, 41],
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 3, 5, 6, 7, 7, 9, 10],
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            [0, 1, 2, 3, 3, 5, 6, 7, 7, 9, 10],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    assert delta == 0

tests/model_executor/test_paddleocr_vl_mrope.py

@@ -0,0 +1,210 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, field
+
+import pytest
+import torch
+
+from vllm.model_executor.models.paddleocr_vl import (
+    PaddleOCRVLForConditionalGeneration,
+)
+from vllm.multimodal.inputs import (
+    MultiModalFeatureSpec,
+    MultiModalFieldElem,
+    MultiModalKwargsItem,
+    PlaceholderRange,
+)
+
+pytestmark = pytest.mark.skip_global_cleanup
+
+
+@pytest.fixture(autouse=True, scope="module")
+def _force_cpu_default_device():
+    original = torch.get_default_device()
+    torch.set_default_device("cpu")
+    yield
+    torch.set_default_device(original)
+
+
+@dataclass
+class DummyVisionConfig:
+    spatial_merge_size: int = 2
+    patch_size: int = 14
+
+
+@dataclass
+class DummyConfig:
+    image_token_id: int = 151655
+    video_token_id: int = 151654
+    vision_start_token_id: int = 151652
+    vision_end_token_id: int = 151653
+    vision_config: DummyVisionConfig = field(default_factory=DummyVisionConfig)
+
+
+def make_model(config: DummyConfig) -> PaddleOCRVLForConditionalGeneration:
+    model = object.__new__(PaddleOCRVLForConditionalGeneration)
+    model.config = config
+    return model
+
+
+def make_mm_feature(
+    *,
+    offset: int,
+    length: int,
+    image_grid_thw: tuple[int, int, int],
+) -> MultiModalFeatureSpec:
+    return MultiModalFeatureSpec(
+        data=MultiModalKwargsItem(
+            {
+                "image_grid_thw": MultiModalFieldElem(
+                    data=torch.tensor(image_grid_thw),
+                    field=None,
+                ),
+            }
+        ),
+        modality="image",
+        identifier="DUMMY",
+        mm_position=PlaceholderRange(offset=offset, length=length),
+    )
+
+
+def test_get_mrope_input_positions_text_only():
+    model = make_model(DummyConfig())
+    input_tokens = [11, 12, 13, 14, 15]
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=[],
+    )
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+        ]
+    )
+    assert torch.equal(positions, expected)
+    assert delta == 0
+
+
+def test_get_mrope_input_positions_single_image():
+    model = make_model(DummyConfig())
+    spatial_merge_size = model.config.vision_config.spatial_merge_size
+
+    t, h, w = 1, 2, 2
+    num_image_tokens = t * h * w
+
+    input_tokens = (
+        [10]
+        + [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num_image_tokens
+        + [model.config.vision_end_token_id]
+        + [30, 31]
+    )
+
+    mm_features = [
+        make_mm_feature(
+            offset=2,  # 1 (text) + 1 (vision_start)
+            length=num_image_tokens,
+            image_grid_thw=(t, h * spatial_merge_size, w * spatial_merge_size),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 2, 2, 2, 2, 4, 5, 6],
+            [0, 1, 2, 2, 3, 3, 4, 5, 6],
+            [0, 1, 2, 3, 2, 3, 4, 5, 6],
+        ]
+    )
+
+    assert torch.equal(positions, expected)
+    expected_delta = (positions.max().item() + 1) - len(input_tokens)
+    assert delta == expected_delta
+
+
+def test_get_mrope_input_positions_multiple_images():
+    model = make_model(DummyConfig())
+    spatial_merge_size = model.config.vision_config.spatial_merge_size
+
+    t1, h1, w1 = 1, 2, 2
+    num1 = t1 * h1 * w1
+
+    t2, h2, w2 = 1, 1, 3
+    num2 = t2 * h2 * w2
+
+    input_tokens = (
+        [10]
+        + [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num1
+        + [model.config.vision_end_token_id]
+        + [20, 21]
+        + [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num2
+        + [model.config.vision_end_token_id]
+        + [30]
+    )
+
+    mm_features = [
+        make_mm_feature(
+            offset=2,
+            length=num1,
+            image_grid_thw=(t1, h1 * spatial_merge_size, w1 * spatial_merge_size),
+        ),
+        make_mm_feature(
+            offset=2 + num1 + 1 + 2 + 1,
+            length=num2,
+            image_grid_thw=(t2, h2 * spatial_merge_size, w2 * spatial_merge_size),
+        ),
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=mm_features,
+    )
+
+    assert positions.shape == (3, 15)
+    assert not torch.equal(positions[:, 2:6], torch.arange(4).expand(3, 4) + 2)
+    assert not torch.equal(positions[:, 10:13], torch.arange(3).expand(3, 3) + 10)
+
+
+def test_get_mrope_input_positions_image_at_start():
+    model = make_model(DummyConfig())
+    spatial_merge_size = model.config.vision_config.spatial_merge_size
+
+    t, h, w = 1, 2, 2
+    num_tokens = t * h * w
+
+    input_tokens = (
+        [model.config.vision_start_token_id]
+        + [model.config.image_token_id] * num_tokens
+        + [model.config.vision_end_token_id]
+        + [10, 11]
+    )
+
+    mm_features = [
+        make_mm_feature(
+            offset=1,  # start token at index 0
+            length=num_tokens,
+            image_grid_thw=(t, h * spatial_merge_size, w * spatial_merge_size),
+        )
+    ]
+
+    positions, delta = model.get_mrope_input_positions(
+        input_tokens=input_tokens,
+        mm_features=mm_features,
+    )
+
+    expected = torch.tensor(
+        [
+            [0, 1, 1, 1, 1, 3, 4, 5],
+            [0, 1, 1, 2, 2, 3, 4, 5],
+            [0, 1, 2, 1, 2, 3, 4, 5],
+        ]
+    )
+
+    assert torch.equal(positions, expected)

tests/model_executor/test_weight_utils.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import os
 import tempfile
 
 import huggingface_hub.constants
@@ -10,26 +9,10 @@ from huggingface_hub.utils import LocalEntryNotFoundError
 
 from vllm.model_executor.model_loader.weight_utils import (
     download_weights_from_hf,
-    enable_hf_transfer,
     maybe_remap_kv_scale_name,
 )
 
 
-def test_hf_transfer_auto_activation():
-    if "HF_HUB_ENABLE_HF_TRANSFER" in os.environ:
-        # in case it is already set, we can't test the auto activation
-        pytest.skip("HF_HUB_ENABLE_HF_TRANSFER is set, can't test auto activation")
-    enable_hf_transfer()
-    try:
-        # enable hf hub transfer if available
-        import hf_transfer  # type: ignore # noqa
-
-        HF_TRANSFER_ACTIVE = True
-    except ImportError:
-        HF_TRANSFER_ACTIVE = False
-    assert huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER == HF_TRANSFER_ACTIVE
-
-
 def test_download_weights_from_hf():
     with tempfile.TemporaryDirectory() as tmpdir:
         # assert LocalEntryNotFoundError error is thrown
@@ -178,5 +161,4 @@ class TestMaybeRemapKvScaleName:
 
 
 if __name__ == "__main__":
-    test_hf_transfer_auto_activation()
     test_download_weights_from_hf()

tests/models/language/generation/test_common.py

@@ -100,7 +100,7 @@ AITER_MODEL_LIST = [
         pytest.param("bigcode/starcoder2-3b"),  # starcoder2
         pytest.param(
             "TitanML/tiny-mixtral",  # mixtral
-            marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+            marks=[pytest.mark.core_model],
         ),
         pytest.param("swiss-ai/Apertus-8B-Instruct-2509"),  # apertus
         pytest.param(
@@ -143,6 +143,11 @@ def test_models(
         # in parts of the operators
         pytest.skip(f"Skipping '{model}' model test with AITER kernel.")
 
+    if current_platform.is_cpu() and model in ("openai-community/gpt2",):
+        # These models are sensitive to the rounding error
+        # Fuse ops to reduce rounding
+        monkeypatch.setenv("VLLM_CPU_CI_ENV", "0")
+
     with hf_runner(model) as hf_model:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs

tests/models/language/generation/test_granite.py

@@ -15,6 +15,7 @@ MODELS = [
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
+@pytest.mark.cpu_model
 def test_models(
     hf_runner,
     vllm_runner,

tests/models/language/pooling_mteb_test/mteb_embed_utils.py

@@ -74,10 +74,25 @@ class MtebEmbedMixin(mteb.EncoderProtocol):
         return sim
 
 
+class HfMtebEncoder(MtebEmbedMixin):
+    def __init__(self, model):
+        self.model = model
+
+    def encode(
+        self,
+        inputs: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        sentences = [text for batch in inputs for text in batch["text"]]
+        return self.model.encode(sentences)
+
+
 class VllmMtebEncoder(MtebEmbedMixin):
-    def __init__(self, vllm_model):
+    def __init__(self, vllm_model, prompt_prefix: str | None = None):
         self.llm = vllm_model
         self.rng = np.random.default_rng(seed=42)
+        self.prompt_prefix = prompt_prefix
 
     def encode(
         self,
@@ -87,7 +102,11 @@ class VllmMtebEncoder(MtebEmbedMixin):
     ) -> np.ndarray:
         # Hoping to discover potential scheduling
         # issues by randomizing the order.
-        sentences = [text for batch in inputs for text in batch["text"]]
+        sentences = [
+            self.prompt_prefix + text if self.prompt_prefix else text
+            for batch in inputs
+            for text in batch["text"]
+        ]
         r = self.rng.permutation(len(sentences))
         sentences = [sentences[i] for i in r]
         outputs = self.llm.embed(sentences, use_tqdm=False)
@@ -143,6 +162,7 @@ def mteb_test_embed_models(
     vllm_extra_kwargs=None,
     hf_model_callback=None,
     atol=MTEB_EMBED_TOL,
+    prompt_prefix: str | None = None,
 ):
     vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
 
@@ -182,7 +202,7 @@ def mteb_test_embed_models(
             )
 
         vllm_main_score = run_mteb_embed_task(
-            VllmMtebEncoder(vllm_model), MTEB_EMBED_TASKS
+            VllmMtebEncoder(vllm_model, prompt_prefix=prompt_prefix), MTEB_EMBED_TASKS
         )
         vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
         head_dtype = model_config.head_dtype
@@ -210,7 +230,9 @@ def mteb_test_embed_models(
             if hf_model_callback is not None:
                 hf_model_callback(hf_model)
 
-            st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
+            st_main_score = run_mteb_embed_task(
+                HfMtebEncoder(hf_model), MTEB_EMBED_TASKS
+            )
             st_dtype = next(hf_model.model.parameters()).dtype
 
             # Check embeddings close to hf outputs

tests/models/language/pooling_mteb_test/test_baai.py

@@ -69,7 +69,10 @@ MODELS = [
         attn_type="decoder",
         is_prefix_caching_supported=True,
         is_chunked_prefill_supported=True,
-        enable_test=True,
+        # Skip: model's custom tokenizer on HF hub is incompatible with
+        # transformers v5 (sets attrs before super().__init__, triggering
+        # AttributeError on 'verbose' in __getattr__).
+        enable_test=False,
     ),
 ]
 

tests/models/language/pooling_mteb_test/test_gte.py

@@ -72,7 +72,8 @@ MODELS = [
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
-        enable_test=True,
+        # Skip: numerical regression with transformers v5.
+        enable_test=False,
     ),
     ########## ModernBertModel
     EmbedModelInfo(

tests/models/language/pooling_mteb_test/test_jina.py

@@ -28,7 +28,16 @@ EMBEDDING_MODELS = [
         attn_type="encoder_only",
         is_prefix_caching_supported=False,
         is_chunked_prefill_supported=False,
-    )
+    ),
+    EmbedModelInfo(
+        "jinaai/jina-embeddings-v5-text-small",
+        mteb_score=0.794535707854956,
+        architecture="JinaEmbeddingsV5Model",
+        seq_pooling_type="LAST",
+        attn_type="decoder",
+        is_prefix_caching_supported=True,
+        is_chunked_prefill_supported=True,
+    ),
 ]
 
 RERANK_MODELS = [
@@ -46,11 +55,18 @@ RERANK_MODELS = [
 
 @pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
 def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    task = "retrieval" if "v5" in model_info.name else "text-matching"
+    prompt_prefix: str | None = "Document: " if "v5" in model_info.name else None
+
     def hf_model_callback(model):
-        model.encode = partial(model.encode, task="text-matching")
+        model.encode = partial(model.encode, task=task)
 
     mteb_test_embed_models(
-        hf_runner, vllm_runner, model_info, hf_model_callback=hf_model_callback
+        hf_runner,
+        vllm_runner,
+        model_info,
+        hf_model_callback=hf_model_callback,
+        prompt_prefix=prompt_prefix,
     )
 
 
@@ -58,8 +74,10 @@ def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -
 def test_embed_models_correctness(
     hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
 ) -> None:
+    task = "retrieval" if "v5" in model_info.name else "text-matching"
+
     def hf_model_callback(model):
-        model.encode = partial(model.encode, task="text-matching")
+        model.encode = partial(model.encode, task=task)
 
     correctness_test_embed_models(
         hf_runner,
@@ -75,6 +93,10 @@ def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
     mteb_test_rerank_models(vllm_runner, model_info)
 
 
+@pytest.mark.skip(
+    reason="jinaai/jina-embeddings-v3 custom XLMRobertaLoRA model on HF hub "
+    "is incompatible with transformers v5 (missing all_tied_weights_keys)"
+)
 @pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("dimensions", [16, 32])
@@ -93,12 +115,14 @@ def test_matryoshka(
     # ST will strip the input texts, see test_embedding.py
     example_prompts = [str(s).strip() for s in example_prompts]
 
+    task = "retrieval" if "v5" in model_info.name else "text-matching"
+
     with hf_runner(
         model_info.name,
         dtype=dtype,
         is_sentence_transformer=True,
     ) as hf_model:
-        hf_outputs = hf_model.encode(example_prompts, task="text-matching")
+        hf_outputs = hf_model.encode(example_prompts, task=task)
         hf_outputs = matryoshka_fy(hf_outputs, dimensions)
 
     with vllm_runner(

tests/models/multimodal/generation/test_common.py

@@ -186,7 +186,14 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         auto_cls=AutoModel,
         hf_output_post_proc=model_utils.ultravox_trunc_hf_output,
-        marks=[pytest.mark.core_model, pytest.mark.cpu_model],
+        marks=[
+            pytest.mark.core_model,
+            pytest.mark.cpu_model,
+            # TODO: Remove skip once model has been upstreamed to Transformers
+            pytest.mark.skip(
+                reason="Custom model code is not compatible with Transformers v5"
+            ),
+        ],
     ),
     #### Transformers fallback to test
     ## To reduce test burden, we only test batching arbitrary image size
@@ -397,14 +404,14 @@ VLM_TEST_SETTINGS = {
     "gemma4": VLMTestInfo(
         models=["google/gemma-4-E2B-it"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt: f"<bos><start_of_turn>user\n{img_prompt}<end_of_turn>\n<start_of_turn>model\n",  # noqa: E501
+        prompt_formatter=lambda img_prompt: f"<bos><|turn>user\n{img_prompt}<turn|>\n<|turn>model\n",  # noqa: E501
         single_image_prompts=IMAGE_ASSETS.prompts(
             {
-                "stop_sign": "What's the content in the center of the image?",
-                "cherry_blossom": "What is the season?",
+                "stop_sign": "<|image|>What's the content in the center of the image?",  # noqa: E501
+                "cherry_blossom": "<|image|>What is the season?",
             }
         ),
-        multi_image_prompt="Describe the two images in detail.",
+        multi_image_prompt="<|image|><|image|>Describe the two images in detail.",  # noqa: E501
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -533,6 +540,12 @@ VLM_TEST_SETTINGS = {
         max_model_len=4096,
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.internvl_patch_hf_runner,
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[
+            pytest.mark.skip(
+                reason="Custom model code tries to access data from meta-tensor"
+            )
+        ],
     ),
     "intern_vl-video": VLMTestInfo(
         models=[
@@ -545,6 +558,12 @@ VLM_TEST_SETTINGS = {
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.internvl_patch_hf_runner,
         num_logprobs=10 if current_platform.is_rocm() else 5,
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[
+            pytest.mark.skip(
+                reason="Custom model code tries to access data from meta-tensor"
+            )
+        ],
     ),
     "intern_vl-hf": VLMTestInfo(
         models=["OpenGVLab/InternVL3-1B-hf"],
@@ -591,6 +610,8 @@ VLM_TEST_SETTINGS = {
         hf_model_kwargs={"device_map": "auto"},
         patch_hf_runner=model_utils.isaac_patch_hf_runner,
         image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[pytest.mark.skip(reason="Custom model imports deleted object")],  # noqa: E501
     ),
     "kimi_vl": VLMTestInfo(
         models=["moonshotai/Kimi-VL-A3B-Instruct"],
@@ -806,7 +827,12 @@ VLM_TEST_SETTINGS = {
             pytest.mark.skipif(
                 Version(TRANSFORMERS_VERSION) == Version("4.57.3"),
                 reason="This model is broken in Transformers v4.57.3",
-            )
+            ),
+            pytest.mark.skipif(
+                Version(TRANSFORMERS_VERSION) >= Version("5.0.0"),
+                reason="Model's custom code uses ROPE_INIT_FUNCTIONS"
+                "['default'] which was removed in transformers v5",
+            ),
         ],
     ),
     "phi3v": VLMTestInfo(
@@ -960,6 +986,12 @@ VLM_TEST_SETTINGS = {
             )
             for inp in custom_inputs.different_patch_input_cases_internvl()
         ],
+        # TODO: Remove skip once model has been upstreamed to Transformers
+        marks=[
+            pytest.mark.skip(
+                reason="Custom model code tries to access data from meta-tensor"
+            )
+        ],
     ),
     "llava_onevision-multiple-images": VLMTestInfo(
         models=["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"],

tests/models/multimodal/generation/test_nemotron_parse.py

@@ -103,6 +103,10 @@ def run_test(
         )
 
 
+@pytest.mark.skip(
+    reason="Model's custom MBart decoder has head count mismatch with "
+    "transformers v5's GQA-aware cross-attention (8 vs 16 heads)"
+)
 @pytest.mark.parametrize("model", ["nvidia/NVIDIA-Nemotron-Parse-v1.1"])
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("num_logprobs", [5])

tests/models/multimodal/generation/test_phi4siglip.py

@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Sequence
+from importlib.metadata import version
 
 import pytest
 import regex as re
+from packaging.version import Version
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from vllm.logprobs import SampleLogprobs
@@ -19,6 +21,15 @@ from ....conftest import (
 from ....utils import multi_gpu_test
 from ...utils import check_logprobs_close
 
+pytestmark = pytest.mark.skipif(
+    Version("5.0") <= Version(version("transformers")),
+    reason=(
+        "vllm upgraded transformers above v5.4 where HF model custom code uses siglip2 "
+        "internals (filter_out_non_signature_kwargs) removed by "
+        "huggingface/transformers#43514"
+    ),
+)
+
 MODEL_ID = "microsoft/Phi-4-reasoning-vision-15B"
 
 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(

tests/models/multimodal/generation/test_voxtral.py

@@ -149,6 +149,10 @@ def test_online_serving(vllm_runner, audio_assets: AudioTestAssets):
     )
 
 
+@pytest.mark.skip(
+    reason="VoxtralProcessor.apply_chat_template() in transformers v5 "
+    "doesn't resolve chat_template=None to the default template"
+)
 def test_hf_reference(hf_runner, vllm_runner, audio_assets: AudioTestAssets):
     """Compare vLLM Mistral-format output against HF Transformers reference.
 

tests/models/multimodal/generation/vlm_utils/core.py

@@ -80,6 +80,11 @@ def run_test(
     if vllm_runner_kwargs:
         vllm_runner_kwargs_.update(vllm_runner_kwargs)
 
+    # Avoid passing limit_mm_per_prompt twice when vllm_runner_kwargs
+    # already contains it (e.g. gemma4 sets it via vllm_runner_kwargs).
+    if "limit_mm_per_prompt" in vllm_runner_kwargs_:
+        limit_mm_per_prompt = vllm_runner_kwargs_.pop("limit_mm_per_prompt")
+
     with vllm_runner(
         model,
         max_model_len=max_model_len,

tests/models/multimodal/pooling/test_colqwen3.py

@@ -22,6 +22,11 @@ from vllm.entrypoints.pooling.scoring.typing import ScoreMultiModalParam
 
 from ....conftest import VllmRunner
 
+pytestmark = pytest.mark.skip(
+    reason="ColQwen3 model's weight tying is incompatible with "
+    "transformers v5 (missing all_tied_weights_keys)"
+)
+
 MODELS = [
     "TomoroAI/tomoro-colqwen3-embed-4b",
     "OpenSearch-AI/Ops-Colqwen3-4B",

tests/models/multimodal/pooling/test_intern_vit.py

@@ -12,6 +12,11 @@ from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 from ....conftest import ImageTestAssets
 
+pytestmark = pytest.mark.skip(
+    reason="InternVisionModel's custom code is incompatible with "
+    "transformers v5 (missing all_tied_weights_keys)"
+)
+
 # we use snapshot_download to prevent conflicts between
 # dynamic_module and trust_remote_code for hf_runner
 DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]

tests/models/multimodal/pooling/test_jinavl_reranker.py

@@ -15,6 +15,11 @@ from vllm.entrypoints.pooling.scoring.typing import ScoreMultiModalParam
 
 from ....conftest import HfRunner, VllmRunner
 
+pytestmark = pytest.mark.skip(
+    reason="jinaai/jina-reranker-m0 custom code is incompatible with "
+    "transformers v5 (missing all_tied_weights_keys)"
+)
+
 MODELS = ["jinaai/jina-reranker-m0"]
 
 MM_PROCESSOR_KWARGS = {

tests/models/multimodal/processing/test_musicflamingo.py

@@ -17,11 +17,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from importlib.metadata import version
 from unittest.mock import MagicMock
 
 import numpy as np
 import pytest
 import torch
+from packaging.version import Version
 from transformers import PretrainedConfig
 
 from tests.models.registry import HF_EXAMPLE_MODELS
@@ -122,6 +124,11 @@ def test_musicflamingo_dummy_text_uses_plain_audio_tokens(mock_ctx):
     assert builder.get_dummy_text({"audio": 2}) == "<sound><sound>"
 
 
+@pytest.mark.skipif(
+    Version(version("transformers")) >= Version("5.5"),
+    reason="transformers v5.5 added native MusicFlamingoForConditionalGeneration "
+    "with a different get_audio_features signature (requires input_ids)",
+)
 def test_musicflamingo_audio_feature_pipeline_matches_hf_small_config():
     from transformers.models.musicflamingo import (
         modeling_musicflamingo as hf_musicflamingo_modeling,

tests/models/registry.py

@@ -335,7 +335,15 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
         "internlm/internlm2-chat-7b", trust_remote_code=True
     ),
     "InternLM2VEForCausalLM": _HfExamplesInfo(
-        "OpenGVLab/Mono-InternVL-2B", trust_remote_code=True
+        "OpenGVLab/Mono-InternVL-2B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "Custom config cannot be loaded with Transformers "
+                "v5 because `vision_config` is not always set"
+            )
+        },
     ),
     "InternLM3ForCausalLM": _HfExamplesInfo(
         "internlm/internlm3-8b-instruct", trust_remote_code=True
@@ -475,6 +483,13 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "Plamo2ForCausalLM": _HfExamplesInfo(
         "pfnet/plamo-2-1b",
         trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "hf": (
+                "Custom model code uses `_tied_weight_keys: list[str]` but "
+                "Transformers v5 now expects `_tied_weight_keys: dict[str, str]`"
+            )
+        },
     ),
     "Plamo3ForCausalLM": _HfExamplesInfo(
         "pfnet/plamo-3-nict-2b-base",
@@ -515,6 +530,13 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
         trust_remote_code=True,
         max_model_len=4096,
         is_available_online=True,
+        max_transformers_version="5.3",
+        transformers_version_reason={
+            "vllm": (
+                "vllm upgraded transformers above v5.4 where "
+                "validate_rope() no longer accepts ignore_keys param"
+            )
+        },
     ),
     "SeedOssForCausalLM": _HfExamplesInfo(
         "ByteDance-Seed/Seed-OSS-36B-Instruct",
@@ -553,6 +575,11 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
         "xverse/XVERSE-7B-Chat",
         tokenizer="meta-llama/Llama-2-7b",
         trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "XVERSE tokenizer is incompatible with transformers v5 "
+            "(add_prefix_space / prepend_scheme mismatch).",
+        },
     ),
     "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct"),
     "MiMoForCausalLM": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL", trust_remote_code=True),
@@ -582,6 +609,10 @@ _EMBEDDING_EXAMPLE_MODELS = {
         trust_remote_code=True,
         hf_overrides={"architectures": ["GteNewModel"]},
     ),
+    "JinaEmbeddingsV5Model": _HfExamplesInfo(
+        "jinaai/jina-embeddings-v5-text-small",
+        trust_remote_code=True,
+    ),
     "LlamaModel": _HfExamplesInfo("llama", is_available_online=False),
     "LlamaBidirectionalModel": _HfExamplesInfo(
         "nvidia/llama-nemotron-embed-1b-v2", trust_remote_code=True
@@ -763,10 +794,18 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     # [Decoder-only]
     "AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria"),
     "AudioFlamingo3ForConditionalGeneration": _HfExamplesInfo(
-        "nvidia/audio-flamingo-3-hf", min_transformers_version="5.0.0"
+        "nvidia/audio-flamingo-3-hf",
+        min_transformers_version="5.3.0",
+        transformers_version_reason={
+            "vllm": "Needs https://github.com/huggingface/transformers/pull/43538"
+        },
     ),
     "MusicFlamingoForConditionalGeneration": _HfExamplesInfo(
-        "nvidia/music-flamingo-2601-hf", min_transformers_version="5.3.0"
+        "nvidia/music-flamingo-2601-hf",
+        min_transformers_version="5.3.0",
+        transformers_version_reason={
+            "vllm": "Needs https://github.com/huggingface/transformers/pull/43538"
+        },
     ),
     "AyaVisionForConditionalGeneration": _HfExamplesInfo("CohereLabs/aya-vision-8b"),
     "BagelForConditionalGeneration": _HfExamplesInfo("ByteDance-Seed/BAGEL-7B-MoT"),
@@ -821,12 +860,30 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     ),
     "FireRedASR2ForConditionalGeneration": _HfExamplesInfo(
         "allendou/FireRedASR2-LLM-vllm",
+        trust_remote_code=True,
+        max_transformers_version="5.1",
+        transformers_version_reason={
+            "vllm": "Incompatible with transformers v5.2+ "
+            "(dict object has no attribute '__name__').",
+        },
     ),
     "FireRedLIDForConditionalGeneration": _HfExamplesInfo(
         "PatchyTisa/FireRedLID-vllm",
+        trust_remote_code=True,
+        max_transformers_version="5.1",
+        transformers_version_reason={
+            "vllm": "Incompatible with transformers v5.2+ "
+            "(dict object has no attribute '__name__').",
+        },
     ),
     "FunASRForConditionalGeneration": _HfExamplesInfo(
         "allendou/Fun-ASR-Nano-2512-vllm",
+        trust_remote_code=True,
+        max_transformers_version="5.1",
+        transformers_version_reason={
+            "vllm": "Incompatible with transformers v5.2+ "
+            "(dict object has no attribute '__name__').",
+        },
     ),
     "FunAudioChatForConditionalGeneration": _HfExamplesInfo(
         "funaudiochat", is_available_online=False
@@ -868,6 +925,13 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "HCXVisionForCausalLM": _HfExamplesInfo(
         "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B",
         trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "Custom config cannot be loaded with Transformers "
+                "v5 because `text_config` is not always set"
+            )
+        },
     ),
     "HCXVisionV2ForCausalLM": _HfExamplesInfo(
         "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B",
@@ -887,7 +951,12 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         extras={"0.2-2B-Preview": "PerceptronAI/Isaac-0.2-2B-Preview"},
     ),
     "InternS1ForConditionalGeneration": _HfExamplesInfo(
-        "internlm/Intern-S1", trust_remote_code=True
+        "internlm/Intern-S1",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Custom tokenizer code is not compatible with Transformers v5."
+        },
     ),
     "InternS1ProForConditionalGeneration": _HfExamplesInfo(
         "internlm/Intern-S1-Pro",
@@ -976,7 +1045,14 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "MiDashengLMModel": _HfExamplesInfo(
         "mispeech/midashenglm-7b", trust_remote_code=True
     ),
-    "MiniCPMO": _HfExamplesInfo("openbmb/MiniCPM-o-2_6", trust_remote_code=True),
+    "MiniCPMO": _HfExamplesInfo(
+        "openbmb/MiniCPM-o-2_6",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "hf": "Custom processor code is not compatible with Transformers v5."
+        },
+    ),
     "MiniCPMV": _HfExamplesInfo(
         "openbmb/MiniCPM-Llama3-V-2_5",
         extras={
@@ -984,6 +1060,13 @@ _MULTIMODAL_EXAMPLE_MODELS = {
             "4.0": "openbmb/MiniCPM-V-4",
             "4.5": "openbmb/MiniCPM-V-4_5",
         },
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "MiniCPMVBatchFeature is incompatible with its base class in "
+                "Transformers v5. See https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/discussions/78"
+            )
+        },
         trust_remote_code=True,
     ),
     "MiniMaxVL01ForConditionalGeneration": _HfExamplesInfo(
@@ -1038,14 +1121,70 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         },
         trust_remote_code=True,
     ),
+    # NemotronH_Nano_Omni_Reasoning_V3 is an alias for NemotronH_Nano_VL_V2
+    # Use the same registry test as NemotronH_Nano_VL_V2 above
+    "NemotronH_Nano_Omni_Reasoning_V3": _HfExamplesInfo(
+        "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16",
+        max_model_len=4096,
+        use_original_num_layers=True,
+        hf_overrides={
+            "vision_config": PretrainedConfig(
+                args={
+                    "min_num_patches": 1,
+                    "max_num_patches": 12,
+                    "model": "vit_huge_patch16_224",
+                },
+                video_temporal_patch_size=2,
+            ),
+            "text_config": {
+                "num_hidden_layers": 2,
+                "hybrid_override_pattern": "M*",
+            },
+        },
+        trust_remote_code=True,
+    ),
+    # NemotronH_Super_Omni_Reasoning_V3 is an alias for NemotronH_Nano_VL_V2 as well
+    # Use the same registry test as NemotronH_Nano_VL_V2 above
+    "NemotronH_Super_Omni_Reasoning_V3": _HfExamplesInfo(
+        "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16",
+        max_model_len=4096,
+        use_original_num_layers=True,
+        hf_overrides={
+            "vision_config": PretrainedConfig(
+                args={
+                    "min_num_patches": 1,
+                    "max_num_patches": 12,
+                    "model": "vit_huge_patch16_224",
+                },
+                video_temporal_patch_size=2,
+            ),
+            "text_config": {
+                "num_hidden_layers": 2,
+                "hybrid_override_pattern": "M*",
+            },
+        },
+        trust_remote_code=True,
+    ),
     "OpenCUAForConditionalGeneration": _HfExamplesInfo(
-        "xlangai/OpenCUA-7B", trust_remote_code=True
+        "xlangai/OpenCUA-7B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Tokenizer cannot be initialised in Transformers v5."
+        },
     ),
     "OpenPanguVLForConditionalGeneration": _HfExamplesInfo(
         "FreedomIntelligence/openPangu-VL-7B",
         trust_remote_code=True,
         max_model_len=4096,
         enforce_eager=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": (
+                "OpenPanguVLVideoProcessorInitKwargs does not specify total=False, "
+                "making all kwargs required. See https://huggingface.co/FreedomIntelligence/openPangu-VL-7B/discussions/2"
+            )
+        },
     ),
     "Ovis": _HfExamplesInfo(
         "AIDC-AI/Ovis2-1B",
@@ -1057,12 +1196,24 @@ _MULTIMODAL_EXAMPLE_MODELS = {
             "1.6-gemma": "AIDC-AI/Ovis1.6-Gemma2-9B",
         },
     ),
-    "Ovis2_5": _HfExamplesInfo("AIDC-AI/Ovis2.5-2B", trust_remote_code=True),
+    "Ovis2_5": _HfExamplesInfo(
+        "AIDC-AI/Ovis2.5-2B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Custom processor code is not compatible with Transformers v5."
+        },
+    ),
     "Ovis2_6ForCausalLM": _HfExamplesInfo(
         "AIDC-AI/Ovis2.6-2B", is_available_online=False, trust_remote_code=True
     ),
     "Ovis2_6_MoeForCausalLM": _HfExamplesInfo(
-        "AIDC-AI/Ovis2.6-30B-A3B", trust_remote_code=True
+        "AIDC-AI/Ovis2.6-30B-A3B",
+        trust_remote_code=True,
+        max_transformers_version="4.57",
+        transformers_version_reason={
+            "vllm": "Custom processor code is not compatible with Transformers v5."
+        },
     ),
     "PaddleOCRVLForConditionalGeneration": _HfExamplesInfo(
         "PaddlePaddle/PaddleOCR-VL",
@@ -1082,7 +1233,17 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         extras={"phi3.5": "microsoft/Phi-3.5-vision-instruct"},
     ),
     "Phi4ForCausalLMV": _HfExamplesInfo(
-        "microsoft/Phi-4-reasoning-vision-15B", trust_remote_code=True
+        "microsoft/Phi-4-reasoning-vision-15B",
+        trust_remote_code=True,
+        max_transformers_version="5.3",
+        transformers_version_reason={
+            "vllm": (
+                "vllm upgraded transformers above v5.4 where HF model "
+                "custom code uses siglip2 internals "
+                "(filter_out_non_signature_kwargs) removed "
+                "by huggingface/transformers#43514"
+            )
+        },
     ),
     "Phi4MMForCausalLM": _HfExamplesInfo(
         "microsoft/Phi-4-multimodal-instruct", trust_remote_code=True
@@ -1179,6 +1340,14 @@ _MULTIMODAL_EXAMPLE_MODELS = {
             "architectures": ["Tarsier2ForConditionalGeneration"],
             "model_type": "tarsier2",
         },
+        max_transformers_version="5.3",
+        transformers_version_reason={
+            "vllm": (
+                "Qwen2VLConfig was split into Qwen2VLConfig + "
+                "Qwen2VLTextConfig in transformers v5, breaking "
+                "attribute access (num_attention_heads, hidden_size, etc.)"
+            )
+        },
     ),
     "VoxtralForConditionalGeneration": _HfExamplesInfo(
         "mistralai/Voxtral-Mini-3B-2507",

tests/models/utils.py

@@ -476,7 +476,16 @@ def dummy_hf_overrides(
     else:
         # Use minimal layers for testing
         num_layers = 1
-        num_hidden_layers = 3 if model_arch == "Gemma3nForConditionalGeneration" else 1
+        num_hidden_layers = (
+            3
+            if model_arch
+            in (
+                "Gemma3nForConditionalGeneration",
+                "Gemma4ForCausalLM",
+                "Gemma4ForConditionalGeneration",
+            )
+            else 1
+        )
 
     update_dict = {
         "num_layers": num_layers,

tests/reasoning/test_step3p5_reasoning_parser.py

@@ -2,10 +2,10 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pytest
-from transformers import AutoTokenizer
 
 from tests.reasoning.utils import run_reasoning_extraction
 from vllm.reasoning import ReasoningParser, ReasoningParserManager
+from vllm.tokenizers import get_tokenizer
 
 parser_name = "step3p5"
 start_token = "<think>"
@@ -16,7 +16,7 @@ REASONING_MODEL_NAME = "stepfun-ai/Step-3.5-Flash"
 
 @pytest.fixture(scope="module")
 def step3p5_tokenizer():
-    return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
+    return get_tokenizer(tokenizer_name=REASONING_MODEL_NAME)
 
 
 SIMPLE_REASONING = {

tests/tool_parsers/test_ernie45_moe_tool_parser.py

@@ -328,7 +328,7 @@ def test_extract_tool_calls_streaming_incremental(
     expected_content,
 ):
     """Verify the Ernie45 Parser streaming behavior by verifying each chunk is as expected."""  # noqa: E501
-    request = ChatCompletionRequest(model=MODEL, messages=[], tools=[])
+    request = ChatCompletionRequest(model=MODEL, messages=[])
 
     tool_calls_dict = {}
     for delta_message in stream_delta_message_generator(

tests/tool_parsers/test_xlam_tool_parser.py

@@ -484,7 +484,7 @@ def test_extract_tool_calls_streaming_incremental(
     expected_content,
 ):
     """Verify the XLAM Parser streaming behavior by verifying each chunk is as expected."""  # noqa: E501
-    request = ChatCompletionRequest(model=MODEL, messages=[], tools=[])
+    request = ChatCompletionRequest(model=MODEL, messages=[])
 
     chunks = []
     for delta_message in stream_delta_message_generator(

tests/tool_use/mistral/test_mistral_tool_calls.py

@@ -1,25 +1,198 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import json
+from dataclasses import dataclass, field
+
 import openai
 import pytest
 
-from tests.tool_use.utils import MESSAGES_ASKING_FOR_TOOLS, WEATHER_TOOL
+from tests.tool_use.utils import (
+    MESSAGES_ASKING_FOR_PARALLEL_TOOLS,
+    MESSAGES_ASKING_FOR_TOOLS,
+    MESSAGES_WITH_TOOL_RESPONSE,
+    MESSAGES_WITHOUT_TOOLS,
+    SEARCH_TOOL,
+    SEED,
+    WEATHER_TOOL,
+    ensure_system_prompt,
+)
+
+from .utils import ServerConfig
+
+
+def _requires_tool_parser(server_config: ServerConfig) -> None:
+    r"""Skip test if server was not started with --tool-call-parser."""
+    if "--tool-call-parser" not in server_config.get("arguments", []):
+        pytest.skip(
+            f"Skipping: {server_config['model']} not configured with --tool-call-parser"
+        )
+
+
+def _is_pre_v11(server_config: ServerConfig) -> bool:
+    r"""Pre-v11 Mistral models lack grammar-based tool call enforcement."""
+    return "7B" in server_config.get("model", "")
+
+
+@dataclass
+class StreamedToolCallResult:
+    r"""Accumulated result from streaming a single tool call."""
+
+    function_name: str | None = None
+    function_args_str: str = ""
+    tool_call_id: str | None = None
+    role_name: str | None = None
+    finish_reason_count: int = 0
+    finish_reason: str | None = None
+
+
+async def _collect_streamed_tool_call(
+    stream: openai.AsyncStream,
+    *,
+    expected_finish_reason: str = "tool_calls",
+) -> StreamedToolCallResult:
+    result = StreamedToolCallResult()
+
+    async for chunk in stream:
+        if chunk.choices[0].finish_reason:
+            result.finish_reason_count += 1
+            result.finish_reason = chunk.choices[0].finish_reason
+            assert chunk.choices[0].finish_reason == expected_finish_reason
+
+        if chunk.choices[0].delta.role:
+            assert not result.role_name or result.role_name == "assistant"
+            result.role_name = "assistant"
+
+        streamed_tool_calls = chunk.choices[0].delta.tool_calls
+        if streamed_tool_calls and len(streamed_tool_calls) > 0:
+            assert len(streamed_tool_calls) == 1
+            tool_call = streamed_tool_calls[0]
+
+            if tool_call.id:
+                assert not result.tool_call_id
+                result.tool_call_id = tool_call.id
+
+            if tool_call.function:
+                if tool_call.function.name:
+                    assert result.function_name is None
+                    result.function_name = tool_call.function.name
+                if tool_call.function.arguments:
+                    result.function_args_str += tool_call.function.arguments
+
+    return result
+
+
+@dataclass
+class StreamedContentResult:
+    r"""Accumulated result from streaming a content-only response."""
+
+    chunks: list[str] = field(default_factory=list)
+    finish_reason_count: int = 0
+    finish_reason: str | None = None
+    role_sent: bool = False
+
+
+async def _collect_streamed_content(
+    stream: openai.AsyncStream,
+    *,
+    expected_finish_reason: str | None = None,
+    no_tool_calls: bool = True,
+) -> StreamedContentResult:
+    r"""Consume a streaming response and collect text content."""
+    result = StreamedContentResult()
+
+    async for chunk in stream:
+        delta = chunk.choices[0].delta
+
+        if delta.role:
+            assert not result.role_sent
+            assert delta.role == "assistant"
+            result.role_sent = True
+
+        if delta.content:
+            result.chunks.append(delta.content)
+
+        if chunk.choices[0].finish_reason is not None:
+            result.finish_reason_count += 1
+            result.finish_reason = chunk.choices[0].finish_reason
+            if expected_finish_reason is not None:
+                assert result.finish_reason == expected_finish_reason
+
+        if no_tool_calls:
+            assert not delta.tool_calls or len(delta.tool_calls) == 0
+
+    return result
+
+
+@dataclass
+class StreamedParallelToolCallResult:
+    r"""Accumulated result from streaming parallel tool calls."""
+
+    function_names: list[str] = field(default_factory=list)
+    function_args_strs: list[str] = field(default_factory=list)
+    tool_call_ids: list[str] = field(default_factory=list)
+    role_name: str | None = None
+    finish_reason_count: int = 0
+
+
+async def _collect_streamed_parallel_tool_calls(
+    stream: openai.AsyncStream,
+) -> StreamedParallelToolCallResult:
+    r"""Consume a streaming response and collect parallel tool calls."""
+    result = StreamedParallelToolCallResult()
+    tool_call_idx: int = -1
+
+    async for chunk in stream:
+        if chunk.choices[0].finish_reason:
+            result.finish_reason_count += 1
+            assert chunk.choices[0].finish_reason == "tool_calls"
+
+        if chunk.choices[0].delta.role:
+            assert not result.role_name or result.role_name == "assistant"
+            result.role_name = "assistant"
+
+        streamed_tool_calls = chunk.choices[0].delta.tool_calls
+        if streamed_tool_calls and len(streamed_tool_calls) > 0:
+            assert len(streamed_tool_calls) == 1
+            tool_call = streamed_tool_calls[0]
+
+            if tool_call.index != tool_call_idx:
+                tool_call_idx = tool_call.index
+                result.function_args_strs.append("")
+                result.tool_call_ids.append("")
+
+            if tool_call.id:
+                result.tool_call_ids[tool_call.index] = tool_call.id
+
+            if tool_call.function:
+                if tool_call.function.name:
+                    result.function_names.append(tool_call.function.name)
+                if tool_call.function.arguments:
+                    result.function_args_strs[tool_call.index] += (
+                        tool_call.function.arguments
+                    )
+
+    return result
 
 
 # test: a tool_choice with mistral-tokenizer results in an ID of length 9
 @pytest.mark.asyncio
-async def test_tool_call_with_tool_choice(client: openai.AsyncOpenAI):
+async def test_tool_call_with_tool_choice(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+
     models = await client.models.list()
     model_name: str = models.data[0].id
     chat_completion = await client.chat.completions.create(
-        messages=MESSAGES_ASKING_FOR_TOOLS,
+        messages=ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
         temperature=0,
         max_completion_tokens=100,
         model=model_name,
         tools=[WEATHER_TOOL],
         tool_choice=WEATHER_TOOL,
         logprobs=False,
+        seed=SEED,
     )
 
     choice = chat_completion.choices[0]
@@ -28,3 +201,307 @@ async def test_tool_call_with_tool_choice(client: openai.AsyncOpenAI):
     assert choice.message.role == "assistant"
     assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 1
     assert len(choice.message.tool_calls[0].id) == 9  # length of 9 for mistral
+
+
+_NOT_SET = object()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "tools, tool_choice, streaming_id_len_pre_v11",
+    [
+        pytest.param(
+            [WEATHER_TOOL, SEARCH_TOOL],
+            _NOT_SET,
+            9,
+            id="auto",
+        ),
+        pytest.param(
+            [WEATHER_TOOL],
+            "required",
+            30,
+            id="required",
+        ),
+    ],
+)
+async def test_tool_call_auto_or_required(
+    client: openai.AsyncOpenAI,
+    server_config: ServerConfig,
+    tools: list,
+    tool_choice: object,
+    streaming_id_len_pre_v11: int,
+) -> None:
+    _requires_tool_parser(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    create_kwargs: dict = {
+        "messages": ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
+        "temperature": 0,
+        "max_completion_tokens": 100,
+        "model": model_name,
+        "tools": tools,
+        "logprobs": False,
+        "seed": SEED,
+    }
+    if tool_choice is not _NOT_SET:
+        create_kwargs["tool_choice"] = tool_choice
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(**create_kwargs)
+
+    choice = chat_completion.choices[0]
+    tool_calls = choice.message.tool_calls
+
+    assert choice.finish_reason == "tool_calls"
+    assert tool_calls is not None and len(tool_calls) >= 1
+    assert tool_calls[0].function.name == "get_current_weather"
+    parsed_arguments = json.loads(tool_calls[0].function.arguments)
+    assert "city" in parsed_arguments
+    assert len(tool_calls[0].id) == 9
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(**create_kwargs, stream=True)
+
+    result = await _collect_streamed_tool_call(stream)
+
+    assert result.finish_reason_count == 1
+    assert result.role_name == "assistant"
+    assert result.function_name == "get_current_weather"
+    streamed_args = json.loads(result.function_args_str)
+    assert isinstance(result.tool_call_id, str)
+    if _is_pre_v11(server_config):
+        assert len(result.tool_call_id) == streaming_id_len_pre_v11
+    else:
+        assert len(result.tool_call_id) == 9
+    assert parsed_arguments == streamed_args
+
+
+@pytest.mark.asyncio
+async def test_tool_call_none_with_tools(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        tool_choice="none",
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+
+    assert choice.finish_reason != "tool_calls"
+    assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 0
+    assert choice.message.content is not None
+    # Without grammar enforcement, pre-v11 models may still emit [TOOL_CALLS]
+    if not _is_pre_v11(server_config):
+        assert "[TOOL_CALLS]" not in choice.message.content
+
+    non_streaming_content = choice.message.content
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_ASKING_FOR_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        tool_choice="none",
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    # Pre-v11 models lack grammar enforcement, so the model may still
+    # emit tool calls even with tool_choice="none".
+    pre_v11 = _is_pre_v11(server_config)
+    result = await _collect_streamed_content(stream, no_tool_calls=not pre_v11)
+
+    assert result.finish_reason_count == 1
+    if not pre_v11:
+        assert result.finish_reason != "tool_calls"
+    streamed_content = "".join(result.chunks)
+    if not pre_v11:
+        assert "[TOOL_CALLS]" not in streamed_content
+        assert streamed_content == non_streaming_content
+
+
+@pytest.mark.asyncio
+async def test_chat_without_tools(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITHOUT_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=150,
+        model=model_name,
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+    output_text = choice.message.content
+
+    assert output_text is not None and len(output_text) > 0
+    assert choice.finish_reason != "tool_calls"
+    assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 0
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITHOUT_TOOLS, server_config),
+        temperature=0,
+        max_completion_tokens=150,
+        model=model_name,
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    result = await _collect_streamed_content(
+        stream, expected_finish_reason=choice.finish_reason
+    )
+
+    assert result.role_sent
+    assert result.finish_reason_count == 1
+    assert len(result.chunks)
+    assert "".join(result.chunks) == output_text
+
+
+@pytest.mark.asyncio
+async def test_tool_call_with_results(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITH_TOOL_RESPONSE, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL, SEARCH_TOOL],
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+
+    assert choice.finish_reason != "tool_calls"
+    assert choice.message.tool_calls is None or len(choice.message.tool_calls) == 0
+    assert choice.message.content is not None
+    assert "98" in choice.message.content
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(MESSAGES_WITH_TOOL_RESPONSE, server_config),
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL, SEARCH_TOOL],
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    result = await _collect_streamed_content(
+        stream, expected_finish_reason=choice.finish_reason
+    )
+
+    assert result.role_sent
+    assert result.finish_reason_count == 1
+    assert len(result.chunks)
+    assert "".join(result.chunks) == choice.message.content
+
+
+def _requires_parallel(server_config: ServerConfig) -> None:
+    r"""Skip test if the model does not support parallel tool calls."""
+    if not server_config.get("supports_parallel"):
+        pytest.skip(
+            f"Skipping: {server_config['model']} does not support parallel tool calls"
+        )
+
+
+@pytest.mark.asyncio
+async def test_tool_call_parallel(
+    client: openai.AsyncOpenAI, server_config: ServerConfig
+) -> None:
+    _requires_tool_parser(server_config)
+    _requires_parallel(server_config)
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+
+    # --- non-streaming ---
+    chat_completion = await client.chat.completions.create(
+        messages=ensure_system_prompt(
+            MESSAGES_ASKING_FOR_PARALLEL_TOOLS, server_config
+        ),
+        temperature=0,
+        max_completion_tokens=200,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        logprobs=False,
+        seed=SEED,
+    )
+
+    choice = chat_completion.choices[0]
+    tool_calls = choice.message.tool_calls
+
+    assert choice.finish_reason == "tool_calls"
+    assert tool_calls is not None and len(tool_calls) >= 2
+    for tc in tool_calls:
+        assert tc.type == "function"
+        assert tc.function.name == "get_current_weather"
+        assert isinstance(tc.function.arguments, str)
+        parsed = json.loads(tc.function.arguments)
+        assert "city" in parsed
+        assert len(tc.id) == 9
+
+    non_streaming_tool_calls = tool_calls
+
+    # --- streaming ---
+    stream = await client.chat.completions.create(
+        messages=ensure_system_prompt(
+            MESSAGES_ASKING_FOR_PARALLEL_TOOLS, server_config
+        ),
+        temperature=0,
+        max_completion_tokens=200,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        logprobs=False,
+        seed=SEED,
+        stream=True,
+    )
+
+    result = await _collect_streamed_parallel_tool_calls(stream)
+
+    assert result.finish_reason_count == 1
+    assert result.role_name == "assistant"
+    assert len(result.function_names) >= 2
+    assert all(name == "get_current_weather" for name in result.function_names)
+    assert len(result.tool_call_ids) >= 2
+    assert all(isinstance(tid, str) and len(tid) == 9 for tid in result.tool_call_ids)
+
+    for args_str in result.function_args_strs:
+        streamed_args = json.loads(args_str)
+        assert "city" in streamed_args
+
+    assert len(result.function_names) == len(non_streaming_tool_calls)

tests/tool_use/mistral/utils.py

@@ -2,16 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 
-from typing_extensions import TypedDict
-
-
-class ServerConfig(TypedDict, total=False):
-    model: str
-    arguments: list[str]
-    system_prompt: str | None
-    supports_parallel: bool | None
-    supports_rocm: bool | None
-
+from tests.tool_use.utils import ServerConfig
 
 ARGS: list[str] = ["--max-model-len", "1024"]
 
@@ -21,6 +12,11 @@ CONFIGS: dict[str, ServerConfig] = {
         "arguments": [
             "--tokenizer-mode",
             "mistral",
+            "--tool-call-parser",
+            "mistral",
+            "--enable-auto-tool-choice",
+            "--enforce-eager",
+            "--no-enable-prefix-caching",
             '--ignore-patterns="consolidated.safetensors"',
         ],
         "system_prompt": "You are a helpful assistant with access to tools. If a tool"
@@ -29,4 +25,22 @@ CONFIGS: dict[str, ServerConfig] = {
         "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
         "to the user's question - just respond to it normally.",
     },
+    "ministral-3b": {
+        "model": "mistralai/Ministral-3-3B-Instruct-2512",
+        "arguments": [
+            "--tokenizer-mode",
+            "mistral",
+            "--tool-call-parser",
+            "mistral",
+            "--enable-auto-tool-choice",
+            "--enforce-eager",
+            "--no-enable-prefix-caching",
+        ],
+        "system_prompt": "You are a helpful assistant with access to tools. If a tool"
+        " that you have would be helpful to answer a user query, "
+        "call the tool. Otherwise, answer the user's query directly "
+        "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
+        "to the user's question - just respond to it normally.",
+        "supports_parallel": True,
+    },
 }

tests/tool_use/test_chat_completion_request_validations.py

@@ -26,15 +26,15 @@ def test_chat_completion_request_with_no_tools():
     )
     assert request.tool_choice == "none"
 
-    # tools key present but empty
-    request = ChatCompletionRequest.model_validate(
-        {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "model": "facebook/opt-125m",
-            "tools": [],
-        }
-    )
-    assert request.tool_choice == "none"
+    # tools key present but empty -- should be rejected
+    with pytest.raises(ValueError, match="must not be an empty array"):
+        ChatCompletionRequest.model_validate(
+            {
+                "messages": [{"role": "user", "content": "Hello"}],
+                "model": "facebook/opt-125m",
+                "tools": [],
+            }
+        )
 
 
 @pytest.mark.parametrize("tool_choice", ["auto", "required"])

tests/utils.py

@@ -1261,9 +1261,6 @@ def fork_new_process_for_each_test(func: Callable[_P, None]) -> Callable[_P, Non
 
     @functools.wraps(func)
     def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
-        # Make the process the leader of its own process group
-        # to avoid sending SIGTERM to the parent process
-        os.setpgrp()
         from _pytest.outcomes import Skipped
 
         # Create a unique temporary file to store exception info from child
@@ -1283,6 +1280,9 @@ def fork_new_process_for_each_test(func: Callable[_P, None]) -> Callable[_P, Non
             pid = os.fork()
             print(f"Fork a new process to run a test {pid}")
             if pid == 0:
+                # Make the child process the leader of its own process group
+                # to avoid sending SIGTERM to the parent process
+                os.setpgrp()
                 # Parent process responsible for deleting, don't delete
                 # in child.
                 delete_after.pop_all()
@@ -1322,14 +1322,12 @@ def fork_new_process_for_each_test(func: Callable[_P, None]) -> Callable[_P, Non
                 else:
                     os._exit(0)
             else:
-                pgid = os.getpgid(pid)
+                # After setpgrp(), the child's pgid equals its pid
+                pgid = pid
                 _pid, _exitcode = os.waitpid(pid, 0)
-                # ignore SIGTERM signal itself
-                old_signal_handler = signal.signal(signal.SIGTERM, signal.SIG_IGN)
-                # kill all child processes
-                os.killpg(pgid, signal.SIGTERM)
-                # restore the signal handler
-                signal.signal(signal.SIGTERM, old_signal_handler)
+                # kill all child processes - but they may already have exited cleanly
+                with contextlib.suppress(ProcessLookupError):
+                    os.killpg(pgid, signal.SIGTERM)
                 if _exitcode != 0:
                     # Try to read the exception from the child process
                     exc_info = {}
@@ -1858,6 +1856,7 @@ class TestFP8Layer(torch.nn.Module):
             out_dtype=out_dtype,
             force_kernel=force_kernel,
         )
+        self.kernel.process_weights_after_loading(self)
 
     def is_quant_fp8_enabled(self) -> bool:
         return self.kernel.quant_fp8.enabled()

tests/v1/determinism/test_batch_invariance.py

@@ -36,10 +36,10 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
     using the high-level v1 LLM() API only (no manual batching).
 
     Strategy:
-    - Create two LLM engines with identical config except max_num_seqs: 1 vs N.
-    - Compute a baseline output for the needle prompt with the bs=1 engine.
-    - For many trials, generate a batch (size N) where the needle appears at a
-      random position among random filler prompts using the bs=N engine.
+    - Create a single LLM engine configured for the larger batch limit (N).
+    - Compute a baseline output for the needle prompt when it is run alone.
+    - For many trials, generate a mixed batch (size N) where the needle appears
+      at a random position among random filler prompts using the same engine.
     - Track how many trials match vs mismatch, and report totals at the end.
       The test fails if any mismatches occur, but we still dump pass/fail
       counts.
@@ -83,11 +83,9 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
 
     needle_prompt = "There once was a "
 
-    llm_bs1 = None
-    llm_bsN = None
+    llm = None
     try:
-        # Engine with bs=1 behavior
-        llm_bs1 = LLM_with_max_seqs(
+        llm = LLM_with_max_seqs(
             model=model,
             max_num_seqs=max_batch_size,
             gpu_memory_utilization=gpu_mem_util,
@@ -96,20 +94,11 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
         )
 
         # Baseline generation for the needle prompt alone.
-        baseline_out = llm_bs1.generate([needle_prompt], sampling)
+        baseline_out = llm.generate([needle_prompt], sampling)
         assert len(baseline_out) == 1
         assert len(baseline_out[0].outputs) >= 1
         baseline_text = baseline_out[0].outputs[0].text
 
-        # Engine with larger batch limit (e.g., 64)
-        llm_bsN = LLM_with_max_seqs(
-            model=model,
-            max_num_seqs=max_batch_size,
-            gpu_memory_utilization=gpu_mem_util,
-            max_model_len=max_model_len,
-            attention_config=attention_config,
-        )
-
         mismatches = 0
 
         for trial in range(num_trials):
@@ -124,8 +113,8 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
                 else:
                     prompts.append(_random_prompt(min_random_prompt, max_random_prompt))
 
-            # Generate with the larger-batch engine
-            outputs = llm_bsN.generate(prompts, sampling)
+            # Generate with the same engine but in a larger batch.
+            outputs = llm.generate(prompts, sampling)
             # Find the needle output by position
             needle_output = outputs[needle_pos]
             assert needle_output.prompt == needle_prompt
@@ -151,12 +140,9 @@ def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(
 
     finally:
         # Ensure engines are shutdown to free GPU/VRAM across test sessions
-        if llm_bs1 is not None:
+        if llm is not None:
             with contextlib.suppress(Exception):
-                llm_bs1.shutdown()
-        if llm_bsN is not None:
-            with contextlib.suppress(Exception):
-                llm_bsN.shutdown()
+                llm.shutdown()
 
 
 @skip_unsupported

tests/v1/e2e/spec_decode/test_spec_decode.py

@@ -557,12 +557,16 @@ def test_eagle_correctness_light(
             "auto",
             0.8,
         ),
-        (
+        pytest.param(
             ("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1),
             False,
             False,
             "transformers",
             0.8,
+            # TODO(hmellor): figure out why memory usage is so high
+            marks=pytest.mark.skip(
+                reason="Feature is experimental and uses too much memory in CI",
+            ),
         ),
         pytest.param(
             (

tests/v1/executor/test_ray_utils.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import numpy as np
+
+from vllm.v1.executor.ray_utils import detach_zero_copy_from_model_runner_output
+from vllm.v1.outputs import LogprobsLists, LogprobsTensors, ModelRunnerOutput
+
+
+def _make_readonly(arr: np.ndarray) -> np.ndarray:
+    arr.setflags(write=False)
+    return arr
+
+
+def test_detach_zero_copy_from_model_runner_output_copies_only_numpy_views():
+    cu_num_generated_tokens = [0, 2]
+    prompt_logprobs = LogprobsTensors.empty_cpu(1, 2)
+    output = ModelRunnerOutput(
+        req_ids=["req-0"],
+        req_id_to_index={"req-0": 0},
+        logprobs=LogprobsLists(
+            logprob_token_ids=_make_readonly(
+                np.array([[1, 2], [3, 4]], dtype=np.int32)
+            ),
+            logprobs=_make_readonly(
+                np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
+            ),
+            sampled_token_ranks=_make_readonly(np.array([1, 2], dtype=np.int32)),
+            cu_num_generated_tokens=cu_num_generated_tokens,
+        ),
+        prompt_logprobs_dict={"req-0": prompt_logprobs},
+    )
+
+    original_logprobs = output.logprobs
+    assert original_logprobs is not None
+
+    detach_zero_copy_from_model_runner_output(output)
+
+    detached_logprobs = output.logprobs
+    assert detached_logprobs is not None
+    assert detached_logprobs is not original_logprobs
+    assert (
+        detached_logprobs.logprob_token_ids is not original_logprobs.logprob_token_ids
+    )
+    assert detached_logprobs.logprobs is not original_logprobs.logprobs
+    assert (
+        detached_logprobs.sampled_token_ranks
+        is not original_logprobs.sampled_token_ranks
+    )
+    assert detached_logprobs.logprob_token_ids.flags.writeable
+    assert detached_logprobs.logprobs.flags.writeable
+    assert detached_logprobs.sampled_token_ranks.flags.writeable
+    assert detached_logprobs.cu_num_generated_tokens is cu_num_generated_tokens
+    assert output.prompt_logprobs_dict["req-0"] is prompt_logprobs

tests/v1/kv_connector/nixl_integration/toy_proxy_server.py

@@ -173,6 +173,9 @@ async def send_request_to_service(
         req_data["max_completion_tokens"] = 1
     if "stream_options" in req_data:
         del req_data["stream_options"]
+    # These args are not supported for P
+    min_tokens = req_data.pop("min_tokens", None)
+    min_completion_tokens = req_data.pop("min_completion_tokens", None)
     headers = {
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
         "X-Request-Id": request_id,
@@ -187,6 +190,10 @@ async def send_request_to_service(
     # otherwise, it would http.ReadError
     await response.aread()
 
+    # Add back the min_tokens and min_completion_tokens so D can use them
+    req_data["min_tokens"] = min_tokens
+    req_data["min_completion_tokens"] = min_completion_tokens
+
     return response
 
 

tests/v1/kv_connector/unit/test_mooncake_connector.py

@@ -609,6 +609,51 @@ def test_register_kv_caches():
                 assert bl == tensor1[0].nbytes // tensor1.shape[1]
 
 
+def test_register_kv_caches_supports_mixed_mla_and_eagle_shapes():
+    """Mixed MLA+Eagle caches should register by byte length, not shape."""
+
+    vllm_config = create_vllm_config(
+        kv_connector="MooncakeConnector", kv_role="kv_consumer"
+    )
+
+    with (
+        set_current_vllm_config(vllm_config),
+        patch_worker_dependencies(),
+        patch(
+            "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector.threading.Event"
+        ),
+        patch(
+            "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector.threading.Thread"
+        ) as mock_thread,
+    ):
+        connector = MooncakeConnector(vllm_config, KVConnectorRole.WORKER)
+        worker = connector.connector_worker
+        mock_thread.return_value.is_alive.return_value = False
+
+        worker.use_mla = True
+        worker.kv_topo.is_mla = True
+
+        # MLA cache tensor: shape[-2] is the block size.
+        mla_cache = torch.zeros((2, 16, 96), dtype=torch.float16)
+        # Eagle3/GQA-like cache tensor: shape[-2] is num_kv_heads, not block size.
+        eagle_cache = torch.zeros((2, 16, 8, 64), dtype=torch.float16)
+        kv_caches = {"mla_layer": mla_cache, "eagle_layer": eagle_cache}
+
+        with patch.object(
+            worker.engine, "batch_register_memory", return_value=0
+        ) as mock_batch_register:
+            connector.register_kv_caches(kv_caches)
+
+        mock_batch_register.assert_called_once()
+        registered_ptrs, registered_lens = mock_batch_register.call_args[0]
+        assert registered_ptrs == [mla_cache.data_ptr(), eagle_cache.data_ptr()]
+        assert registered_lens == [mla_cache.nbytes, eagle_cache.nbytes]
+        assert worker.block_len_per_layer == [
+            mla_cache.nbytes // mla_cache.shape[0],
+            eagle_cache.nbytes // eagle_cache.shape[0],
+        ]
+
+
 @pytest.mark.asyncio
 @patch(
     "vllm.distributed.kv_transfer.kv_connector.v1.mooncake."

tests/v1/kv_connector/unit/test_nixl_connector_hma.py

@@ -89,6 +89,99 @@ def test_logical_to_kernel_block_ids_with_hma():
     )
 
 
+@pytest.mark.cpu_test
+@pytest.mark.parametrize(
+    "has_mamba,swa_enabled,mamba_enabled,remote_ratio,"
+    "remote_block_ids,expected_remote_block_ids",
+    [
+        # Non-mamba (FA+SWA): both groups expanded via _logical_to_kernel_block_ids.
+        # Regression for https://github.com/vllm-project/vllm/pull/39724
+        (
+            False,
+            True,
+            False,
+            1,
+            ([0, 1, 2], [3, 4]),
+            [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9]],
+        ),
+        # Mamba (FA+Mamba): FA expanded via _logical_to_remote_kernel_block_ids,
+        # Mamba passed through unchanged.
+        # remote_ratio=261 (Nemotron 30B TP=1) != local_ratio=2 so that using
+        # the wrong conversion method produces different FA results.
+        (
+            True,
+            False,
+            True,
+            261,
+            ([0, 1, 2], [10, 11]),
+            [[0, 1, 261, 262, 522, 523], [10, 11]],
+        ),
+    ],
+    ids=["non_mamba_fa_swa", "mamba_fa_ssm"],
+)
+def test_read_blocks_for_req_expands_remote_ids(
+    has_mamba,
+    swa_enabled,
+    mamba_enabled,
+    remote_ratio,
+    remote_block_ids,
+    expected_remote_block_ids,
+):
+    """_read_blocks_for_req must expand remote logical block IDs to kernel
+    block IDs when kernel block size != logical block size.
+
+    Non-mamba path uses _logical_to_kernel_block_ids (all groups expanded).
+    Mamba path uses _logical_to_remote_kernel_block_ids (FA expanded, Mamba
+    passed through).
+    """
+    from unittest.mock import MagicMock
+
+    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.metadata import (
+        NixlConnectorMetadata,
+    )
+    from vllm.distributed.kv_transfer.kv_connector.v1.nixl.worker import (
+        NixlConnectorWorker,
+    )
+
+    worker = object.__new__(NixlConnectorWorker)
+    worker._has_mamba = has_mamba
+    worker._physical_blocks_per_logical_kv_block = 2
+    worker.kv_cache_config = make_kv_cache_config(
+        block_size=16, swa_enabled=swa_enabled, mamba_enabled=mamba_enabled
+    )
+
+    remote_engine_id = "remote-engine"
+    if has_mamba:
+        worker._mamba_phys_ratio = {remote_engine_id: remote_ratio}
+
+    # Mock kv_topo: empty remote ranks skips the transfer machinery entirely,
+    # isolating the block-ID expansion logic.
+    worker.kv_topo = MagicMock()
+    worker.kv_topo.get_target_remote_ranks_from_engine_id.return_value = []
+    worker.kv_topo.tp_ratio_from_engine_id.return_value = 1
+
+    metadata = NixlConnectorMetadata()
+    metadata.add_new_req_to_recv(
+        request_id="test-req",
+        local_block_ids=([0, 1], [2, 3]),
+        kv_transfer_params={
+            "remote_block_ids": remote_block_ids,
+            "remote_engine_id": remote_engine_id,
+            "remote_request_id": "prefill-test-req",
+            "remote_host": "localhost",
+            "remote_port": 1234,
+            "tp_size": 1,
+        },
+    )
+
+    meta = metadata.reqs_to_recv["test-req"]
+    worker._read_blocks_for_req("test-req", meta)
+
+    assert meta.remote.block_ids == expected_remote_block_ids, (
+        f"Expected {expected_remote_block_ids}, got {meta.remote.block_ids}"
+    )
+
+
 @pytest.mark.parametrize("model_name, sw_size", [("google/gemma-3-1b-it", 512)])
 def test_fewer_blocks_with_hma(monkeypatch, model_name, sw_size):
     """Test that a prefill instance returns fewer "remote blocks" for the SWA groups
@@ -102,7 +195,7 @@ def test_fewer_blocks_with_hma(monkeypatch, model_name, sw_size):
     llm_kwargs = {
         "model": model_name,
         "enforce_eager": True,
-        "gpu_memory_utilization": 0.5,
+        "gpu_memory_utilization": 0.47,
         "kv_transfer_config": kv_transfer_config,
         "max_model_len": 2048,
         # NOTE: Make sure HMA is enabled

tests/v1/spec_decode/test_speculators_dflash.py

@@ -0,0 +1,171 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+from tests.evals.gsm8k.gsm8k_eval import evaluate_gsm8k_offline
+from tests.utils import large_gpu_mark
+from vllm import LLM
+from vllm.config import SpeculativeConfig
+from vllm.distributed import cleanup_dist_env_and_memory
+
+MODEL_PATH = "nm-testing/dflash-qwen3-8b-speculators"
+
+EXPECTED_GSM8K_ACCURACY = 0.885
+ACCURACY_RTOL = 0.03
+EXPECTED_ACCEPTANCE_LEN = 3.45
+ACCEPTANCE_LEN_RTOL = 0.15
+
+# Expected per-position acceptance rates (accepted_at_pos / num_drafts)
+# Based on GSM8K evaluation with Qwen3-8B dflash speculators.
+EXPECTED_PER_POS_ACCEPTANCE_RATES = [0.795, 0.611, 0.429, 0.282]
+PER_POS_RTOL = 0.15
+
+
+def compute_spec_decode_stats(
+    metrics,
+) -> dict:
+    """Extract all spec-decode metrics and compute derived stats."""
+    name2metric = {m.name: m for m in metrics}
+
+    n_drafts = name2metric["vllm:spec_decode_num_drafts"].value
+    n_draft_tokens = name2metric["vllm:spec_decode_num_draft_tokens"].value
+    n_accepted = name2metric["vllm:spec_decode_num_accepted_tokens"].value
+
+    per_pos_vec = name2metric["vllm:spec_decode_num_accepted_tokens_per_pos"].values
+
+    acceptance_len = 1 + (n_accepted / n_drafts) if n_drafts > 0 else 1.0
+    draft_tokens_per_step = (n_draft_tokens / n_drafts) if n_drafts > 0 else 0
+    overall_acceptance_rate = (n_accepted / n_draft_tokens) if n_draft_tokens > 0 else 0
+    per_pos_rates = [v / n_drafts for v in per_pos_vec] if n_drafts > 0 else []
+
+    return {
+        "num_drafts": n_drafts,
+        "num_draft_tokens": n_draft_tokens,
+        "num_accepted_tokens": n_accepted,
+        "acceptance_len": acceptance_len,
+        "draft_tokens_per_step": draft_tokens_per_step,
+        "overall_acceptance_rate": overall_acceptance_rate,
+        "per_pos_accepted": list(per_pos_vec),
+        "per_pos_acceptance_rates": per_pos_rates,
+    }
+
+
+def print_spec_decode_stats(stats: dict) -> None:
+    """Print all spec-decode metrics and derived values."""
+    print("\n===== Spec Decode Metrics =====")
+    print(f"  num_drafts:              {stats['num_drafts']}")
+    print(f"  num_draft_tokens:        {stats['num_draft_tokens']}")
+    print(f"  num_accepted_tokens:     {stats['num_accepted_tokens']}")
+    print(f"  draft_tokens_per_step:   {stats['draft_tokens_per_step']:.2f}")
+    print(f"  overall_acceptance_rate: {stats['overall_acceptance_rate']:.4f}")
+    print(f"  acceptance_len (1+acc/drafts): {stats['acceptance_len']:.4f}")
+    print("  per-position accepted tokens:", stats["per_pos_accepted"])
+    print("  per-position acceptance rates:")
+    for i, rate in enumerate(stats["per_pos_acceptance_rates"]):
+        print(f"    pos {i}: {rate:.4f}")
+    print("===============================\n")
+
+
+def test_dflash_speculators_model(vllm_runner, example_prompts, monkeypatch):
+    """
+    Test DFlash speculators model properly initializes speculative decoding.
+
+    Verifies:
+    1. Speculative config is automatically initialized from speculators config
+    2. Method is detected as 'dflash'
+    3. The draft model path is correctly set
+    4. Speculative tokens count is valid (num_speculative_tokens=8)
+    5. Text generation works with speculative decoding enabled
+    """
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+    with vllm_runner(
+        MODEL_PATH,
+        dtype=torch.bfloat16,
+        enforce_eager=True,
+        quantization="fp8",
+    ) as vllm_model:
+        vllm_config = vllm_model.llm.llm_engine.vllm_config
+
+        assert isinstance(vllm_config.speculative_config, SpeculativeConfig), (
+            "Speculative config should be initialized for speculators model"
+        )
+
+        spec_config = vllm_config.speculative_config
+        assert spec_config.method == "dflash", (
+            f"Expected method='dflash', got '{spec_config.method}'"
+        )
+        assert spec_config.num_speculative_tokens > 0, (
+            f"Expected positive speculative tokens, "
+            f"got {spec_config.num_speculative_tokens}"
+        )
+        assert spec_config.model == MODEL_PATH, (
+            f"Draft model should be {MODEL_PATH}, got {spec_config.model}"
+        )
+
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens=20)
+        assert vllm_outputs, f"No outputs generated for speculators model {MODEL_PATH}"
+
+
+@pytest.mark.slow_test
+@large_gpu_mark(min_gb=40)
+def test_dflash_speculators_correctness(monkeypatch):
+    """
+    E2E correctness test for DFlash via the speculators auto-detect path.
+
+    Evaluates GSM8k accuracy to ensure the speculators-format model produces
+    correct outputs, and checks that acceptance length does not collapse under
+    batched inference (lm-eval style).
+
+    Observed per-position acceptance rates on GSM8K (1319 prompts):
+        pos 0: 0.795, pos 1: 0.611, pos 2: 0.429, pos 3: 0.282,
+        pos 4: 0.169, pos 5: 0.093, pos 6: 0.048, pos 7: 0.023
+    Observed mean AL: 3.45 (GSM8K dataset, max_num_seqs=128)
+    """
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
+
+    spec_llm = LLM(
+        model=MODEL_PATH,
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=128,
+        gpu_memory_utilization=0.85,
+        enforce_eager=False,
+        disable_log_stats=False,
+    )
+
+    results = evaluate_gsm8k_offline(spec_llm)
+    accuracy = results["accuracy"]
+    accuracy_threshold = EXPECTED_GSM8K_ACCURACY * (1 - ACCURACY_RTOL)
+    assert accuracy >= accuracy_threshold, (
+        f"Expected GSM8K accuracy >= {accuracy_threshold:.3f}, got {accuracy:.3f}"
+    )
+
+    current_metrics = spec_llm.get_metrics()
+    stats = compute_spec_decode_stats(current_metrics)
+    print_spec_decode_stats(stats)
+
+    acceptance_len = stats["acceptance_len"]
+    al_threshold = EXPECTED_ACCEPTANCE_LEN * (1 - ACCEPTANCE_LEN_RTOL)
+    assert acceptance_len >= al_threshold, (
+        f"DFlash speculators acceptance length too low: "
+        f"{acceptance_len:.2f} < {al_threshold:.2f}"
+    )
+
+    # Check per-position acceptance rates for the first few positions.
+    per_pos_rates = stats["per_pos_acceptance_rates"]
+    for i, expected_rate in enumerate(EXPECTED_PER_POS_ACCEPTANCE_RATES):
+        assert i < len(per_pos_rates), (
+            f"Missing per-position acceptance rate for position {i}"
+        )
+        threshold = expected_rate * (1 - PER_POS_RTOL)
+        assert per_pos_rates[i] >= threshold, (
+            f"Per-position acceptance rate at pos {i} too low: "
+            f"{per_pos_rates[i]:.4f} < {threshold:.4f} "
+            f"(expected ~{expected_rate:.4f})"
+        )
+
+    del spec_llm
+    torch.accelerator.empty_cache()
+    cleanup_dist_env_and_memory()

vllm/_aiter_ops.py

@@ -8,12 +8,18 @@ from torch._ops import OpOverload
 
 import vllm.envs as envs
 from vllm.platforms import current_platform
+from vllm.utils.import_utils import PlaceholderModule
 from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
     rocm_aiter_sparse_attn_indexer,
     rocm_aiter_sparse_attn_indexer_fake,
 )
 
+try:
+    import pandas as pd
+except ImportError:
+    pd = PlaceholderModule("pandas")
+
 # fp8_dtype is not cached.
 # on ROCm the fp8_dtype always calls is_fp8_fnuz
 # which is a host op, so we cache it once here.
@@ -56,6 +62,29 @@ def is_aiter_found_and_supported() -> bool:
     return False
 
 
+@functools.cache
+def _load_gemm_tuned_configs(
+    q_dtype_w: torch.dtype, csv_path: str
+) -> set[tuple[int, int, int]]:
+    try:
+        df = pd.read_csv(csv_path).drop_duplicates()
+        df = df[df["q_dtype_w"] == str(q_dtype_w)]
+        return set(zip(df["N"].astype(int), df["K"].astype(int), df["M"].astype(int)))
+    except Exception:
+        return set()
+
+
+def _check_kernel_tuned(N: int, K: int, q_dtype_w: torch.dtype, csv_path: str) -> bool:
+    configs = _load_gemm_tuned_configs(q_dtype_w, csv_path)
+    l_m = (
+        [1, 2, 4]
+        + list(range(8, 513, 8))
+        + [1024, 1536]
+        + [2**i for i in range(11, 19)]
+    )
+    return any((N, K, M) in configs for M in l_m)
+
+
 def if_aiter_supported(func: Callable) -> Callable:
     """Decorator that only executes the function if
     ROCm AITER package is supported and enabled on gfx9 archs.
@@ -468,7 +497,7 @@ def _rocm_aiter_mla_decode_fwd_fake(
     pass
 
 
-def _rocm_aiter_gemm_a8w8_impl(
+def _rocm_aiter_w8a8_gemm_impl(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -485,7 +514,7 @@ def _rocm_aiter_gemm_a8w8_impl(
     return gemm_a8w8_CK(A, B, As, Bs, bias, output_dtype)
 
 
-def _rocm_aiter_gemm_a8w8_fake(
+def _rocm_aiter_w8a8_gemm_fake(
     A: torch.Tensor,
     B: torch.Tensor,
     As: torch.Tensor,
@@ -499,6 +528,35 @@ def _rocm_aiter_gemm_a8w8_fake(
     return Y
 
 
+def _rocm_aiter_preshuffled_per_token_w8a8_gemm_impl(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    from aiter import gemm_a8w8_bpreshuffle
+
+    output = gemm_a8w8_bpreshuffle(A, B, As, Bs, None, output_dtype)
+    if bias is not None:
+        output.add_(bias)
+    return output
+
+
+def _rocm_aiter_preshuffled_per_token_w8a8_gemm_fake(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    bias: torch.Tensor | None = None,
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    m = A.shape[0]
+    n = B.shape[0]
+    return torch.empty(m, n, dtype=output_dtype, device=A.device)
+
+
 def _rocm_aiter_triton_gemm_a8w8_blockscale_impl(
     A: torch.Tensor,
     B: torch.Tensor,
@@ -1313,11 +1371,15 @@ class rocm_aiter_ops:
             )
 
             direct_register_custom_op(
-                op_name="rocm_aiter_gemm_a8w8",
-                op_func=_rocm_aiter_gemm_a8w8_impl,
-                mutates_args=[],
-                fake_impl=_rocm_aiter_gemm_a8w8_fake,
-                dispatch_key=current_platform.dispatch_key,
+                op_name="rocm_aiter_w8a8_gemm",
+                op_func=_rocm_aiter_w8a8_gemm_impl,
+                fake_impl=_rocm_aiter_w8a8_gemm_fake,
+            )
+
+            direct_register_custom_op(
+                op_name="_rocm_aiter_preshuffled_per_token_w8a8_gemm",
+                op_func=_rocm_aiter_preshuffled_per_token_w8a8_gemm_impl,
+                fake_impl=_rocm_aiter_preshuffled_per_token_w8a8_gemm_fake,
             )
 
             direct_register_custom_op(
@@ -1493,7 +1555,7 @@ class rocm_aiter_ops:
         )
 
     @staticmethod
-    def gemm_a8w8(
+    def w8a8_gemm(
         A: torch.Tensor,
         B: torch.Tensor,
         As: torch.Tensor,
@@ -1501,7 +1563,20 @@ class rocm_aiter_ops:
         bias: torch.Tensor | None = None,
         output_dtype: torch.dtype = torch.float16,
     ) -> torch.Tensor:
-        return torch.ops.vllm.rocm_aiter_gemm_a8w8(A, B, As, Bs, bias, output_dtype)
+        return torch.ops.vllm.rocm_aiter_w8a8_gemm(A, B, As, Bs, bias, output_dtype)
+
+    @staticmethod
+    def preshuffled_per_token_w8a8_gemm(
+        A: torch.Tensor,
+        B: torch.Tensor,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        output_dtype: torch.dtype = torch.float16,
+    ) -> torch.Tensor:
+        return torch.ops.vllm._rocm_aiter_preshuffled_per_token_w8a8_gemm(
+            A, B, As, Bs, bias, output_dtype
+        )
 
     @staticmethod
     def triton_gemm_a8w8_blockscale(
@@ -1920,6 +1995,24 @@ class rocm_aiter_ops:
             (8192, 3584),
         ]
 
+    @staticmethod
+    def is_shuffled_per_token_w8a8_gemm_tuned(
+        N: int, K: int, q_dtype_w: torch.dtype
+    ) -> bool:
+        import aiter.ops.gemm_op_a8w8 as aiter_gemm_a8w8_ops
+
+        csv_path = (
+            aiter_gemm_a8w8_ops.AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE_FILE
+        )
+        return _check_kernel_tuned(N, K, q_dtype_w, csv_path)
+
+    @staticmethod
+    def is_per_token_w8a8_gemm_tuned(N: int, K: int, q_dtype_w: torch.dtype) -> bool:
+        import aiter.ops.gemm_op_a8w8 as aiter_gemm_a8w8_ops
+
+        csv_path = aiter_gemm_a8w8_ops.AITER_CONFIGS.AITER_CONFIG_GEMM_A8W8_FILE
+        return _check_kernel_tuned(N, K, q_dtype_w, csv_path)
+
     @staticmethod
     def shuffle_weight(
         tensor: torch.Tensor, layout: tuple[int, int] = (16, 16)

vllm/_custom_ops.py

@@ -3300,6 +3300,12 @@ def cpu_gemm_wna16(
     return output
 
 
+def cpu_activation_lut_bf16(input: torch.Tensor, activation: str) -> torch.Tensor:
+    out = torch.empty_like(input)
+    torch.ops._C.activation_lut_bf16(out, input, activation)
+    return out
+
+
 def cpu_prepack_moe_weight(
     weight: torch.Tensor,
     isa: str,

vllm/benchmarks/datasets/datasets.py

@@ -25,6 +25,7 @@ from contextlib import suppress
 from dataclasses import dataclass, replace
 from functools import cache
 from io import BytesIO
+from pathlib import Path
 from tempfile import NamedTemporaryFile
 from typing import Any, cast
 
@@ -1422,6 +1423,7 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
             "custom_mm",
             "prefix_repetition",
             "spec_bench",
+            "speed_bench",
         ],
         help="Name of the dataset to benchmark on.",
     )
@@ -1606,6 +1608,34 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         "repetition dataset.",
     )
 
+    speed_bench_group = parser.add_argument_group("speed bench dataset options")
+    speed_bench_group.add_argument(
+        "--speed-bench-dataset-subset",
+        type=str,
+        default="qualitative",
+        choices={
+            "qualitative",
+            "throughput_1k",
+            "throughput_2k",
+            "throughput_8k",
+            "throughput_16k",
+            "throughput_32k",
+        },
+        help="Subset of the SPEED-Bench dataset.",
+    )
+    speed_bench_group.add_argument(
+        "--speed-bench-output-len",
+        type=int,
+        default=4096,
+        help="Num of output tokens per request, used only for speed bench dataset.",
+    )
+    speed_bench_group.add_argument(
+        "--speed-bench-category",
+        type=str,
+        default=None,
+        help="Category for speed bench dataset. If None, use all categories.",
+    )
+
 
 def add_random_dataset_base_args(
     parser_or_group: FlexibleArgumentParser | argparse._ArgumentGroup,
@@ -2074,6 +2104,19 @@ def get_samples(args, tokenizer: TokenizerLike) -> list[SampleRequest]:
                 request_id_prefix=args.request_id_prefix,
                 no_oversample=args.no_oversample,
             ),
+            "speed_bench": lambda: SpeedBench(
+                dataset_path=args.dataset_path,
+                dataset_subset=args.speed_bench_dataset_subset,
+                category=args.speed_bench_category,
+                disable_shuffle=args.disable_shuffle,
+            ).sample(
+                num_requests=args.num_prompts,
+                tokenizer=tokenizer,
+                output_len=args.speed_bench_output_len,
+                enable_multimodal_chat=args.enable_multimodal_chat,
+                request_id_prefix=args.request_id_prefix,
+                no_oversample=args.no_oversample,
+            ),
         }
 
         try:
@@ -3551,3 +3594,48 @@ class MMStarDataset(HuggingFaceDataset):
             sampled_requests, num_requests, request_id_prefix, no_oversample
         )
         return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# Speed Bench Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class SpeedBench(CustomDataset):
+    """
+    Implements the SPEED-Bench dataset: https://huggingface.co/datasets/nvidia/SPEED-Bench
+    Download the dataset using:
+    curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py | python3 -
+    """  # noqa: E501
+
+    def __init__(self, **kwargs) -> None:
+        self.dataset_subset = kwargs.pop("dataset_subset", "qualitative")
+        self.category = kwargs.pop("category", None)
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        self.data = []
+
+        # Load the JSONL file
+        jsonl_data = pd.read_json(
+            path_or_buf=Path(self.dataset_path) / f"{self.dataset_subset}.jsonl",
+            lines=True,
+        )
+
+        # check if the JSONL file has a 'turns' column
+        if "messages" not in jsonl_data.columns:
+            raise ValueError("JSONL file must contain a 'messages' column.")
+
+        for _, row in jsonl_data.iterrows():
+            # sample only from a specific category if specified
+            if (not self.category) or (self.category == row["category"]):
+                prompt = row["messages"][0]["content"]
+                self.data.append({"prompt": prompt})
+
+        random.seed(self.random_seed)
+        if not getattr(self, "disable_shuffle", False):
+            random.shuffle(self.data)

vllm/compilation/backends.py

@@ -1263,6 +1263,23 @@ class VllmBackend:
             original_split_gm if envs.VLLM_USE_MEGA_AOT_ARTIFACT else self.graph
         )
 
+        from vllm.compilation.codegen import (
+            compile_execution_fn,
+            generate_execution_code,
+        )
+
+        execution_code, submod_names = generate_execution_code(self.split_gm)
+        # Use getattr to get correct callables: __dict__ has PiecewiseBackend
+        # instances (from PiecewiseCompileInterpreter), _modules has originals.
+        # getattr checks __dict__ first, then falls back to _modules.
+        submod_callables = {
+            name: getattr(self.split_gm, name)
+            for name, _ in self.split_gm.named_children()
+        }
+        runtime_callable = compile_execution_fn(
+            execution_code, submod_callables, submod_names
+        )
+
         if (
             self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE
             or not self.compilation_config.cudagraph_copy_inputs
@@ -1271,9 +1288,11 @@ class VllmBackend:
                 graph_to_serialize,
                 example_inputs,
                 self.prefix,
-                self.split_gm,
+                runtime_callable,
                 is_encoder=self.is_encoder,
                 vllm_backend=self,
+                execution_code=execution_code,
+                submod_names=submod_names,
             )
 
         # index of tensors that have symbolic shapes (batch size)
@@ -1294,7 +1313,7 @@ class VllmBackend:
         copy_and_call = make_copy_and_call(
             sym_tensor_indices,
             [example_inputs[x].clone() for x in sym_tensor_indices],
-            self.split_gm,
+            runtime_callable,
         )
 
         return VllmSerializableFunction(
@@ -1305,4 +1324,6 @@ class VllmBackend:
             is_encoder=self.is_encoder,
             vllm_backend=self,
             sym_tensor_indices=sym_tensor_indices,
+            execution_code=execution_code,
+            submod_names=submod_names,
         )

vllm/compilation/caching.py

@@ -184,6 +184,8 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
         vllm_backend: Any | None = None,
         sym_tensor_indices: list[int] | None = None,
         aot_autograd_config: dict[str, Any] | None = None,
+        execution_code: str | None = None,
+        submod_names: list[str] | None = None,
     ) -> None:
         assert isinstance(graph_module, torch.fx.GraphModule)
         self.graph_module = graph_module
@@ -194,6 +196,8 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
         self.shape_env = None
         self.vllm_backend = vllm_backend
         self.sym_tensor_indices = sym_tensor_indices
+        self.execution_code = execution_code
+        self.submod_names = submod_names
         self._fake_mode: Any | None = None
 
         import torch._functorch.config as functorch_config
@@ -453,7 +457,7 @@ def reconstruct_serializable_fn_from_mega_artifact(
 
     standalone_compile_artifacts.load_all()
 
-    submod_names = standalone_compile_artifacts.submodule_names()
+    piecewise_submod_names = standalone_compile_artifacts.submodule_names()
     compiled_callables: dict[str, dict[str, Callable[..., Any]]] = {}
 
     for cache_key in standalone_compile_artifacts.submodule_bytes:
@@ -473,13 +477,13 @@ def reconstruct_serializable_fn_from_mega_artifact(
 
     # spot check that cached submodules exist in the graph structure
     graph_children = {name for name, _ in split_gm.named_children()}
-    missing = set(submod_names) - graph_children
+    missing = set(piecewise_submod_names) - graph_children
     assert not missing, (
         f"artifacts reference submodules not in graph: {missing}. "
         f"graph has: {sorted(graph_children)}"
     )
 
-    for i, submod_name in enumerate(submod_names):
+    for i, submod_name in enumerate(piecewise_submod_names):
         assert submod_name in sym_shape_indices_map and submod_name in returns_tuple_map
 
         sym_shape_indices = sym_shape_indices_map[submod_name]
@@ -490,7 +494,7 @@ def reconstruct_serializable_fn_from_mega_artifact(
             graph=None,  # not needed for cached artifacts
             vllm_config=vllm_config,
             piecewise_compile_index=i,
-            total_piecewise_compiles=len(submod_names),
+            total_piecewise_compiles=len(piecewise_submod_names),
             sym_shape_indices=sym_shape_indices,
             vllm_backend=vllm_backend,
             returns_tuple=returns_tuple,
@@ -498,7 +502,7 @@ def reconstruct_serializable_fn_from_mega_artifact(
         )
 
         is_first = i == 0
-        is_last = i == len(submod_names) - 1
+        is_last = i == len(piecewise_submod_names) - 1
         wrapped_backend = wrap_with_cudagraph_if_needed(
             piecewise_backend,
             vllm_config,
@@ -513,6 +517,21 @@ def reconstruct_serializable_fn_from_mega_artifact(
             submod_name,
         )
 
+    # Use codegen'd execution code if available, fall back to split_gm
+    execution_code = state.get("execution_code")
+    submod_names = state.get("submod_names")
+    if execution_code is not None and submod_names is not None:
+        from vllm.compilation.codegen import compile_execution_fn
+
+        submod_callables = {
+            name: getattr(split_gm, name) for name, _ in split_gm.named_children()
+        }
+        runtime_callable = compile_execution_fn(
+            execution_code, submod_callables, submod_names
+        )
+    else:
+        runtime_callable = split_gm
+
     if compilation_config.cudagraph_copy_inputs:
         sym_tensor_indices = state["sym_tensor_indices"]
         input_buffers = [
@@ -521,9 +540,11 @@ def reconstruct_serializable_fn_from_mega_artifact(
             )
             for idx in sym_tensor_indices
         ]
-        optimized_call = make_copy_and_call(sym_tensor_indices, input_buffers, split_gm)
+        optimized_call = make_copy_and_call(
+            sym_tensor_indices, input_buffers, runtime_callable
+        )
     else:
-        optimized_call = split_gm
+        optimized_call = runtime_callable
 
     fn = VllmSerializableFunction(
         **state,

vllm/compilation/codegen.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Code generation for split_gm stitching graph execution.
+
+Generates a plain Python function that replaces the FX GraphModule's
+interpreter-based execution of the stitching graph, eliminating
+nn.Module.__call__ overhead and __getattr__ dispatch.
+"""
+
+import operator
+from collections.abc import Callable
+from functools import partial
+from typing import Any
+
+import torch.fx
+from torch._dynamo.utils import dynamo_timed
+from torch._logging import trace_structured
+
+
+@dynamo_timed("vllm.generate_execution_code")
+def generate_execution_code(
+    split_gm: torch.fx.GraphModule,
+) -> tuple[str, list[str]]:
+    """Generate Python source code from a split_gm's stitching graph.
+
+    Walks split_gm.graph.nodes and produces a function that calls
+    submodules via a __vllm_submods__ list, avoiding FX GraphModule overhead
+    and dict lookup cost.
+
+    Args:
+        split_gm: The split graph module produced by split_graph().
+
+    Returns:
+        A tuple of (code, submod_names) where code is the Python source
+        and submod_names is the ordered list of submodule target names
+        corresponding to list indices used in the generated code.
+    """
+    lines: list[str] = []
+    param_names: list[str] = []
+    submod_names: list[str] = []
+    submod_index: dict[str, int] = {}
+
+    # Build node ordering for liveness analysis.
+    nodes = list(split_gm.graph.nodes)
+    node_order = {node: i for i, node in enumerate(nodes)}
+
+    # For each value-producing node, find the position of its last consumer.
+    # If the last consumer is the output node, skip (return handles cleanup).
+    # Otherwise, schedule a del after that consumer to free memory early.
+    del_after: dict[int, list[str]] = {}  # position -> names to delete
+    for node in nodes:
+        if node.op == "output":
+            continue
+        users = list(node.users.keys())
+        if not users:
+            continue
+        last_user = max(users, key=lambda u: node_order[u])
+        if last_user.op == "output":
+            continue
+        del_after.setdefault(node_order[last_user], []).append(node.name)
+
+    for i, node in enumerate(nodes):
+        if node.op == "placeholder":
+            param_names.append(node.name)
+
+        elif node.op == "call_module":
+            target = node.target
+            if target not in submod_index:
+                submod_index[target] = len(submod_names)
+                submod_names.append(target)
+            idx = submod_index[target]
+            args_str = ", ".join(_node_ref(a) for a in node.args)
+            kwargs_str = ", ".join(
+                f"{k}={_node_ref(v)}" for k, v in node.kwargs.items()
+            )
+            all_args = ", ".join(filter(None, [args_str, kwargs_str]))
+            lines.append(f"    {node.name} = __vllm_submods__[{idx}]({all_args})")
+
+        elif node.op == "call_function" and node.target is operator.getitem:
+            source = _node_ref(node.args[0])
+            index = node.args[1]
+            assert isinstance(index, int)
+            lines.append(f"    {node.name} = {source}[{index}]")
+
+        elif node.op == "output":
+            assert len(node.args) == 1
+            ret = _node_ref(node.args[0])
+            lines.append(f"    return {ret}")
+
+        else:
+            raise RuntimeError(f"Unsupported node from codegen: {node.format_node()}")
+
+        # Emit del for variables whose last use was this node.
+        if i in del_after:
+            names = sorted(del_after[i])
+            lines.append(f"    del {', '.join(names)}")
+
+    assert len(param_names) > 0
+    params = ", ".join(param_names)
+    header = f"def execution_fn({params}, *, __vllm_submods__):"
+    return "import torch\n" + "\n".join([header] + lines) + "\n", submod_names
+
+
+@dynamo_timed("vllm.compile_execution_fn")
+def compile_execution_fn(
+    code: str,
+    submod_callables: dict[str, Callable[..., Any]],
+    submod_names: list[str],
+) -> Callable[..., Any]:
+    """Compile execution code and bind submodule callables.
+
+    Args:
+        code: Python source from generate_execution_code().
+        submod_callables: Mapping of submodule names to their callables.
+        submod_names: Ordered list of submodule names matching the indices
+            used in the generated code.
+
+    Returns:
+        A callable that executes the stitching logic.
+    """
+    trace_structured(
+        "artifact",
+        metadata_fn=lambda: {
+            "name": "vllm_execution_code",
+            "encoding": "string",
+        },
+        payload_fn=lambda: code,
+    )
+    namespace: dict[str, Any] = {}
+    exec(code, namespace)  # noqa: S102
+    fn = namespace["execution_fn"]
+    # Use .forward() directly to avoid nn.Module.__call__ overhead.
+    submods_list = [
+        c.forward if isinstance(c, torch.fx.GraphModule) else c
+        for c in (submod_callables[name] for name in submod_names)
+    ]
+    return partial(fn, __vllm_submods__=submods_list)
+
+
+def _node_ref(arg: Any) -> str:
+    """Convert an FX node argument to a source code reference recursively."""
+    if isinstance(arg, torch.fx.Node):
+        return arg.name
+    if isinstance(arg, list):
+        return f"[{', '.join(_node_ref(x) for x in arg)}]"
+    if isinstance(arg, tuple):
+        items = ", ".join(_node_ref(x) for x in arg)
+        return f"({items},)" if len(arg) == 1 else f"({items})"
+    if isinstance(arg, dict):
+        return (
+            "{"
+            + ", ".join(f"{_node_ref(k)}: {_node_ref(v)}" for k, v in arg.items())
+            + "}"
+        )
+    return repr(arg)

vllm/compilation/cuda_graph.py

@@ -290,9 +290,14 @@ class CUDAGraphWrapper:
                     # across layers will make the cudagraph capture very slow.
                     # therefore, we only run gc for the first graph,
                     # and disable gc for the rest of the graphs.
-                    stack.enter_context(patch("gc.collect", lambda: None))
                     stack.enter_context(
-                        patch("torch.accelerator.empty_cache", lambda: None)
+                        patch("gc.collect", lambda *args, **kwargs: None)
+                    )
+                    stack.enter_context(
+                        patch(
+                            "torch.accelerator.empty_cache",
+                            lambda *args, **kwargs: None,
+                        )
                     )
 
                 if self.graph_pool is not None:

vllm/config/speculative.py

@@ -300,6 +300,10 @@ class SpeculativeConfig:
                 {"n_predict": n_predict, "architectures": ["ErnieMTPModel"]}
             )
 
+        if hf_config.architectures[0] == "NemotronH_Super_Omni_Reasoning_V3":
+            # Promote VLM's text_config so MTP detection below fires correctly
+            hf_config = hf_config.text_config
+
         if (
             hf_config.model_type in {"nemotron_h", "nemotron_h_puzzle"}
             and hasattr(hf_config, "num_nextn_predict_layers")

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py

@@ -215,8 +215,11 @@ class LMCacheMPRequestTracker:
     # Main state
     state: LMCacheMPRequestState = LMCacheMPRequestState.PREFETCHING
 
+    cache_salt: str = ""
+
     def __init__(self, request: "Request"):
         self.request_id = request.request_id
+        self.cache_salt: str = request.cache_salt or ""
         self.all_token_ids = request.all_token_ids
         self.block_hashes = ConstantList(request.block_hashes)
         self.allocated_block_ids = []
@@ -289,6 +292,7 @@ class LMCacheMPRequestMetadata:
     request_id: str
     direction: Literal["STORE", "RETRIEVE"]
     op: LoadStoreOp
+    cache_salt: str = ""
 
     @staticmethod
     def GetStoreMetadata(
@@ -355,6 +359,7 @@ class LMCacheMPRequestMetadata:
                 request_id=tracker.request_id,
                 direction="STORE",
                 op=op,
+                cache_salt=tracker.cache_salt,
             )
 
             # Update the request tracker
@@ -421,6 +426,7 @@ class LMCacheMPRequestMetadata:
                 request_id=tracker.request_id,
                 direction="RETRIEVE",
                 op=op,
+                cache_salt=tracker.cache_salt,
             )
             return ret
 
@@ -569,12 +575,14 @@ class LMCacheMPConnector(KVConnectorBase_V1):
 
         request_ids = []
         ops = []
+        cache_salts = []
 
         for meta in metadata.requests:
             if meta.direction != "RETRIEVE":
                 continue
             request_ids.append(meta.request_id)
             ops.append(meta.op)
+            cache_salts.append(meta.cache_salt)
 
         if len(request_ids) == 0:
             return
@@ -583,7 +591,9 @@ class LMCacheMPConnector(KVConnectorBase_V1):
             event = torch.cuda.Event(interprocess=True)
             event.record()
 
-        self.worker_adapter.batched_submit_retrieve_requests(request_ids, ops, event)
+        self.worker_adapter.batched_submit_retrieve_requests(
+            request_ids, ops, event, cache_salts=cache_salts
+        )
 
     def wait_for_layer_load(self, layer_name: str) -> None:
         """
@@ -640,11 +650,13 @@ class LMCacheMPConnector(KVConnectorBase_V1):
 
         request_ids = []
         ops = []
+        cache_salts = []
         for meta in metadata.requests:
             if meta.direction != "STORE":
                 continue
             request_ids.append(meta.request_id)
             ops.append(meta.op)
+            cache_salts.append(meta.cache_salt)
 
         if len(request_ids) == 0:
             return
@@ -653,7 +665,9 @@ class LMCacheMPConnector(KVConnectorBase_V1):
             event = torch.cuda.Event(interprocess=True)
             event.record()
 
-        self.worker_adapter.batched_submit_store_requests(request_ids, ops, event)
+        self.worker_adapter.batched_submit_store_requests(
+            request_ids, ops, event, cache_salts=cache_salts
+        )
 
     def get_finished(
         self, finished_req_ids: set[str]
@@ -755,6 +769,7 @@ class LMCacheMPConnector(KVConnectorBase_V1):
         self.scheduler_adapter.maybe_submit_lookup_request(
             request.request_id,
             token_ids=list(request.all_token_ids),
+            cache_salt=tracker.cache_salt,
         )
 
         ret = self.scheduler_adapter.check_lookup_result(request.request_id)

vllm/distributed/kv_transfer/kv_connector/v1/mooncake/mooncake_connector.py

@@ -23,6 +23,7 @@ from vllm.distributed.kv_transfer.kv_connector.utils import (
     EngineId,
     TpKVTopology,
     get_current_attn_backend,
+    get_current_attn_backends,
 )
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
@@ -47,6 +48,7 @@ from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
+from vllm.v1.worker.utils import select_common_block_size
 
 logger = init_logger(__name__)
 
@@ -751,6 +753,7 @@ class MooncakeConnectorWorker:
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.use_mla = self.model_config.use_mla
+        self._sync_block_size_with_kernel()
 
         # Get the attention backend from the first layer
         # NOTE (NickLucche) models with multiple backends are not supported yet
@@ -777,6 +780,23 @@ class MooncakeConnectorWorker:
         self._xfer_meta_decoder = msgspec.msgpack.Decoder(MooncakeXferMetadata)
         self._xfer_resp_decoder = msgspec.msgpack.Decoder(MooncakeXferResponse)
 
+    def _sync_block_size_with_kernel(self) -> None:
+        # When speculative decoding (e.g. Eagle) is enabled, the main model
+        # and draft model may use different attention backends with different
+        # physical block sizes. Pick the common (smallest) block size so that
+        # KV-cache registration and transfer work correctly for both models.
+        backends = get_current_attn_backends(self.vllm_config)
+        kernel_block_size = select_common_block_size(self.block_size, backends)
+        if self.block_size != kernel_block_size:
+            logger.info_once(
+                "User-specified logical block size (%s) does not match"
+                " physical kernel block size (%s). Using the latter.",
+                self.block_size,
+                kernel_block_size,
+            )
+            assert self.block_size > kernel_block_size
+            self.block_size = kernel_block_size
+
     def __del__(self):
         self.shutdown()
 
@@ -1268,9 +1288,6 @@ class MooncakeConnectorWorker:
                 self.block_len_per_layer.append(
                     curr_tensor_size_bytes // self.num_blocks
                 )
-
-                kernel_block_size = cache.shape[-2 if self.use_mla else -3]
-                assert self.block_size == kernel_block_size
                 kv_data_ptrs.append(base_addr)
                 kv_data_lens.append(curr_tensor_size_bytes)
 

vllm/distributed/kv_transfer/kv_connector/v1/nixl/worker.py

@@ -1914,6 +1914,10 @@ class NixlConnectorWorker:
                 meta.remote.block_ids,
                 self._mamba_phys_ratio[meta.remote.engine_id],
             )
+        else:
+            meta.remote.block_ids = self._logical_to_kernel_block_ids(
+                meta.remote.block_ids
+            )
         # D may have to perform multiple reads from different remote ranks.
         for i, remote_rank in enumerate(remote_ranks):
             if self.use_mla and tp_ratio < 0 and i > 0:

vllm/entrypoints/llm.py

@@ -48,7 +48,7 @@ from vllm.entrypoints.chat_utils import (
     ChatTemplateContentFormatOption,
     load_chat_template,
 )
-from vllm.entrypoints.pooling.io_processor_factories import init_pooling_io_processors
+from vllm.entrypoints.pooling.factories import init_pooling_io_processors
 from vllm.entrypoints.pooling.scoring.io_processor import ScoringIOProcessor
 from vllm.entrypoints.pooling.scoring.typing import ScoreInput
 from vllm.entrypoints.pooling.typing import OfflineInputsContext, OfflineOutputsContext
@@ -1575,7 +1575,7 @@ class LLM:
         seq_prompts = prompt_to_seq(prompts)
         seq_params = self._params_to_seq(params, len(seq_prompts))
         seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_prompts))
-        seq_priority = self._priority_to_seq(priority, len(prompts))
+        seq_priority = self._priority_to_seq(priority, len(seq_prompts))
 
         return self._render_and_add_requests(
             prompts=(

vllm/entrypoints/openai/api_server.py

@@ -220,6 +220,12 @@ def build_app(
 
         elastic_ep_attach_router(app)
 
+        from vllm.entrypoints.openai.generative_scoring.api_router import (
+            register_generative_scoring_api_router,
+        )
+
+        register_generative_scoring_api_router(app)
+
     if "generate" in supported_tasks or "render" in supported_tasks:
         from vllm.entrypoints.serve.render.api_router import (
             attach_router as attach_render_router,
@@ -242,17 +248,10 @@ def build_app(
         register_realtime_api_router(app)
 
     if any(task in POOLING_TASKS for task in supported_tasks):
-        from vllm.entrypoints.pooling import register_pooling_api_routers
+        from vllm.entrypoints.pooling.factories import register_pooling_api_routers
 
         register_pooling_api_routers(app, supported_tasks, model_config)
 
-    if "generate" in supported_tasks:
-        from vllm.entrypoints.openai.generative_scoring.api_router import (
-            register_generative_scoring_api_router,
-        )
-
-        register_generative_scoring_api_router(app)
-
     app.root_path = args.root_path
     app.add_middleware(
         CORSMiddleware,
@@ -401,6 +400,12 @@ async def init_app_state(
             engine_client, state, args, request_logger, supported_tasks
         )
 
+        from vllm.entrypoints.openai.generative_scoring.api_router import (
+            init_generative_scoring_state,
+        )
+
+        await init_generative_scoring_state(engine_client, state, args, request_logger)
+
     if "transcription" in supported_tasks:
         from vllm.entrypoints.openai.speech_to_text.api_router import (
             init_transcription_state,
@@ -416,17 +421,10 @@ async def init_app_state(
         init_realtime_state(engine_client, state, args, request_logger, supported_tasks)
 
     if any(task in POOLING_TASKS for task in supported_tasks):
-        from vllm.entrypoints.pooling import init_pooling_state
+        from vllm.entrypoints.pooling.factories import init_pooling_state
 
         init_pooling_state(engine_client, state, args, request_logger, supported_tasks)
 
-    if "generate" in supported_tasks:
-        from vllm.entrypoints.openai.generative_scoring.api_router import (
-            init_generative_scoring_state,
-        )
-
-        await init_generative_scoring_state(engine_client, state, args, request_logger)
-
     state.enable_server_load_tracking = args.enable_server_load_tracking
     state.server_load_metrics = 0
 

vllm/entrypoints/openai/chat_completion/protocol.py

@@ -11,7 +11,7 @@ from openai.types.chat.chat_completion_audio import (
     ChatCompletionAudio as OpenAIChatCompletionAudio,
 )
 from openai.types.chat.chat_completion_message import Annotation as OpenAIAnnotation
-from pydantic import Field, model_validator
+from pydantic import Field, PrivateAttr, model_validator
 
 from vllm.config import ModelConfig
 from vllm.config.utils import replace
@@ -398,6 +398,9 @@ class ChatCompletionRequest(OpenAIBaseModel):
                 msg["tool_calls"] = list(tool_calls)
         return self
 
+    _grammar_from_tool_parser: bool = PrivateAttr(default=False)
+    """CAUTION: Should only be set by ``ToolParser.adjust_request``."""
+
     def build_chat_params(
         self,
         default_template: str | None,
@@ -678,6 +681,18 @@ class ChatCompletionRequest(OpenAIBaseModel):
     @model_validator(mode="before")
     @classmethod
     def check_tool_usage(cls, data):
+        if isinstance(data, ValueError):
+            raise data
+        if not isinstance(data, dict):
+            return data
+
+        # Reject empty tools array, matching OpenAI API behavior
+        if data.get("tools") == []:
+            raise ValueError(
+                "`tools` must not be an empty array. "
+                "Either provide at least one tool or omit the field entirely."
+            )
+
         # if "tool_choice" is not specified but tools are provided,
         # default to "auto" tool_choice
         if "tool_choice" not in data and data.get("tools"):
@@ -704,18 +719,6 @@ class ChatCompletionRequest(OpenAIBaseModel):
                     "are supported."
                 )
 
-            # if tool_choice is "required" but the "tools" list is empty,
-            # override the data to behave like "none" to align with
-            # OpenAI’s behavior.
-            if (
-                data["tool_choice"] == "required"
-                and isinstance(data["tools"], list)
-                and len(data["tools"]) == 0
-            ):
-                data["tool_choice"] = "none"
-                del data["tools"]
-                return data
-
             # ensure that if "tool_choice" is specified as an object,
             # it matches a valid tool
             correct_usage_message = (
@@ -822,13 +825,6 @@ class ChatCompletionRequest(OpenAIBaseModel):
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def set_include_reasoning_for_none_effort(cls, data: Any) -> Any:
-        if data.get("reasoning_effort") == "none":
-            data["include_reasoning"] = False
-        return data
-
 
 class BatchChatCompletionRequest(OpenAIBaseModel):
     """Request model for the /v1/chat/completions/batch endpoint.