FIX

Signed-off-by: Jee Jee Li <jeejeelee@inferact.ai>
Merge remote-tracking branch 'origin/main' into glm5--fp32-router
2026-07-04 13:45:35 +00:00 · 2026-07-03 15:40:18 +00:00 · 2026-07-03 11:25:06 +00:00 · 2026-07-03 10:27:36 +00:00
899 changed files with 10360 additions and 43814 deletions
@@ -8,7 +8,6 @@ run_all_patterns:
  - "docker/docker-bake-rocm.hcl"
  - ".buildkite/hardware_tests/amd.yaml"
  - ".buildkite/scripts/ci-bake-rocm.sh"
-  - ".buildkite/scripts/rocm/"
  - ".buildkite/scripts/hardware_ci/run-amd-test.py"
  - ".buildkite/scripts/hardware_ci/run-amd-test.sh"
  - "CMakeLists.txt"
@@ -1,28 +1,5 @@
 group: Hardware - AMD Build
-
-# ROCm image flow:
-# 1. Refresh the long-lived ROCm base image only when Dockerfile.rocm_base changes.
-# 2. Build ci_base from either the stable base or the freshly refreshed base.
-# 3. Build the per-commit ROCm CI image and smoke-test it before GPU jobs run.
 steps:
-  - label: "AMD: :docker: refresh ROCm base"
-    key: refresh-rocm-base-amd
-    depends_on: []
-    device: amd_cpu
-    no_plugin: true
-    commands:
-      - bash .buildkite/scripts/rocm/refresh-base-image.sh
-    env:
-      DOCKER_BUILDKIT: "1"
-      BUILDKIT_PROGRESS: "tty"
-      TERM: "xterm-256color"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 1
-        - exit_status: -10  # Agent was lost
-          limit: 1
-
  # Ensure ci_base is up-to-date before building the test image.
  # Compares a content hash of ci_base-affecting files against the remote
  # image label. If hashes match the build is skipped (< 30 s); if they
@@ -30,16 +7,13 @@ steps:
  - label: "AMD: :docker: ensure ci_base"
    key: ensure-ci-base-amd
    soft_fail: false
-    depends_on:
-      - refresh-rocm-base-amd
+    depends_on: []
    device: amd_cpu
    no_plugin: true
    commands:
-      - bash .buildkite/scripts/rocm/build-ci-base.sh
+      - bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
    env:
      DOCKER_BUILDKIT: "1"
-      BUILDKIT_PROGRESS: "tty"
-      TERM: "xterm-256color"
      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
      REMOTE_VLLM: "1"
@@ -59,12 +33,35 @@ steps:
    device: amd_cpu
    no_plugin: true
    commands:
-      - bash .buildkite/scripts/rocm/build-test-image.sh
-      - bash .buildkite/scripts/rocm/smoke-test-image.sh
+      - |
+        if [[ "${ROCM_CI_ARTIFACT_ONLY:-0}" == "1" ]]; then
+          echo "ROCM_CI_ARTIFACT_ONLY=1; building ROCm wheel artifact only"
+          IMAGE_TAG="" bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-artifacts
+        else
+          bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-wheel
+        fi
+      - |
+        docker run --rm --network=none --entrypoint /bin/bash "rocm/vllm-ci:${BUILDKITE_COMMIT}" -ec '
+          if [ ! -d /vllm-workspace ]; then echo Missing directory: /vllm-workspace >&2; exit 1; fi
+          if [ ! -d /vllm-workspace/tests ]; then echo Missing directory: /vllm-workspace/tests >&2; exit 1; fi
+          if [ ! -d /vllm-workspace/src/vllm ]; then echo Missing directory: /vllm-workspace/src/vllm >&2; exit 1; fi
+          if [ ! -x /vllm-workspace/src/vllm/vllm-rs ]; then echo Missing executable: /vllm-workspace/src/vllm/vllm-rs >&2; exit 1; fi
+          command -v python3
+          command -v uv
+          command -v pytest
+          if ! command -v amd-smi >/dev/null 2>&1 && ! command -v rocminfo >/dev/null 2>&1; then
+            echo No ROCm CLI found in image >&2
+            exit 1
+          fi
+          python3 - <<PY
+        import torch, vllm
+        print(torch.__version__)
+        print(vllm.__version__)
+        PY
+          echo AMD image smoke OK
+        '
    env:
      DOCKER_BUILDKIT: "1"
-      BUILDKIT_PROGRESS: "tty"
-      TERM: "xterm-256color"
      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
      IMAGE_TAG: "rocm/vllm-ci:$BUILDKITE_COMMIT"
@@ -17,14 +17,12 @@ steps:
  - tests/kernels/test_awq_int4_to_int8.py
  - tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
  - tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
-  - tests/kernels/mamba/test_cpu_short_conv.py
  commands:
    - |
      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
      pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
      pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
      pytest -x -v -s tests/kernels/moe/test_cpu_quant_fused_moe.py
-      pytest -x -v -s tests/kernels/mamba/test_cpu_short_conv.py
      pytest -x -v -s tests/kernels/test_onednn.py
      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
@@ -141,22 +139,9 @@ steps:
  commands:
    - |
      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 --ignore=tests/models/multimodal/generation/test_qwen2_5_vl.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
+      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: 4

- label: CPU-Qwen2.5-VL Multimodal Tests
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies:
-  # - vllm/
-  - vllm/model_executor/layers/rotary_embedding
-  - tests/models/multimodal/generation/
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
-      VLLM_CI_ENV=0 pytest -x -v -s tests/models/multimodal/generation/test_qwen2_5_vl.py"
-
 - label: "Arm CPU Test"
  depends_on: []
  soft_fail: false
@@ -45,7 +45,7 @@ steps:
    agent_tags:
      label: production
      gpu: 1+
-      mem: 24+
+      mem: 16+
    no_plugin: true
    env:
      REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -79,18 +79,12 @@ setup_buildx_builder() {
    docker buildx ls | grep -E '^\*|^NAME' || docker buildx ls
 }

-annotate_image_tags() {
-    .buildkite/scripts/annotate-image-build.sh \
-        "${IMAGE_TAG:-}" "${IMAGE_TAG_LATEST:-}"
-}
-
 check_and_skip_if_image_exists() {
    if [[ -n "${IMAGE_TAG:-}" ]]; then
        echo "--- :mag: Checking if image exists"
        if docker manifest inspect "${IMAGE_TAG}" >/dev/null 2>&1; then
            echo "Image already exists: ${IMAGE_TAG}"
            echo "Skipping build"
-            annotate_image_tags
            exit 0
        fi
        echo "Image not found, proceeding with build"
@@ -177,18 +171,6 @@ BRANCH=$4
 IMAGE_TAG=$5
 IMAGE_TAG_LATEST=${6:-} # only used for main branch, optional

-# When TORCH_NIGHTLY=1, build the base CI image against PyTorch nightly so the
-# entire existing pipeline runs on nightly torch (CUDA/GPU lane only). Delegate
-# to the dedicated nightly build (PYTORCH_NIGHTLY=1, CUDA 13.0) and tag it at the
-# normal IMAGE_TAG that every test step already pulls -- no separate image tag,
-# no duplicate "vLLM Against PyTorch Nightly" pipeline section.
-if [[ "${TORCH_NIGHTLY:-0}" == "1" ]]; then
-    echo "--- :warning: TORCH_NIGHTLY=1 -- building base image on PyTorch nightly"
-    SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-    exec "${SCRIPT_DIR}/image_build_torch_nightly.sh" \
-        "${REGISTRY}" "${REPO}" "${BUILDKITE_COMMIT}" "${BRANCH}" "${IMAGE_TAG}"
-fi
-
 # build config
 TARGET="test-ci"
 VLLM_BAKE_FILE_PATH="${VLLM_BAKE_FILE_PATH:-docker/docker-bake.hcl}"
@@ -272,5 +254,3 @@ echo "--- :docker: Building ${TARGET}"
 docker --debug buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --progress plain "${TARGET}"

 echo "--- :white_check_mark: Build complete"
-
-annotate_image_tags
@@ -9,31 +9,30 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
-IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-arm64"

 # authenticate with AWS ECR
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true

 # skip build if image already exists
-if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
-  echo "Image found"
-else
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64) ]]; then
  echo "Image not found, proceeding with build..."
-  # build for arm64 GPU targets: Grace/GH200 (sm_90),
-  # Blackwell/Thor (sm_100/sm_103/sm_110), and DGX Spark/GB10
-  # (sm_121, family-covered by 12.0 under CUDA 13)
-  docker build --file docker/Dockerfile \
-    --platform linux/arm64 \
-    --build-arg max_jobs=16 \
-    --build-arg nvcc_threads=4 \
-    --build-arg torch_cuda_arch_list="9.0 10.0 11.0 12.0" \
-    --build-arg USE_SCCACHE=1 \
-    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-    --tag "$IMAGE" \
-    --target test \
-    --progress plain .
-  # push
-  docker push "$IMAGE"
+else
+  echo "Image found"
+  exit 0
 fi

-.buildkite/scripts/annotate-image-build.sh "$IMAGE"
+# build for arm64 GPU targets: Grace/GH200 (sm_90) and DGX Spark/GB10
+# (sm_121, family-covered by 12.0 under CUDA 13)
+docker build --file docker/Dockerfile \
+  --platform linux/arm64 \
+  --build-arg max_jobs=16 \
+  --build-arg nvcc_threads=4 \
+  --build-arg torch_cuda_arch_list="9.0 12.0" \
+  --build-arg USE_SCCACHE=1 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64 \
+  --target test \
+  --progress plain .
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64
@@ -9,26 +9,26 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
-IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu"

 # authenticate with AWS ECR
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true

 # skip build if image already exists
-if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
-  echo "Image found"
-else
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
  echo "Image not found, proceeding with build..."
-  # build
-  docker build --file docker/Dockerfile.cpu \
-    --build-arg max_jobs=16 \
-    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-    --build-arg VLLM_CPU_X86=true \
-    --tag "$IMAGE" \
-    --target vllm-test \
-    --progress plain .
-  # push
-  docker push "$IMAGE"
+else
+  echo "Image found"
+  exit 0
 fi

-.buildkite/scripts/annotate-image-build.sh "$IMAGE"
+# build
+docker build --file docker/Dockerfile.cpu \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --build-arg VLLM_CPU_X86=true \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
+  --target vllm-test \
+  --progress plain .
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu
@@ -9,25 +9,25 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
-IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-arm64-cpu"

 # authenticate with AWS ECR
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true

 # skip build if image already exists
-if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
-  echo "Image found"
-else
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
  echo "Image not found, proceeding with build..."
-  # build
-  docker build --file docker/Dockerfile.cpu \
-    --build-arg max_jobs=16 \
-    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-    --tag "$IMAGE" \
-    --target vllm-test \
-    --progress plain .
-  # push
-  docker push "$IMAGE"
+else
+  echo "Image found"
+  exit 0
 fi

-.buildkite/scripts/annotate-image-build.sh "$IMAGE"
+# build
+docker build --file docker/Dockerfile.cpu \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
+  --target vllm-test \
+  --progress plain .
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu
@@ -9,26 +9,26 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
-IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu"

 # authenticate with AWS ECR
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true

 # skip build if image already exists
-if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
-  echo "Image found"
-else
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then
  echo "Image not found, proceeding with build..."
-  # build
-  docker build \
-    --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
-    --build-arg max_jobs=16 \
-    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-    --tag "$IMAGE" \
-    --progress plain \
-    https://github.com/vllm-project/vllm-gaudi.git
-  # push
-  docker push "$IMAGE"
+else
+  echo "Image found"
+  exit 0
 fi

-.buildkite/scripts/annotate-image-build.sh "$IMAGE"
+# build
+docker build \
+  --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu \
+  --progress plain \
+  https://github.com/vllm-project/vllm-gaudi.git
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu
@@ -40,7 +40,6 @@ docker buildx ls
 echo "--- :mag: Checking if image already exists"
 if docker manifest inspect "$IMAGE_TAG" >/dev/null 2>&1; then
  echo "Image found: $IMAGE_TAG — skipping build"
-  .buildkite/scripts/annotate-image-build.sh "$IMAGE_TAG"
  exit 0
 fi
 echo "Image not found, proceeding with build..."
@@ -67,5 +66,3 @@ docker buildx build --file docker/Dockerfile \
  --progress plain .

 echo "--- :white_check_mark: Torch nightly image build complete: $IMAGE_TAG"
-
-.buildkite/scripts/annotate-image-build.sh "$IMAGE_TAG"
@@ -9,26 +9,26 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
-IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-xpu"

 # authenticate with AWS ECR
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true

 # skip build if image already exists
-if docker manifest inspect "$IMAGE" &> /dev/null; then
-  echo "Image found"
-else
+if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then
  echo "Image not found, proceeding with build..."
-  # build
-  docker build \
-    --file docker/Dockerfile.xpu \
-    --build-arg max_jobs=16 \
-    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-    --tag "$IMAGE" \
-    --progress plain .
-  # push
-  docker push "$IMAGE"
+else
+  echo "Image found"
+  exit 0
 fi

-.buildkite/scripts/annotate-image-build.sh "$IMAGE"
+# build
+docker build \
+  --file docker/Dockerfile.xpu \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu \
+  --progress plain .
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu
@@ -38,7 +38,7 @@ steps:
  agent_tags:
    label: production
    gpu: 1+
-    mem: 24+
+    mem: 16+
  no_plugin: true
  working_dir: "."
  env:
@@ -72,31 +72,9 @@ steps:
      pytest -v -s v1/test_oracle.py &&
      pytest -v -s v1/test_request.py &&
      pytest -v -s v1/test_outputs.py &&
-      pytest -v -s v1/sample'
-
- label: Basic Models Tests (Initialization)
-  timeout_in_minutes: 60
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 1+
-    mem: 16+
-  no_plugin: true
-  working_dir: "."
-  env:
-    REGISTRY: "public.ecr.aws/q9t5s3a7"
-    REPO: "vllm-ci-test-repo"
-    VLLM_TEST_DEVICE: "xpu"
-  source_file_dependencies:
-    - vllm/
-    - tests/models/test_initialization.py
-    - tests/models/registry.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'export VLLM_XPU_FUSED_MOE_USE_REF=1 &&
-      cd tests &&
-      pytest -v -s models/test_initialization.py::test_can_initialize_large_subset[Eagle3MiniMaxM2ForCausalLM]'
+      pytest -v -s v1/sample/test_topk_topp_sampler.py &&
+      pytest -v -s v1/sample/test_logprobs.py &&
+      pytest -v -s v1/sample/test_logprobs_e2e.py'

 - label: XPU CPU Offload
  timeout_in_minutes: 60
@@ -132,7 +110,7 @@ steps:
  agent_tags:
    label: production
    gpu: 2+
-    mem: 16+
+    mem: 24+
  no_plugin: true
  working_dir: "."
  env:
@@ -9,7 +9,7 @@ steps:
  agent_tags:
    label: production
    gpu: 2+
-    mem: 16+
+    mem: 24+
  no_plugin: true
  working_dir: "."
  env:
@@ -9,7 +9,7 @@ steps:
  agent_tags:
    label: production
    gpu: 1+
-    mem: 24+
+    mem: 16+
  no_plugin: true
  working_dir: "."
  env:
@@ -81,7 +81,7 @@ steps:
  agent_tags:
    label: production
    gpu: 1+
-    mem: 24+
+    mem: 16+
  no_plugin: true
  working_dir: "."
  env:
@@ -105,7 +105,7 @@ steps:
  agent_tags:
    label: production
    gpu: 1+
-    mem: 24+
+    mem: 16+
  no_plugin: true
  working_dir: "."
  env:
@@ -49,30 +49,6 @@ steps:
        VLLM_XPU_FUSED_MOE_USE_REF=1 python3 examples/basic/offline_inference/generate.py --model Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 --enforce-eager -tp 2 --max-model-len 8192 &&
        python3 examples/basic/offline_inference/generate.py --model INCModel/Qwen3-30B-A3B-Instruct-2507-MXFP4-LLMC --enforce-eager -tp 2 --max-model-len 8192
        '
-  - label: "XPU W8A8 FP8 Linear Examples"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 60
-    device: intel_gpu
-    agent_tags:
-      label: production
-      gpu: 1+
-      mem: 24+
-    no_plugin: true
-    env:
-      REGISTRY: "public.ecr.aws/q9t5s3a7"
-      REPO: "vllm-ci-test-repo"
-      VLLM_TEST_DEVICE: "xpu"
-    source_file_dependencies:
-      - vllm/
-      - .buildkite/intel_jobs/test-intel.yaml
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 --enforce-eager --max-model-len 4096 &&
-        python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic --enforce-eager --max-model-len 4096 &&
-        python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model meta-llama/Llama-3.2-1B-Instruct --quantization fp8 --enforce-eager --max-model-len 4096
-        '
  - label: "XPU V1 test"
    depends_on:
      - image-build-xpu
@@ -81,7 +57,7 @@ steps:
    agent_tags:
      label: production
      gpu: 1+
-      mem: 24+
+      mem: 16+
    no_plugin: true
    env:
      REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -144,27 +120,4 @@ steps:
      - >-
        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
        'cd tests &&
-        pytest -v -s quantization/test_auto_round.py'
-  - label: "XPU compressed tensors FP8 test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 60
-    device: intel_gpu
-    agent_tags:
-      label: production
-      gpu: 1+
-      mem: 16+
-    no_plugin: true
-    env:
-      REGISTRY: "public.ecr.aws/q9t5s3a7"
-      REPO: "vllm-ci-test-repo"
-      VLLM_TEST_DEVICE: "xpu"
-    source_file_dependencies:
-      - vllm/
-      - tests/quantization/test_compressed_tensors.py
-      - .buildkite/intel_jobs/test-intel.yaml
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'cd tests &&
-        pytest -v -s quantization/test_compressed_tensors.py::test_compressed_tensors_fp8'
+        pytest -v -s quantization/test_auto_round.py'
@@ -350,25 +350,6 @@ steps:
          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"'

-      - label: ":docker: Build release image - x86_64 - XPU"
-        depends_on: ~
-        id: build-xpu-release-image
-        agents:
-          queue: cpu_queue_release
-        commands:
-          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh xpu) \
-              --build-arg GIT_REPO_CHECK=1 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile.xpu .
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-xpu"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-xpu"'
-        env:
-          DOCKER_BUILDKIT: "1"
-
      - block: "Build release image for x86_64 CPU"
        key: block-cpu-release-image-build
        depends_on: ~
@@ -464,16 +445,6 @@ steps:
          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 12.9 Ubuntu 24.04" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"'

-      - label: "Create manifest - XPU"
-        depends_on:
-          - build-xpu-release-image
-        id: create-manifest-xpu
-        agents:
-          queue: small_cpu_queue_release
-        commands:
-          - "bash .buildkite/scripts/xpu/create-xpu-ecr-manifest.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: XPU" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-xpu"'
-
      - label: "Publish nightly multi-arch image to DockerHub"
        depends_on:
          - create-multi-arch-manifest
@@ -848,23 +819,6 @@ steps:
      DOCKER_BUILDKIT: "1"
      S3_BUCKET: "vllm-wheels"

-  - label: "Publish nightly XPU image to DockerHub"
-    depends_on:
-      - create-manifest-xpu
-    if: build.env("NIGHTLY") == "1"
-    agents:
-      queue: small_cpu_queue_release
-    commands:
-      - "bash .buildkite/scripts/xpu/push-nightly-builds-xpu.sh"
-      - "bash .buildkite/scripts/cleanup-nightly-builds.sh nightly- vllm/vllm-openai-xpu"
-    plugins:
-      - docker-login#v3.0.0:
-          username: vllmbot
-          password-env: DOCKERHUB_TOKEN
-    env:
-      DOCKER_BUILDKIT: "1"
-      DOCKERHUB_USERNAME: "vllmbot"
-
  - label: "Publish nightly ROCm image to DockerHub"
    depends_on:
      - build-rocm-release-image
@@ -895,7 +849,6 @@ steps:
      - create-multi-arch-manifest-cuda-12-9
      - create-multi-arch-manifest-ubuntu2404
      - create-multi-arch-manifest-cuda-12-9-ubuntu2404
-      - create-manifest-xpu
      - build-rocm-release-image
      - input-release-version
      # Wait for CPU builds if their block steps were unblocked, so publish
@@ -1,36 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Append the Docker image tag(s) an image-build step pushed to a Buildkite
-# annotation, so the built image tags show up on the build page instead of
-# being buried in the job logs.
-#
-# Usage: annotate-image-build.sh <image_tag> [<image_tag> ...]
-set -euo pipefail
-
-# buildkite-agent only exists on Buildkite agents; no-op elsewhere so the
-# image build scripts stay runnable locally.
-if ! command -v buildkite-agent >/dev/null 2>&1; then
-    echo "buildkite-agent not found; skipping image tag annotation"
-    exit 0
-fi
-
-label="${BUILDKITE_LABEL:-Image build}"
-content=""
-for image in "$@"; do
-    [[ -n "$image" ]] || continue
-    content+="- **${label}**: \`${image}\`"$'\n'
-done
-
-if [[ -z "$content" ]]; then
-    echo "No image tags provided; nothing to annotate"
-    exit 0
-fi
-
-# Best-effort: a flaky annotation must never fail an otherwise successful
-# (and expensive) image build.
-if ! printf '%s' "$content" | \
-    buildkite-agent annotate --append --style 'info' --context 'docker-images'; then
-    echo "warning: failed to annotate build with image tags"
-fi
@@ -29,11 +29,7 @@ if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
        TORCH_INDEX_URL=""
    fi
 else
-    if [ "${TORCH_NIGHTLY:-0}" = "1" ]; then
-        TORCH_INDEX_URL="https://download.pytorch.org/whl/nightly/cu130"
-    else
-        TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
-    fi
+    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
 fi
 echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"

@@ -15,7 +15,7 @@ set -euo pipefail

 DEFAULT_REPO_SLUG="vllm-project/vllm"
 DEFAULT_CI_HCL_SOURCE="docker/ci-rocm.hcl"
-DEFAULT_CI_BASE_CONTENT_FILES="requirements/common.txt requirements/rocm.txt requirements/test/rocm.txt docker/Dockerfile.rocm_base docker/ci-rocm.hcl docker/docker-bake-rocm.hcl tools/install_torchcodec_rocm.sh tests/vllm_test_utils .buildkite/scripts/ci-bake-rocm.sh .buildkite/scripts/rocm/build-ci-base.sh"
+DEFAULT_CI_BASE_CONTENT_FILES="requirements/common.txt requirements/rocm.txt requirements/test/rocm.txt docker/Dockerfile.rocm_base docker/ci-rocm.hcl docker/docker-bake-rocm.hcl tools/install_torchcodec_rocm.sh tests/vllm_test_utils .buildkite/scripts/ci-bake-rocm.sh"
 DEFAULT_CI_BASE_DOCKERFILE="docker/Dockerfile.rocm"
 DEFAULT_CI_BASE_DOCKERFILE_STAGES="base build_rixl build_rocshmem build_deepep mori_base ci_base"
 DEFAULT_CI_BASE_METADATA_VERSION="1"
@@ -393,16 +393,6 @@ should_upload_wheel_artifacts() {
        || "${TARGET}" == *"artifact"* ]]
 }

-set_buildkite_metadata() {
-    local key="$1"
-    local value="$2"
-
-    [[ -n "${value}" ]] || return 0
-    if command -v buildkite-agent >/dev/null 2>&1; then
-        buildkite-agent meta-data set "${key}" "${value}" || true
-    fi
-}
-
 get_remote_image_label() {
    local image_ref="$1"
    local label_key="$2"
@@ -743,8 +733,6 @@ configure_ci_base_image_refs() {

    if is_ci_base_target; then
        IMAGE_TAG="${primary_tag}"
-        CI_BASE_IMAGE="${primary_tag}"
-        export CI_BASE_IMAGE
        export IMAGE_TAG

        echo "ci_base primary image tag: ${CI_BASE_IMAGE_TAG}"
@@ -762,10 +750,6 @@ configure_ci_base_image_refs() {
            echo "ci_base stable alias will not be pushed for this build"
            echo "Set NIGHTLY=1 on ${CI_BASE_STABLE_BRANCH:-main} to refresh ${stable_tag}"
        fi
-        set_buildkite_metadata "rocm-ci-base-image" "${CI_BASE_IMAGE_TAG}"
-        set_buildkite_metadata "rocm-ci-base-image-content" "${content_tag}"
-        set_buildkite_metadata "rocm-ci-base-image-commit" "${CI_BASE_IMAGE_TAG_COMMIT:-}"
-        set_buildkite_metadata "rocm-ci-base-image-stable" "${CI_BASE_IMAGE_TAG_STABLE:-}"
        return 0
    fi

@@ -1795,10 +1779,7 @@ seed_dependency_caches_if_needed() {

        echo "--- :docker: Seeding ${target}"
        echo "Expected cache ref: ${cache_ref}"
-        docker buildx bake \
-            "${BAKE_FILES[@]}" \
-            --progress "${BUILDKIT_PROGRESS:-plain}" \
-            "${target}"
+        docker buildx bake "${BAKE_FILES[@]}" --progress plain "${target}"
        verify_dependency_cache_ref "${cache_ref}"
    done
 }
@@ -1826,10 +1807,7 @@ run_bake() {
    local build_rc=0

    echo "--- :docker: Building ${TARGET}"
-    docker buildx bake \
-        "${BAKE_FILES[@]}" \
-        --progress "${BUILDKIT_PROGRESS:-plain}" \
-        "${BAKE_TARGETS[@]}" || build_rc=$?
+    docker buildx bake "${BAKE_FILES[@]}" --progress plain "${BAKE_TARGETS[@]}" || build_rc=$?

    if [[ ${build_rc} -eq 0 ]]; then
        echo "--- :white_check_mark: Build complete"
@@ -28,17 +28,6 @@
 ###############################################################################
 set -o pipefail

-: "${BUILDKIT_PROGRESS:=plain}"
-: "${TERM:=xterm-256color}"
-: "${FORCE_COLOR:=1}"
-: "${CLICOLOR_FORCE:=1}"
-: "${PY_COLORS:=1}"
-: "${ROCM_DOCKER_TTY:=1}"
-if [[ " ${PYTEST_ADDOPTS:-} " != *" --color"* ]]; then
-  PYTEST_ADDOPTS="${PYTEST_ADDOPTS:+${PYTEST_ADDOPTS} }--color=yes"
-fi
-export BUILDKIT_PROGRESS TERM FORCE_COLOR CLICOLOR_FORCE PY_COLORS PYTEST_ADDOPTS ROCM_DOCKER_TTY
-
 # Export Python path for commands that run directly on the host. Containerized
 # tests set this to /vllm-workspace below so spawned Python processes do not
 # depend on their current working directory.
@@ -160,7 +149,6 @@ EOF
  echo "--- Building local ROCm test image"
  docker build \
    --pull=false \
-    --progress "${BUILDKIT_PROGRESS}" \
    --build-arg "BASE_IMAGE=${base_image}" \
    -t "${artifact_image}" \
    "${context_dir}" || return 1
@@ -460,11 +448,9 @@ checkout="${BUILDKITE_BUILD_CHECKOUT_PATH:-}"
 if [[ -z "${checkout}" || ! -d "${checkout}" ]]; then
  checkout="."
 fi
-# Pass safe.directory per-command (-c) because buildkite runs will always fail
-# the next check on git 2.35.2+ due to mixed uses of root and buildkite-agent/uids.
-if git -c "safe.directory=${checkout}" -C "${checkout}" rev-parse --is-inside-work-tree >/dev/null 2>&1; then
+if git -C "${checkout}" rev-parse --is-inside-work-tree >/dev/null 2>&1; then
  vllm_standalone_merge_base="$(
-    git -c "safe.directory=${checkout}" -C "${checkout}" merge-base HEAD origin/main 2>/dev/null || true
+    git -C "${checkout}" merge-base HEAD origin/main 2>/dev/null || true
  )"
 fi
 if [[ -z "${vllm_standalone_merge_base}" ]]; then
@@ -547,13 +533,6 @@ if is_multi_node "$commands"; then
 else
  echo "--- Single-node job"
  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
-  docker_run_terminal_args=(-i)
-  if [[ "${ROCM_DOCKER_TTY}" == "1" ]]; then
-    docker_run_terminal_args+=(-t)
-    echo "Docker interactive stdin: enabled; TTY allocation: enabled"
-  else
-    echo "Docker interactive stdin: enabled; TTY allocation: disabled"
-  fi

  ulimit_core_hard=$(ulimit -H -c)
  if [[ "$ulimit_core_hard" == "unlimited" ]]; then
@@ -570,7 +549,6 @@ else
  fi

  docker run \
-    "${docker_run_terminal_args[@]}" \
    --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
    $RDMA_FLAGS \
    --network=host \
@@ -585,11 +563,6 @@ else
    -e AWS_SECRET_ACCESS_KEY \
    -e BUILDKITE_PARALLEL_JOB \
    -e BUILDKITE_PARALLEL_JOB_COUNT \
-    -e TERM \
-    -e FORCE_COLOR \
-    -e CLICOLOR_FORCE \
-    -e PY_COLORS \
-    -e PYTEST_ADDOPTS \
    -v "${HF_CACHE}:${HF_MOUNT}" \
    -e "HF_HOME=${HF_MOUNT}" \
    -e "PYTHONPATH=${MYPYTHONPATH}" \
@@ -38,9 +38,7 @@ function cpu_tests() {
    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_cpu_fused_moe.py
-    pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
-    pytest -x -v -s tests/kernels/moe/test_cpu_int4_moe.py
-    pytest -x -v -s tests/kernels/mamba/test_cpu_short_conv.py"
+    pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"

  # skip tests requiring model downloads if HF_TOKEN is not set
  # due to rate-limits
@@ -64,6 +62,7 @@ function cpu_tests() {
    set -e
    pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"

+
  # basic online serving
  docker exec cpu-test bash -c '
    set -e
@@ -130,22 +130,6 @@ docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm
 docker push vllm/vllm-openai-rocm:latest-base
 docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base

-# ---- XPU ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu vllm/vllm-openai-xpu:latest-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai-xpu:latest-x86_64
-docker push vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64
-
-docker manifest rm vllm/vllm-openai-xpu:latest || true
-docker manifest rm vllm/vllm-openai-xpu:v${RELEASE_VERSION} || true
-docker manifest create vllm/vllm-openai-xpu:latest vllm/vllm-openai-xpu:latest-x86_64 --amend
-docker manifest create vllm/vllm-openai-xpu:v${RELEASE_VERSION} vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64 --amend
-docker manifest push vllm/vllm-openai-xpu:latest
-docker manifest push vllm/vllm-openai-xpu:v${RELEASE_VERSION}
-
 # ---- CPU ----
 # CPU images are behind separate block steps and may not have been built.
 # All-or-nothing: inspect both arches first, then either publish everything
@@ -1,32 +0,0 @@
-#!/usr/bin/env bash
-# Build the ROCm ci_base image, optionally from a freshly rebuilt ROCm base.
-
-set -euo pipefail
-
-metadata_get() {
-    local key="$1"
-    if command -v buildkite-agent >/dev/null 2>&1; then
-        buildkite-agent meta-data get "${key}" 2>/dev/null || true
-    fi
-}
-
-main() {
-    local base_refreshed=""
-
-    base_refreshed="$(metadata_get rocm-base-refresh)"
-    if [[ "${base_refreshed}" == "1" ]]; then
-        export BASE_IMAGE
-        export CI_BASE_PUSH_STABLE_TAG
-
-        BASE_IMAGE="$(metadata_get rocm-base-image)"
-        CI_BASE_PUSH_STABLE_TAG="$(metadata_get rocm-base-push-stable-tag)"
-        CI_BASE_PUSH_STABLE_TAG="${CI_BASE_PUSH_STABLE_TAG:-0}"
-
-        echo "Using refreshed ROCm base image for ci_base: ${BASE_IMAGE}"
-        echo "Push stable ci_base tag: ${CI_BASE_PUSH_STABLE_TAG}"
-    fi
-
-    bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
-}
-
-main "$@"
@@ -1,57 +0,0 @@
-#!/usr/bin/env bash
-# Build the ROCm CI test image or wheel artifact.
-#
-# When Dockerfile.rocm_base changes, always build the full image so downstream
-# ROCm tests can validate the freshly rebuilt base -> ci_base -> ci image chain.
-
-set -euo pipefail
-
-metadata_get() {
-    local key="$1"
-    if command -v buildkite-agent >/dev/null 2>&1; then
-        buildkite-agent meta-data get "${key}" 2>/dev/null || true
-    fi
-}
-
-use_refreshed_base_if_present() {
-    local base_refreshed=""
-
-    base_refreshed="$(metadata_get rocm-base-refresh)"
-    if [[ "${base_refreshed}" != "1" ]]; then
-        return 1
-    fi
-
-    export BASE_IMAGE
-    export CI_BASE_IMAGE
-    export IMAGE_TAG_LATEST
-
-    BASE_IMAGE="$(metadata_get rocm-base-image)"
-    CI_BASE_IMAGE="$(metadata_get rocm-ci-base-image)"
-    IMAGE_TAG_LATEST="$(metadata_get rocm-ci-image-descriptive)"
-
-    echo "Using refreshed ROCm base image for test image: ${BASE_IMAGE}"
-    echo "Using refreshed ROCm ci_base image for test image: ${CI_BASE_IMAGE}"
-    if [[ -n "${IMAGE_TAG_LATEST}" ]]; then
-        echo "Also tagging full ROCm CI image as: ${IMAGE_TAG_LATEST}"
-    fi
-
-    return 0
-}
-
-main() {
-    local base_refreshed=0
-
-    if use_refreshed_base_if_present; then
-        base_refreshed=1
-    fi
-
-    if [[ "${ROCM_CI_ARTIFACT_ONLY:-0}" == "1" && "${base_refreshed}" != "1" ]]; then
-        echo "ROCM_CI_ARTIFACT_ONLY=1; building ROCm wheel artifact only"
-        IMAGE_TAG="" bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-artifacts
-        return
-    fi
-
-    bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-wheel
-}
-
-main "$@"
@@ -1,513 +0,0 @@
-#!/usr/bin/env bash
-# Build and publish a fresh ROCm base image when Dockerfile.rocm_base changes.
-#
-# Normal AMD CI builds should not pay for this path. The script no-ops unless
-# docker/Dockerfile.rocm_base changed relative to the branch base, the previous
-# main commit, or ROCM_BASE_REFRESH_FORCE=1 is set.
-
-set -euo pipefail
-
-DOCKERFILE="${ROCM_BASE_DOCKERFILE:-docker/Dockerfile.rocm_base}"
-BASE_REPO="${ROCM_BASE_IMAGE_REPO:-rocm/vllm-dev}"
-CI_IMAGE_REPO="${ROCM_CI_IMAGE_REPO:-rocm/vllm-ci}"
-BUILDER_NAME="${ROCM_BASE_BUILDER_NAME:-vllm-rocm-base-builder}"
-DEFAULT_ROCM_BASE_METADATA_VERSION="1"
-DEFAULT_ROCM_BASE_CONTENT_FILES="${DOCKERFILE}"
-DEFAULT_ROCM_BASE_CONTENT_ARGS="BASE_IMAGE TRITON_BRANCH TRITON_REPO PYTORCH_BRANCH PYTORCH_REPO PYTORCH_VISION_BRANCH PYTORCH_VISION_REPO PYTORCH_AUDIO_BRANCH PYTORCH_AUDIO_REPO FA_BRANCH FA_REPO AITER_BRANCH AITER_REPO MORI_BRANCH MORI_REPO PYTORCH_ROCM_ARCH PYTHON_VERSION USE_SCCACHE"
-
-metadata_set() {
-    local key="$1"
-    local value="$2"
-
-    [[ -n "${value}" ]] || return 0
-    if command -v buildkite-agent >/dev/null 2>&1; then
-        buildkite-agent meta-data set "${key}" "${value}" || true
-    fi
-}
-
-compute_content_hash() {
-    local path=""
-    local file=""
-
-    for path in "$@"; do
-        if [[ -d "${path}" ]]; then
-            while IFS= read -r -d '' file; do
-                printf 'file:%s\n' "${file}"
-                sha256sum "${file}"
-            done < <(find "${path}" -type f -print0 | sort -z)
-        elif [[ -f "${path}" ]]; then
-            printf 'file:%s\n' "${path}"
-            sha256sum "${path}"
-        else
-            printf 'missing:%s\n' "${path}"
-        fi
-    done | sha256sum | cut -d' ' -f1
-}
-
-clean_docker_tag() {
-    local input="$1"
-    echo "${input}" | sed 's/[^a-zA-Z0-9._-]/_/g' | cut -c1-128
-}
-
-tag_component() {
-    local input="$1"
-    local max_chars="${2:-24}"
-
-    clean_docker_tag "${input:-unknown}" | cut -c1-"${max_chars}"
-}
-
-extract_arg_default() {
-    local arg_name="$1"
-
-    sed -n -E "s/^[[:space:]]*ARG[[:space:]]+${arg_name}=\"?([^\"[:space:]]+)\"?.*/\\1/p" \
-        "${DOCKERFILE}" | head -1
-}
-
-resolve_image_digest() {
-    local image_ref="$1"
-
-    docker buildx imagetools inspect "${image_ref}" 2>/dev/null \
-        | sed -n -E 's/^Digest:[[:space:]]+//p' \
-        | head -1 || true
-}
-
-resolve_rocm_base_arg_value() {
-    local arg_name="$1"
-    local use_sccache="$2"
-
-    case "${arg_name}" in
-        USE_SCCACHE)
-            printf '%s\n' "${use_sccache}"
-            ;;
-        *)
-            extract_arg_default "${arg_name}"
-            ;;
-    esac
-}
-
-hash_rocm_base_arg_values() {
-    local use_sccache="$1"
-    local base_image_digest="$2"
-    local arg_name=""
-    local arg_value=""
-    shift 2 || true
-
-    for arg_name in "$@"; do
-        [[ -n "${arg_name}" ]] || continue
-        arg_value=$(resolve_rocm_base_arg_value "${arg_name}" "${use_sccache}")
-        printf 'arg:%s=%s\n' "${arg_name}" "${arg_value:-<empty>}"
-        if [[ "${arg_name}" == "BASE_IMAGE" && -n "${arg_value}" ]]; then
-            printf 'arg:%s.digest=%s\n' "${arg_name}" "${base_image_digest:-unknown}"
-        fi
-    done
-}
-
-rocm_version_from_base_image() {
-    local base_image="$1"
-    local version=""
-
-    version="$(sed -n -E 's/.*:([0-9]+\.[0-9]+(\.[0-9]+)?)-.*/\1/p' <<<"${base_image}")"
-    tag_component "${version:-${base_image}}" 16
-}
-
-git_diff_changed_base() {
-    local range="$1"
-    [[ -n "$(git diff --name-only "${range}" -- "${DOCKERFILE}" 2>/dev/null)" ]]
-}
-
-short_git_ref() {
-    local ref="$1"
-
-    git rev-parse --short "${ref}" 2>/dev/null || printf '%s\n' "${ref}"
-}
-
-extract_arg_default_from_ref() {
-    local ref="$1"
-    local arg_name="$2"
-    local content=""
-
-    content="$(git show "${ref}:${DOCKERFILE}" 2>/dev/null || true)"
-    sed -n -E "s/^[[:space:]]*ARG[[:space:]]+${arg_name}=\"?([^\"[:space:]]+)\"?.*/\\1/p" \
-        <<<"${content}" | head -1
-}
-
-log_arg_default_changes() {
-    local old_ref="$1"
-    local new_ref="$2"
-    local content_args="${ROCM_BASE_CONTENT_ARGS:-${DEFAULT_ROCM_BASE_CONTENT_ARGS}}"
-    local arg_name=""
-    local old_value=""
-    local new_value=""
-    local changed=0
-
-    echo "Changed ROCm base ARG defaults:"
-    for arg_name in ${content_args}; do
-        old_value="$(extract_arg_default_from_ref "${old_ref}" "${arg_name}")"
-        new_value="$(extract_arg_default_from_ref "${new_ref}" "${arg_name}")"
-        if [[ "${old_value}" != "${new_value}" ]]; then
-            echo "  - ${arg_name}: ${old_value:-<unset>} -> ${new_value:-<unset>}"
-            changed=1
-        fi
-    done
-
-    if [[ "${changed}" == "0" ]]; then
-        echo "  - none detected; Dockerfile instructions changed outside tracked ARG defaults"
-    fi
-}
-
-log_arg_line_diff() {
-    local range="$1"
-    local arg_diff=""
-
-    arg_diff="$(
-        git diff --unified=0 "${range}" -- "${DOCKERFILE}" 2>/dev/null \
-            | awk '/^[+-][[:space:]]*ARG[[:space:]]/ && $0 !~ /^(---|\+\+\+)/ { print "  " $0 }' \
-            || true
-    )"
-
-    if [[ -n "${arg_diff}" ]]; then
-        echo "Changed Dockerfile ARG lines:"
-        printf '%s\n' "${arg_diff}"
-    fi
-}
-
-log_rocm_base_change_check() {
-    local context="$1"
-    local range="$2"
-    local old_ref="$3"
-    local old_short=""
-    local head_short=""
-
-    old_short="$(short_git_ref "${old_ref}")"
-    head_short="$(short_git_ref HEAD)"
-
-    echo "--- :mag: ROCm base refresh check"
-    echo "Context: ${context}"
-    echo "Dockerfile: ${DOCKERFILE}"
-    echo "Base revision: ${old_short}"
-    echo "Head revision: ${head_short}"
-    echo "Git diff range: ${range}"
-}
-
-log_rocm_base_rebuild_reason() {
-    local context="$1"
-    local range="$2"
-    local old_ref="$3"
-    local changed_files=""
-
-    log_rocm_base_change_check "${context}" "${range}" "${old_ref}"
-
-    changed_files="$(git diff --name-only "${range}" -- "${DOCKERFILE}" 2>/dev/null || true)"
-    echo "Changed files:"
-    if [[ -n "${changed_files}" ]]; then
-        sed 's/^/  - /' <<<"${changed_files}"
-    else
-        echo "  - ${DOCKERFILE}"
-    fi
-    log_arg_default_changes "${old_ref}" HEAD
-    log_arg_line_diff "${range}"
-    echo "Decision: rebuilding ROCm base image because ${DOCKERFILE} changed."
-}
-
-rocm_base_changed_in_range() {
-    local context="$1"
-    local range="$2"
-    local old_ref="$3"
-
-    if git_diff_changed_base "${range}"; then
-        log_rocm_base_rebuild_reason "${context}" "${range}" "${old_ref}"
-        return 0
-    fi
-
-    log_rocm_base_change_check "${context}" "${range}" "${old_ref}"
-    echo "Decision: ROCm base refresh not required; ${DOCKERFILE} is unchanged."
-    return 1
-}
-
-rocm_base_changed() {
-    local base_branch="${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}"
-    local base_ref="refs/remotes/origin/${base_branch}"
-    local merge_base=""
-
-    if [[ "${ROCM_BASE_REFRESH_SKIP:-0}" == "1" ]]; then
-        echo "ROCM_BASE_REFRESH_SKIP=1 set; skipping ROCm base refresh"
-        return 1
-    fi
-
-    if [[ "${ROCM_BASE_REFRESH_FORCE:-0}" == "1" ]]; then
-        echo "ROCM_BASE_REFRESH_FORCE=1 set; refreshing ROCm base image"
-        return 0
-    fi
-
-    if ! git rev-parse --is-inside-work-tree >/dev/null 2>&1; then
-        echo "Not in a git checkout; skipping ROCm base refresh unless forced"
-        return 1
-    fi
-
-    if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" ]]; then
-        git fetch --no-tags --depth=200 origin \
-            "+refs/heads/${base_branch}:${base_ref}" >/dev/null 2>&1 || true
-        merge_base=$(git merge-base HEAD "${base_ref}" 2>/dev/null || true)
-        if [[ -z "${merge_base}" ]]; then
-            echo "Unable to determine merge base with PR base ${base_ref}; skipping ROCm base refresh unless forced"
-            return 1
-        fi
-        if rocm_base_changed_in_range \
-            "pull request build against ${base_ref}" \
-            "${merge_base}...HEAD" \
-            "${merge_base}"; then
-            return 0
-        fi
-    elif [[ "${BUILDKITE_BRANCH:-}" == "${ROCM_BASE_STABLE_BRANCH:-main}" ]] \
-        && git rev-parse --verify HEAD~1 >/dev/null 2>&1; then
-        if rocm_base_changed_in_range \
-            "stable branch build; comparing against previous ${ROCM_BASE_STABLE_BRANCH:-main} commit" \
-            "HEAD~1..HEAD" \
-            "HEAD~1"; then
-            return 0
-        fi
-    else
-        git fetch --no-tags --depth=200 origin \
-            "+refs/heads/${base_branch}:${base_ref}" >/dev/null 2>&1 || true
-        merge_base=$(git merge-base HEAD "${base_ref}" 2>/dev/null || true)
-        if [[ -z "${merge_base}" ]]; then
-            echo "Unable to determine merge base with branch base ${base_ref}; skipping ROCm base refresh unless forced"
-            return 1
-        fi
-        if rocm_base_changed_in_range \
-            "branch build against ${base_ref}" \
-            "${merge_base}...HEAD" \
-            "${merge_base}"; then
-            return 0
-        fi
-    fi
-
-    return 1
-}
-
-should_push_stable_tag() {
-    if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" ]]; then
-        return 1
-    fi
-
-    if [[ "${ROCM_BASE_PUSH_STABLE_TAG:-}" == "1" ]]; then
-        return 0
-    fi
-    if [[ "${ROCM_BASE_PUSH_STABLE_TAG:-}" == "0" ]]; then
-        return 1
-    fi
-
-    [[ "${BUILDKITE_PULL_REQUEST:-false}" == "false" \
-        && "${BUILDKITE_BRANCH:-}" == "${ROCM_BASE_STABLE_BRANCH:-main}" ]]
-}
-
-setup_builder() {
-    echo "--- :buildkite: Setting up buildx builder for ROCm base"
-    if docker buildx inspect "${BUILDER_NAME}" >/dev/null 2>&1; then
-        docker buildx use "${BUILDER_NAME}"
-    else
-        docker buildx create --name "${BUILDER_NAME}" --driver docker-container --use
-    fi
-    docker buildx inspect --bootstrap
-}
-
-compute_base_content_hash() {
-    local use_sccache="$1"
-    local base_image_digest="$2"
-    local content_files="${ROCM_BASE_CONTENT_FILES:-${DEFAULT_ROCM_BASE_CONTENT_FILES}}"
-    local content_args="${ROCM_BASE_CONTENT_ARGS:-${DEFAULT_ROCM_BASE_CONTENT_ARGS}}"
-    local -a content_paths=()
-    local -a content_arg_names=()
-
-    read -r -a content_paths <<< "${content_files}"
-    read -r -a content_arg_names <<< "${content_args}"
-
-    {
-        printf 'content-files-hash:%s\n' "$(compute_content_hash "${content_paths[@]}")"
-        printf 'dockerfile:%s\n' "${DOCKERFILE}"
-        printf 'resolved-build-args:\n'
-        hash_rocm_base_arg_values \
-            "${use_sccache}" "${base_image_digest}" "${content_arg_names[@]}"
-    } | sha256sum | cut -d' ' -f1
-}
-
-build_base_image() {
-    local use_sccache="${ROCM_BASE_USE_SCCACHE:-${USE_SCCACHE:-0}}"
-    local base_hash=""
-    local build_date=""
-    local build_suffix=""
-    local base_image_arg=""
-    local base_image_digest=""
-    local rocm_version=""
-    local triton_arg=""
-    local pytorch_arg=""
-    local pytorch_vision_arg=""
-    local pytorch_audio_arg=""
-    local fa_arg=""
-    local aiter_arg=""
-    local mori_arg=""
-    local python_version_arg=""
-    local pytorch_rocm_arch_arg=""
-    local pytorch_branch=""
-    local aiter_branch=""
-    local dependency_summary=""
-    local descriptor=""
-    local ci_descriptor=""
-    local descriptive_tag=""
-    local stable_tag="${BASE_REPO}:base"
-    local ci_descriptive_tag=""
-    local content_files="${ROCM_BASE_CONTENT_FILES:-${DEFAULT_ROCM_BASE_CONTENT_FILES}}"
-    local content_args="${ROCM_BASE_CONTENT_ARGS:-${DEFAULT_ROCM_BASE_CONTENT_ARGS}}"
-    local content_files_hash=""
-    local metadata_version="${ROCM_BASE_METADATA_VERSION:-${DEFAULT_ROCM_BASE_METADATA_VERSION}}"
-    local -a tags=()
-    local -a no_cache_args=()
-    local -a sccache_args=()
-    local -a content_paths=()
-
-    if [[ ! -f "${DOCKERFILE}" ]]; then
-        echo "Error: ROCm base Dockerfile not found: ${DOCKERFILE}" >&2
-        exit 1
-    fi
-
-    build_date="${ROCM_BASE_TAG_DATE:-$(date -u +%Y%m%d)}"
-    if [[ -n "${BUILDKITE_BUILD_NUMBER:-}" ]]; then
-        build_suffix="_bk_${BUILDKITE_BUILD_NUMBER}"
-    fi
-    base_image_arg="$(extract_arg_default BASE_IMAGE)"
-    base_image_digest="$(resolve_image_digest "${base_image_arg}")"
-    read -r -a content_paths <<< "${content_files}"
-    content_files_hash="$(compute_content_hash "${content_paths[@]}")"
-    base_hash=$(compute_base_content_hash "${use_sccache}" "${base_image_digest}")
-    rocm_version="$(rocm_version_from_base_image "${base_image_arg}")"
-    triton_arg="$(extract_arg_default TRITON_BRANCH)"
-    pytorch_arg="$(extract_arg_default PYTORCH_BRANCH)"
-    pytorch_vision_arg="$(extract_arg_default PYTORCH_VISION_BRANCH)"
-    pytorch_audio_arg="$(extract_arg_default PYTORCH_AUDIO_BRANCH)"
-    fa_arg="$(extract_arg_default FA_BRANCH)"
-    aiter_arg="$(extract_arg_default AITER_BRANCH)"
-    mori_arg="$(extract_arg_default MORI_BRANCH)"
-    python_version_arg="$(extract_arg_default PYTHON_VERSION)"
-    pytorch_rocm_arch_arg="$(extract_arg_default PYTORCH_ROCM_ARCH)"
-    pytorch_branch="$(tag_component "${pytorch_arg}" 16)"
-    aiter_branch="$(tag_component "${aiter_arg}" 24)"
-    dependency_summary="base=${base_image_arg},rocm=${rocm_version},python=${python_version_arg},pytorch=${pytorch_arg},torchvision=${pytorch_vision_arg},torchaudio=${pytorch_audio_arg},triton=${triton_arg},flash-attn=${fa_arg},aiter=${aiter_arg},mori=${mori_arg},pytorch-rocm-arch=${pytorch_rocm_arch_arg}"
-    descriptor="$(clean_docker_tag "base_custom_aiter_${aiter_branch}_torch_${pytorch_branch}_${build_date}${build_suffix}")"
-    ci_descriptor="$(clean_docker_tag "ci_custom_aiter_${aiter_branch}_torch_${pytorch_branch}_${build_date}${build_suffix}")"
-
-    descriptive_tag="${BASE_REPO}:${descriptor}"
-    ci_descriptive_tag="${CI_IMAGE_REPO}:${ci_descriptor}"
-
-    tags=(-t "${descriptive_tag}")
-    if should_push_stable_tag; then
-        tags+=(-t "${stable_tag}")
-        metadata_set "rocm-base-push-stable-tag" "1"
-    else
-        metadata_set "rocm-base-push-stable-tag" "0"
-    fi
-
-    if [[ "${ROCM_BASE_NO_CACHE:-1}" == "1" ]]; then
-        no_cache_args=(--no-cache)
-    fi
-
-    for env_name in \
-        SCCACHE_DOWNLOAD_URL \
-        SCCACHE_ENDPOINT \
-        SCCACHE_BUCKET_NAME \
-        SCCACHE_REGION_NAME \
-        SCCACHE_S3_NO_CREDENTIALS; do
-        if [[ -n "${!env_name:-}" ]]; then
-            sccache_args+=(--build-arg "${env_name}=${!env_name}")
-        fi
-    done
-
-    echo "--- :docker: Building ROCm base image"
-    echo "Dockerfile: ${DOCKERFILE}"
-    echo "Descriptive tag: ${descriptive_tag}"
-    echo "Stable tag: ${stable_tag} ($(should_push_stable_tag && echo enabled || echo disabled))"
-    echo "Content hash: ${base_hash}"
-    echo "Dependency summary: ${dependency_summary}"
-    echo "USE_SCCACHE: ${use_sccache}"
-
-    docker buildx build \
-        "${no_cache_args[@]}" \
-        --pull \
-        --progress "${BUILDKIT_PROGRESS:-plain}" \
-        --file "${DOCKERFILE}" \
-        --build-arg "USE_SCCACHE=${use_sccache}" \
-        "${sccache_args[@]}" \
-        --label "org.opencontainers.image.source=https://github.com/vllm-project/vllm" \
-        --label "org.opencontainers.image.vendor=vLLM" \
-        --label "org.opencontainers.image.title=vLLM ROCm base" \
-        --label "org.opencontainers.image.revision=${BUILDKITE_COMMIT:-}" \
-        --label "vllm.rocm_base.metadata_version=${metadata_version}" \
-        --label "vllm.rocm_base.content_hash=${base_hash}" \
-        --label "vllm.rocm_base.content_files_hash=${content_files_hash}" \
-        --label "vllm.rocm_base.dockerfile=${DOCKERFILE}" \
-        --label "vllm.rocm_base.image.descriptive=${descriptive_tag}" \
-        --label "vllm.rocm_base.image.stable=${stable_tag}" \
-        --label "vllm.rocm_base.git_commit=${BUILDKITE_COMMIT:-}" \
-        --label "vllm.rocm_base.stable_branch=${ROCM_BASE_STABLE_BRANCH:-main}" \
-        --label "vllm.rocm_base.descriptor=${descriptor}" \
-        --label "vllm.rocm_base.dependency_summary=${dependency_summary}" \
-        --label "vllm.rocm_base.base_image=${base_image_arg}" \
-        --label "vllm.rocm_base.base_image_digest=${base_image_digest}" \
-        --label "vllm.rocm_base.dependency.rocm=${rocm_version}" \
-        --label "vllm.rocm_base.dependency.python=${python_version_arg}" \
-        --label "vllm.rocm_base.dependency.pytorch=${pytorch_arg}" \
-        --label "vllm.rocm_base.dependency.torchvision=${pytorch_vision_arg}" \
-        --label "vllm.rocm_base.dependency.torchaudio=${pytorch_audio_arg}" \
-        --label "vllm.rocm_base.dependency.triton=${triton_arg}" \
-        --label "vllm.rocm_base.dependency.flash_attention=${fa_arg}" \
-        --label "vllm.rocm_base.dependency.aiter=${aiter_arg}" \
-        --label "vllm.rocm_base.dependency.mori=${mori_arg}" \
-        --label "vllm.rocm_base.pytorch_rocm_arch=${pytorch_rocm_arch_arg}" \
-        "${tags[@]}" \
-        --push \
-        .
-
-    docker buildx imagetools inspect "${descriptive_tag}" >/dev/null
-
-    metadata_set "rocm-base-refresh" "1"
-    metadata_set "rocm-base-image" "${descriptive_tag}"
-    metadata_set "rocm-base-image-descriptive" "${descriptive_tag}"
-    metadata_set "rocm-base-image-stable" "${stable_tag}"
-    metadata_set "rocm-base-image-ci-descriptive" "${ci_descriptive_tag}"
-    metadata_set "rocm-base-metadata-version" "${metadata_version}"
-    metadata_set "rocm-base-content-hash" "${base_hash}"
-    metadata_set "rocm-base-content-files-hash" "${content_files_hash}"
-    metadata_set "rocm-base-content-files" "${content_files}"
-    metadata_set "rocm-base-content-args" "${content_args}"
-    metadata_set "rocm-base-base-image-digest" "${base_image_digest}"
-    metadata_set "rocm-base-dockerfile" "${DOCKERFILE}"
-    metadata_set "rocm-base-descriptor" "${descriptor}"
-    metadata_set "rocm-base-dependency-summary" "${dependency_summary}"
-    metadata_set "rocm-base-dependency-rocm" "${rocm_version}"
-    metadata_set "rocm-base-dependency-python" "${python_version_arg}"
-    metadata_set "rocm-base-dependency-pytorch" "${pytorch_arg}"
-    metadata_set "rocm-base-dependency-torchvision" "${pytorch_vision_arg}"
-    metadata_set "rocm-base-dependency-torchaudio" "${pytorch_audio_arg}"
-    metadata_set "rocm-base-dependency-triton" "${triton_arg}"
-    metadata_set "rocm-base-dependency-flash-attention" "${fa_arg}"
-    metadata_set "rocm-base-dependency-aiter" "${aiter_arg}"
-    metadata_set "rocm-base-dependency-mori" "${mori_arg}"
-    metadata_set "rocm-base-pytorch-rocm-arch" "${pytorch_rocm_arch_arg}"
-    metadata_set "rocm-ci-image-descriptive" "${ci_descriptive_tag}"
-
-    echo "--- :white_check_mark: ROCm base image published"
-    echo "Use BASE_IMAGE=${descriptive_tag} for downstream ROCm CI builds"
-}
-
-main() {
-    metadata_set "rocm-base-refresh" "0"
-
-    if ! rocm_base_changed; then
-        echo "ROCm base Dockerfile did not change; skipping base image refresh"
-        return 0
-    fi
-
-    setup_builder
-    build_base_image
-}
-
-main "$@"
@@ -1,32 +0,0 @@
-#!/usr/bin/env bash
-# Fast structural smoke test for the full ROCm CI image.
-
-set -euo pipefail
-
-image_ref="${VLLM_CI_SMOKE_IMAGE:-rocm/vllm-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT is required}}"
-
-docker run --rm --network=none --entrypoint /bin/bash "${image_ref}" -ec '
-  if [ ! -d /vllm-workspace ]; then echo Missing directory: /vllm-workspace >&2; exit 1; fi
-  if [ ! -d /vllm-workspace/tests ]; then echo Missing directory: /vllm-workspace/tests >&2; exit 1; fi
-  if [ ! -d /vllm-workspace/src/vllm ]; then echo Missing directory: /vllm-workspace/src/vllm >&2; exit 1; fi
-  if [ ! -x /vllm-workspace/src/vllm/vllm-rs ]; then echo Missing executable: /vllm-workspace/src/vllm/vllm-rs >&2; exit 1; fi
-
-  command -v python3
-  command -v uv
-  command -v pytest
-
-  if ! command -v amd-smi >/dev/null 2>&1 && ! command -v rocminfo >/dev/null 2>&1; then
-    echo No ROCm CLI found in image >&2
-    exit 1
-  fi
-
-  python3 - <<PY
-import torch
-import vllm
-
-print(torch.__version__)
-print(vllm.__version__)
-PY
-
-  echo AMD image smoke OK
-'
@@ -109,9 +109,7 @@ run_nodes() {
        if [ "$node" -ne 0 ]; then
            docker exec -d "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
        else
-            # Allocate a TTY (-t -i) for the foreground head node so its output
-            # keeps ANSI color in the Buildkite log (see run-amd-test.sh).
-            docker exec -t -i "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
+            docker exec "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
        fi
    done
 }
@@ -8,12 +8,7 @@ if [[ "$MODE" != "style-clippy" && "$MODE" != "test" ]]; then
  exit 2
 fi

-if ROOT_DIR="$(git rev-parse --show-toplevel 2>/dev/null)"; then
-  :
-else
-  SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd -P)"
-  ROOT_DIR="$(cd -- "${SCRIPT_DIR}/../.." && pwd -P)"
-fi
+ROOT_DIR="$(git rev-parse --show-toplevel)"
 cd "$ROOT_DIR"

 export CARGO_TERM_COLOR="${CARGO_TERM_COLOR:-always}"
@@ -21,20 +16,16 @@ export CARGO_HOME="${CARGO_HOME:-$HOME/.cargo}"
 export RUSTUP_HOME="${RUSTUP_HOME:-$HOME/.rustup}"
 export PATH="$CARGO_HOME/bin:$PATH"

-PROTOC_VERSION="${PROTOC_VERSION:-31.1}"
-CARGO_BINSTALL_VERSION="${CARGO_BINSTALL_VERSION:-1.20.1}"
-UV_VERSION="${UV_VERSION:-0.11.28}"
-PYO3_PYTHON_VERSION="${PYO3_PYTHON_VERSION:-3.12}"
-
-CARGO_SORT_VERSION_REQ="${CARGO_SORT_VERSION_REQ:-2}"
-CARGO_DENY_VERSION_REQ="${CARGO_DENY_VERSION_REQ:-0.20}"
-CARGO_NEXTEST_VERSION_REQ="${CARGO_NEXTEST_VERSION_REQ:-0.9}"
-
 log_section() {
  echo "--- $*"
 }

 install_protoc() {
+  if command -v protoc >/dev/null 2>&1; then
+    return
+  fi
+
+  local version="${PROTOC_VERSION:-31.1}"
  local arch
  case "$(uname -m)" in
    x86_64)
@@ -49,17 +40,16 @@ install_protoc() {
      ;;
  esac

-  local url="https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-${arch}.zip"
+  local url="https://github.com/protocolbuffers/protobuf/releases/download/v${version}/protoc-${version}-linux-${arch}.zip"
  local tmp_dir
  tmp_dir="$(mktemp -d)"

-  log_section "Installing protoc ${PROTOC_VERSION}"
+  log_section "Installing protoc ${version}"
  curl -L --proto '=https' --tlsv1.2 -sSf "$url" -o "$tmp_dir/protoc.zip"
  mkdir -p "$CARGO_HOME/bin"
  unzip -q "$tmp_dir/protoc.zip" bin/protoc 'include/*' -d "$CARGO_HOME"
  chmod +x "$CARGO_HOME/bin/protoc"
  rm -rf "$tmp_dir"
-  protoc --version
 }

 rust_toolchain() {
@@ -80,48 +70,66 @@ install_rust_toolchain() {
 }

 install_cargo_binstall() {
-  log_section "Installing cargo-binstall ${CARGO_BINSTALL_VERSION}"
+  if command -v cargo-binstall >/dev/null 2>&1; then
+    return
+  fi
+
+  log_section "Installing cargo-binstall"
  curl -L --proto '=https' --tlsv1.2 -sSf \
-    "https://raw.githubusercontent.com/cargo-bins/cargo-binstall/v${CARGO_BINSTALL_VERSION}/install-from-binstall-release.sh" \
-    | env BINSTALL_VERSION="$CARGO_BINSTALL_VERSION" bash
-  cargo-binstall -V
+    https://raw.githubusercontent.com/cargo-bins/cargo-binstall/main/install-from-binstall-release.sh \
+    | bash
 }

 install_cargo_sort() {
-  log_section "Installing cargo-sort ${CARGO_SORT_VERSION_REQ}"
-  cargo binstall --no-confirm --force "cargo-sort@${CARGO_SORT_VERSION_REQ}"
+  if command -v cargo-sort >/dev/null 2>&1; then
+    return
+  fi
+
+  log_section "Installing cargo-sort"
+  install_cargo_binstall
+  cargo binstall --no-confirm cargo-sort
 }

 install_cargo_deny() {
-  log_section "Installing cargo-deny ${CARGO_DENY_VERSION_REQ}"
-  cargo binstall --no-confirm --force "cargo-deny@${CARGO_DENY_VERSION_REQ}"
+  if command -v cargo-deny >/dev/null 2>&1; then
+    return
+  fi
+
+  log_section "Installing cargo-deny"
+  install_cargo_binstall
+  cargo binstall --no-confirm cargo-deny
 }

 install_cargo_nextest() {
-  log_section "Installing cargo-nextest ${CARGO_NEXTEST_VERSION_REQ}"
-  cargo binstall \
-    --no-confirm \
-    --force \
-    --secure \
-    "cargo-nextest@${CARGO_NEXTEST_VERSION_REQ}"
+  if command -v cargo-nextest >/dev/null 2>&1; then
+    return
+  fi
+
+  log_section "Installing cargo-nextest"
+  install_cargo_binstall
+  cargo binstall --no-confirm --secure cargo-nextest
 }

 install_uv() {
-  log_section "Installing uv ${UV_VERSION}"
-  curl -L --proto '=https' --tlsv1.2 -sSf \
-    "https://github.com/astral-sh/uv/releases/download/${UV_VERSION}/uv-installer.sh" \
+  if command -v uv >/dev/null 2>&1; then
+    return
+  fi
+
+  log_section "Installing uv"
+  curl -LsSf --proto '=https' --tlsv1.2 https://astral.sh/uv/install.sh \
    | env UV_INSTALL_DIR="$CARGO_HOME/bin" sh
-  uv --version
 }

 setup_pyo3_python() {
-  log_section "Installing Python ${PYO3_PYTHON_VERSION} for PyO3 tests"
-  uv python install "$PYO3_PYTHON_VERSION"
+  local python_version="${PYO3_PYTHON_VERSION:-3.12}"
+
+  log_section "Installing Python ${python_version} for PyO3 tests"
+  uv python install "$python_version"
  PYO3_PYTHON="$(uv python find \
    --managed-python \
    --no-project \
    --resolve-links \
-    "$PYO3_PYTHON_VERSION")"
+    "$python_version")"
  export PYO3_PYTHON

  local python_libdir
@@ -143,7 +151,6 @@ PY
 }

 run_style_clippy() {
-  install_cargo_binstall
  install_cargo_sort
  install_cargo_deny

@@ -156,8 +163,8 @@ run_style_clippy() {
  log_section "Checking Rust dependency bans"
  cargo deny \
    --manifest-path rust/Cargo.toml \
-    --config rust/deny.toml \
    check \
+    --config rust/deny.toml \
    bans

  log_section "Running clippy"
@@ -174,7 +181,6 @@ run_style_clippy() {
 run_tests() {
  install_uv
  setup_pyo3_python
-  install_cargo_binstall
  install_cargo_nextest

  log_section "Running cargo nextest"
@@ -23,7 +23,6 @@ NC='\033[0m' # No Color
 # Default configuration
 PIPELINE="ci"
 DRY_RUN=true
-TORCH_NIGHTLY=false

 usage() {
    cat <<EOF
@@ -35,14 +34,12 @@ Sets RUN_ALL=1 and NIGHTLY=1 environment variables.
 SAFETY: Dry-run by default. Use --execute to actually trigger a build.

 Options:
-    --execute        Actually trigger the build (default: dry-run)
-    --pipeline       Buildkite pipeline slug (default: ${PIPELINE})
-    --commit         Override commit SHA (default: current HEAD)
-    --branch         Override branch name (default: current branch)
-    --message        Custom build message (default: auto-generated)
-    --torch-nightly  Also build and run the full suite against torch nightly
-                     (sets TORCH_NIGHTLY=1)
-    --help           Show this help message
+    --execute       Actually trigger the build (default: dry-run)
+    --pipeline      Buildkite pipeline slug (default: ${PIPELINE})
+    --commit        Override commit SHA (default: current HEAD)
+    --branch        Override branch name (default: current branch)
+    --message       Custom build message (default: auto-generated)
+    --help          Show this help message

 Prerequisites:
    - bk CLI installed: brew tap buildkite/buildkite && brew install buildkite/buildkite/bk
@@ -52,7 +49,6 @@ Examples:
    $(basename "$0")                        # Dry-run, show what would happen
    $(basename "$0") --execute              # Actually trigger the build
    $(basename "$0") --pipeline ci-shadow   # Dry-run with different pipeline
-    $(basename "$0") --torch-nightly        # Dry-run a full torch-nightly run
 EOF
    exit 1
 }
@@ -100,10 +96,6 @@ while [[ $# -gt 0 ]]; do
            MESSAGE="$2"
            shift 2
            ;;
-        --torch-nightly)
-            TORCH_NIGHTLY=true
-            shift
-            ;;
        --help|-h)
            usage
            ;;
@@ -179,17 +171,11 @@ if [[ $(echo "$REMOTE_BRANCHES" | wc -l) -gt 5 ]]; then
 fi
 echo ""

-# Environment variables passed to the build.
-BUILD_ENV=("RUN_ALL=1" "NIGHTLY=1")
-if [[ "$TORCH_NIGHTLY" == true ]]; then
-    BUILD_ENV+=("TORCH_NIGHTLY=1")
-fi
-
 log_info "Pipeline: ${PIPELINE}"
 log_info "Branch: ${BRANCH}"
 log_info "Commit: ${COMMIT}"
 log_info "Message: ${MESSAGE}"
-log_info "Environment: ${BUILD_ENV[*]}"
+log_info "Environment: RUN_ALL=1, NIGHTLY=1"
 echo ""

 # Build the command
@@ -201,10 +187,9 @@ CMD=(bk build create
    --commit "${COMMIT}"
    --branch "${BRANCH}"
    --message "${MESSAGE}"
+    --env "RUN_ALL=1"
+    --env "NIGHTLY=1"
 )
-for env_var in "${BUILD_ENV[@]}"; do
-    CMD+=(--env "${env_var}")
-done

 if [[ "$DRY_RUN" == true ]]; then
    echo "=========================================="
@@ -225,14 +210,8 @@ if [[ "$DRY_RUN" == true ]]; then
    echo "    --commit '$(escape_for_shell "${COMMIT}")' \\"
    echo "    --branch '$(escape_for_shell "${BRANCH}")' \\"
    echo "    --message '$(escape_for_shell "${MESSAGE}")' \\"
-    last_idx=$(( ${#BUILD_ENV[@]} - 1 ))
-    for i in "${!BUILD_ENV[@]}"; do
-        if [[ $i -eq $last_idx ]]; then
-            echo "    --env '$(escape_for_shell "${BUILD_ENV[$i]}")'"
-        else
-            echo "    --env '$(escape_for_shell "${BUILD_ENV[$i]}")' \\"
-        fi
-    done
+    echo "    --env 'RUN_ALL=1' \\"
+    echo "    --env 'NIGHTLY=1'"
    echo ""
    echo "=========================================="
    echo -e "${YELLOW}To actually trigger this build, run:${NC}"
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-REGISTRY="public.ecr.aws/q9t5s3a7"
-REPO="vllm-release-repo"
-ARCH_TAG="${BUILDKITE_COMMIT}-$(uname -m)-xpu"
-PLATFORM_TAG="${BUILDKITE_COMMIT}-xpu"
-
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin ${REGISTRY}
-docker manifest rm ${REGISTRY}/${REPO}:${PLATFORM_TAG} || true
-docker manifest create ${REGISTRY}/${REPO}:${PLATFORM_TAG} ${REGISTRY}/${REPO}:${ARCH_TAG} --amend
-docker manifest push ${REGISTRY}/${REPO}:${PLATFORM_TAG}
@@ -1,21 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-ORIG_TAG_NAME="$BUILDKITE_COMMIT"
-REPO="vllm/vllm-openai-xpu"
-
-echo "Pushing original XPU tag ${ORIG_TAG_NAME}-xpu to nightly tags in ${REPO}"
-
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64-xpu
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64-xpu ${REPO}:nightly-x86_64
-docker push ${REPO}:nightly-x86_64
-
-docker manifest rm ${REPO}:nightly || true
-docker manifest rm ${REPO}:nightly-"$BUILDKITE_COMMIT" || true
-docker manifest create ${REPO}:nightly ${REPO}:nightly-x86_64 --amend
-docker manifest create ${REPO}:nightly-"$BUILDKITE_COMMIT" ${REPO}:nightly-x86_64 --amend
-docker manifest push ${REPO}:nightly
-docker manifest push ${REPO}:nightly-"$BUILDKITE_COMMIT"
@@ -275,7 +275,7 @@ steps:
  - pytest -v -s v1/cudagraph/test_cudagraph_mode.py

 - label: e2e Core (1 GPU) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 35
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
  agent_pool: mi250_1
  optional: true
@@ -380,7 +380,6 @@ steps:
  - tests/test_outputs.py
  - tests/test_pooling_params.py
  - tests/test_ray_env.py
-  - tests/test_sampling_params.py
  - tests/multimodal
  - tests/renderers
  - tests/standalone_tests/lazy_imports.py
@@ -396,7 +395,6 @@ steps:
  - pytest -v -s test_outputs.py
  - pytest -v -s test_pooling_params.py
  - pytest -v -s test_ray_env.py
-  - pytest -v -s test_sampling_params.py
  - pytest -v -s -m 'cpu_test' multimodal
  - pytest -v -s renderers
  - pytest -v -s tokenizers_
@@ -406,40 +404,10 @@ steps:
  - pytest -v -s transformers_utils
  - pytest -v -s config

-#------------------------------------------------------------  mi250 · rust  -----------------------------------------------------------#
-
- label: Rust Frontend Cargo Style + Clippy # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  no_gpu: true
-  working_dir: "/vllm-workspace"
-  source_file_dependencies:
-  - rust/
-  - rust-toolchain.toml
-  - .buildkite/test_areas/rust_frontend_cargo.yaml
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh
-  commands:
-  - bash .buildkite/scripts/run-rust-frontend-cargo-ci.sh style-clippy
-
- label: Rust Frontend Cargo Tests # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  no_gpu: true
-  working_dir: "/vllm-workspace"
-  source_file_dependencies:
-  - rust/
-  - rust-toolchain.toml
-  - .buildkite/test_areas/rust_frontend_cargo.yaml
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh
-  commands:
-  - bash .buildkite/scripts/run-rust-frontend-cargo-ci.sh test
-
 #-----------------------------------------------------------  mi250 · docker  ----------------------------------------------------------#

 - label: Docker Build Metadata (ROCm) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
  agent_pool: mi250_1
  no_gpu: true
@@ -467,7 +435,7 @@ steps:
 #-----------------------------------------------------  mi300 · basic_correctness  -----------------------------------------------------#

 - label: Basic Correctness # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 95
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  fast_check: true
@@ -484,7 +452,7 @@ steps:
  - pytest -v -s basic_correctness/test_cpu_offload.py

 - label: Distributed Model Tests (2 GPUs) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 110
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_2
  num_gpus: 2
@@ -504,7 +472,7 @@ steps:
  commands:
  - TARGET_TEST_SUITE=MI300 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
-  - pytest models/transformers/test_backend.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
  - pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
@@ -724,7 +692,7 @@ steps:
  - pytest -v -s distributed/test_eplb_spec_decode.py

 - label: Distributed Tests (2xH100-2xMI300) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_2
  num_gpus: 2
@@ -1057,23 +1025,6 @@ steps:
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt

- label: MRCR Eval Small Models # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  - tests/evals/mrcr/
-  commands:
-  - pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt
-
 - label: LM Eval Small Models (MI300) # TBD
  timeout_in_minutes: 180
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -1297,7 +1248,7 @@ steps:
 #---------------------------------------------------------  mi300 · examples  ----------------------------------------------------------#

 - label: Examples # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  optional: true
@@ -1332,23 +1283,8 @@ steps:

 #----------------------------------------------------------  mi300 · kernels  ----------------------------------------------------------#

- label: vLLM IR Tests # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/"
-  source_file_dependencies:
-  - vllm/ir
-  - vllm/kernels
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s tests/ir
-  - pytest -v -s tests/kernels/ir
-
 - label: Kernels Attention Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 100
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  optional: true
@@ -1381,23 +1317,8 @@ steps:
  commands:
  - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py

- label: Kernels KDA Test # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/model_executor/layers/fla/ops/kda.py
-  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
-  - vllm/model_executor/layers/fla/ops/l2norm.py
-  - tests/kernels/test_kda.py
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s kernels/test_kda.py
-
 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 95
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  optional: true
@@ -1508,135 +1429,13 @@ steps:
  - pytest -v -s model_executor -m '(not slow_test)'
  - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py

-#----------------------------------------------------  mi300 · model_runner_v2  -------------------------------------------------------#
-
- label: Model Runner V2 Core Tests # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/worker/gpu/
-  - vllm/v1/worker/gpu_worker.py
-  - vllm/v1/core/sched/
-  - vllm/v1/attention/
-  - tests/v1/engine/test_llm_engine.py
-  - tests/v1/e2e/
-  - tests/entrypoints/llm/test_struct_output_generate.py
-  - vllm/platforms/rocm.py
-  commands:
-  - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
-  - ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
-  - pytest -v -s v1/e2e/general/test_context_length.py
-  - pytest -v -s v1/e2e/general/test_min_tokens.py
-  - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
-
- label: Model Runner V2 Examples # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/examples"
-  source_file_dependencies:
-  - vllm/v1/worker/gpu/
-  - vllm/v1/core/sched/
-  - vllm/v1/worker/gpu_worker.py
-  - examples/basic/offline_inference/
-  - examples/generate/multimodal/
-  - examples/features/
-  - examples/pooling/embed/vision_embedding_offline.py
-  - examples/features/tensorize_vllm_model.py
-  - examples/deployment/llm_engine_example.py
-  - vllm/platforms/rocm.py
-  commands:
-  - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - pip install tensorizer
-  - python3 basic/offline_inference/chat.py
-  - python3 basic/offline_inference/generate.py --model facebook/opt-125m
-  - python3 generate/multimodal/audio_language_offline.py --seed 0
-  - python3 generate/multimodal/vision_language_offline.py --seed 0
-  - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-  - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
-  - python3 pooling/embed/vision_embedding_offline.py --seed 0
-  - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-  - python3 deployment/llm_engine_example.py
-  - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
-
- label: Model Runner V2 Distributed (2 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_2
-  num_gpus: 2
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/worker/gpu/
-  - vllm/v1/worker/gpu_worker.py
-  - tests/basic_correctness/test_basic_correctness.py
-  - tests/v1/distributed/test_async_llm_dp.py
-  - tests/v1/distributed/test_eagle_dp.py
-  - vllm/platforms/rocm.py
-  commands:
-  - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - TARGET_TEST_SUITE=MI300 pytest -v -s basic_correctness/test_basic_correctness.py -m 'distributed(num_gpus=2)' -k "not ray and not True"
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py -k "not ray"
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-
- label: Model Runner V2 Pipeline Parallelism (4 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_4
-  num_gpus: 4
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/worker/gpu/
-  - vllm/v1/worker/gpu_worker.py
-  - tests/distributed/test_pipeline_parallel.py
-  - tests/distributed/test_pp_cudagraph.py
-  - vllm/platforms/rocm.py
-  commands:
-  - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - pytest -v -s distributed/test_pipeline_parallel.py -k "not ray and not Jamba"
-  - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
-
- label: Model Runner V2 Spec Decode # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/v1/worker/gpu/
-  - vllm/v1/worker/gpu_worker.py
-  - tests/v1/spec_decode/test_max_len.py
-  - tests/v1/spec_decode/test_rejection_sampler_utils.py
-  - tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
-  - tests/v1/e2e/spec_decode/test_spec_decode.py
-  - vllm/platforms/rocm.py
-  commands:
-  - set -x
-  - export VLLM_USE_V2_MODEL_RUNNER=1
-  - pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
-  - pytest -v -s v1/spec_decode/test_rejection_sampler_utils.py
-  - pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
-  - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"
-
 #------------------------------------------------------  mi300 · models / basic  -------------------------------------------------------#

 - label: Basic Models Tests (Extra Initialization) %N # TBD
  timeout_in_minutes: 180
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
-  parallelism: 6
+  parallelism: 2
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/model_executor/models/
@@ -1661,7 +1460,7 @@ steps:
  - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset

 - label: Basic Models Tests (Other) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  optional: true
@@ -1669,10 +1468,10 @@ steps:
  source_file_dependencies:
  - vllm/
  - tests/models/test_terratorch.py
-  - tests/models/transformers/test_backend.py
+  - tests/models/test_transformers.py
  - tests/models/test_registry.py
  commands:
-  - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
+  - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py

 #-----------------------------------------------------  mi300 · models / language  -----------------------------------------------------#

@@ -1806,7 +1605,7 @@ steps:
  - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model

 - label: Multi-Modal Processor # 1h 42m
-  timeout_in_minutes: 180
+  timeout_in_minutes: 138
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  optional: true
@@ -1892,7 +1691,7 @@ steps:
  - examples/
  commands:
  - pip install --upgrade git+https://github.com/huggingface/transformers
-  - pytest -v -s tests/models/transformers/test_backend.py
+  - pytest -v -s tests/models/test_transformers.py
  - pytest -v -s tests/models/multimodal/test_mapping.py
  - python3 examples/basic/offline_inference/chat.py
  - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
@@ -1938,11 +1737,6 @@ steps:
  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
  - pip uninstall dummy_stat_logger -y
  # END: `stat_logger` plugins test
-  # BEGIN: `endpoint` plugins test
-  - pip install -e ./plugins/vllm_add_dummy_endpoint_plugin
-  - pytest -v -s plugins_tests/test_endpoint_plugins.py
-  - pip uninstall vllm_add_dummy_endpoint_plugin -y
-  # END: `endpoint` plugins test
  # BEGIN: other tests
  - pytest -v -s plugins_tests/test_scheduler_plugins.py
  - pip install -e ./plugins/vllm_add_dummy_model
@@ -1951,144 +1745,6 @@ steps:
  - pytest -v -s plugins_tests/test_oot_registration_offline.py # it needs a clean process
  - pytest -v -s plugins_tests/lora_resolvers # unit tests for in-tree lora resolver plugins

- label: GGUF Plugin # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  soft_fail: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization
-  - tests/plugins_tests/test_gguf_plugin.py
-  - tests/plugins_tests/gguf
-  - vllm/platforms/rocm.py
-  commands:
-  - pip install "vllm-gguf-plugin >= 0.0.2"
-  - pytest -v -s plugins_tests/gguf
-
-#-------------------------------------------------------  mi300 · rust_frontend  -------------------------------------------------------#
-
- label: Rust Frontend OpenAI Coverage # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/benchmarks/
-  - vllm/entrypoints/openai/
-  - vllm/entrypoints/serve/
-  - vllm/v1/sample/
-  - tests/utils.py
-  - tests/benchmarks/test_serve_cli.py
-  - tests/entrypoints/openai/chat_completion/test_chat_completion.py
-  - tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
-  - tests/entrypoints/openai/completion/test_shutdown.py
-  - tests/entrypoints/openai/test_return_token_ids.py
-  - tests/entrypoints/openai/test_uds.py
-  - tests/v1/sample/test_logprobs_e2e.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
-  - pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py -k "not test_invalid_json_schema and not test_invalid_regex"
-  - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not multiple"
-  - pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
-  - pytest -v -s entrypoints/openai/test_return_token_ids.py -k "not test_comparison"
-  - pytest -v -s entrypoints/openai/test_uds.py
-  - pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
-
- label: Rust Frontend Serve Admin Coverage # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - vllm/entrypoints/serve/
-  - vllm/v1/engine/
-  - tests/utils.py
-  - tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
-  - tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
-  - tests/entrypoints/serve/instrumentator/test_basic.py
-  - tests/entrypoints/serve/instrumentator/test_metrics.py
-  - tests/entrypoints/serve/tokenize/test_tokenization.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
-  - pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
-  - pytest -v -s entrypoints/scale_out/token_in_token_out/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
-  - pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
-  - pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info"
-
- label: Rust Frontend Core Correctness # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - tests/utils.py
-  - tests/entrypoints/openai/correctness/test_lmeval.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
- label: Rust Frontend Tool Use # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - vllm/tool_parsers/
-  - tests/utils.py
-  - tests/tool_use/
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
-
- label: Rust Frontend Distributed # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_4
-  num_gpus: 4
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/utils.py
-  - tests/v1/distributed/test_external_lb_dp.py
-  - tests/v1/distributed/test_hybrid_lb_dp.py
-  - tests/v1/distributed/test_internal_lb_dp.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py -k "not 4 and not server_info"
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py -k "not 4 and not server_info"
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py -k "not 4 and not server_info"
-
 #-------------------------------------------------------  mi300 · quantization  --------------------------------------------------------#

 - label: Quantization # TBD
@@ -2255,7 +1911,7 @@ steps:
  - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"

 - label: Spec Decode Eagle # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_1
  optional: true
@@ -2407,7 +2063,7 @@ steps:
  - pytest -v -s v1/worker
  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
  - pytest -v -s -m 'not cpu_test' v1/metrics
-  - pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
+  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
  # - export HSA_NO_SCRATCH_RECLAIM=1
  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine

@@ -2472,7 +2128,7 @@ steps:
  - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py

 - label: Metrics, Tracing (2 GPUs) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 65
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_2
  optional: true
@@ -2598,59 +2254,6 @@ steps:
  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
  - HYBRID_SSM=1 ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh

- label: Hybrid SSM NixlConnector PD prefix cache test (2 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_2
-  num_gpus: 2
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-  - vllm/v1/core/sched/
-  - vllm/v1/core/kv_cache_coordinator.py
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
-  commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/run_mamba_prefix_cache_test.sh
-
- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_2
-  num_gpus: 2
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-  - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
-  commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
-
- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_2
-  num_gpus: 2
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-  - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-  - tests/v1/kv_connector/nixl_integration/
-  - vllm/platforms/rocm.py
-  commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
-
 - label: V1 e2e (4 GPUs) # TBD
  timeout_in_minutes: 180
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -2678,7 +2281,7 @@ steps:
 #------------------------------------------------------  mi300 · weight_loading  -------------------------------------------------------#

 - label: Weight Loading Multiple GPU # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_2
  num_gpus: 2
@@ -2690,7 +2293,7 @@ steps:
  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt

 - label: Weight Loading Multiple GPU - Large Models # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
  agent_pool: mi300_2
  num_gpus: 2
@@ -2941,19 +2544,15 @@ steps:
  - vllm/envs.py
  - examples/offline_inference/data_parallel.py
  - tests/distributed/test_context_parallel.py
-  - tests/distributed/test_rocm_aiter_custom_ar.py
  - tests/distributed/test_rocm_quick_reduce.py
  - tests/distributed/test_quick_all_reduce.py
-  - tests/v1/e2e/general/test_rocm_aiter_custom_ar.py
  - tests/v1/distributed/test_dbo.py
  - tests/utils.py
  commands:
  - pytest -v -s tests/distributed/test_context_parallel.py
-  - pytest -v -s tests/distributed/test_rocm_aiter_custom_ar.py
-  - pytest -v -s tests/v1/e2e/general/test_rocm_aiter_custom_ar.py
+  - pytest -v -s tests/v1/distributed/test_dbo.py
  - pytest -v -s tests/distributed/test_rocm_quick_reduce.py
  - pytest -v -s tests/distributed/test_quick_all_reduce.py
-  - pytest -v -s tests/v1/distributed/test_dbo.py

 #--------------------------------------------------------  mi355 · entrypoints  --------------------------------------------------------#

@@ -3089,7 +2688,7 @@ steps:
    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx950.txt

 - label: LM Eval Qwen3-5 Models (B200-MI355) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 120
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_2
  num_gpus: 2
@@ -3171,7 +2770,7 @@ steps:
 #---------------------------------------------------------  mi355 · examples  ----------------------------------------------------------#

 - label: Examples # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  working_dir: "/vllm-workspace/examples"
@@ -3206,7 +2805,7 @@ steps:
 #----------------------------------------------------------  mi355 · kernels  ----------------------------------------------------------#

 - label: Kernels (B200-MI355) # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 15
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  working_dir: "/vllm-workspace/"
@@ -3231,7 +2830,7 @@ steps:
  - pytest -v -s tests/kernels/attention/test_rocm_aiter_mla_decode_metadata.py

 - label: Kernels Attention Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 100
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  parallelism: 2
@@ -3248,7 +2847,7 @@ steps:
  - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT

 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  parallelism: 5
@@ -3512,7 +3111,7 @@ steps:
  - pytest -v -s v1/attention

 - label: V1 Core + KV + Metrics # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  optional: true
@@ -3534,11 +3133,11 @@ steps:
  - pytest -v -s v1/worker
  - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
  - pytest -v -s -m 'not cpu_test' v1/metrics
-  - pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
+  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine

 - label: V1 Sample + Logits # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  optional: true
@@ -3558,7 +3157,7 @@ steps:
  - pytest -v -s v1/test_outputs.py

 - label: V1 Spec Decode # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_1
  working_dir: "/vllm-workspace/tests"
@@ -3571,7 +3170,7 @@ steps:
 #------------------------------------------------------  mi355 · weight_loading  -------------------------------------------------------#

 - label: Weight Loading Multiple GPU # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_2
  num_gpus: 2
@@ -3583,7 +3182,7 @@ steps:
  - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt

 - label: Weight Loading Multiple GPU - Large Models # TBD
-  timeout_in_minutes: 180
+  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
  agent_pool: mi355_2
  working_dir: "/vllm-workspace/tests"
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: V1 attention (H100-MI300)
  key: v1-attention-h100-mi300
-  timeout_in_minutes: 85
+  timeout_in_minutes: 30
  device: h100
  source_file_dependencies:
    - vllm/config/attention.py
@@ -12,12 +12,11 @@ steps:
    - vllm/v1/attention
    - tests/v1/attention
  commands:
-    - pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s v1/attention
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 95
+      timeout_in_minutes: 70
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -31,7 +30,7 @@ steps:

 - label: V1 attention (B200)
  key: v1-attention-b200
-  timeout_in_minutes: 80
+  timeout_in_minutes: 30
  device: b200-k8s
  source_file_dependencies:
    - vllm/config/attention.py
@@ -39,5 +38,4 @@ steps:
    - vllm/v1/attention
    - tests/v1/attention
  commands:
-    - pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s v1/attention
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
  key: basic-correctness
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -19,6 +19,6 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 70
+      timeout_in_minutes: 50
      depends_on:
      - image-build-amd
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Benchmarks CLI Test
  key: benchmarks-cli-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -23,7 +23,7 @@ steps:
  num_gpus: 2
  optional: true
  working_dir: "/vllm-workspace/"
-  timeout_in_minutes: 20
+  timeout_in_minutes: 10
  source_file_dependencies:
  - benchmarks/attention_benchmarks/
  - vllm/v1/attention/
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
  key: sequence-parallel-correctness-tests-2-gpus
-  timeout_in_minutes: 80
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/"
  num_devices: 2
  source_file_dependencies:
@@ -19,7 +19,7 @@ steps:

 - label: Sequence Parallel Correctness Tests (2xH100)
  key: sequence-parallel-correctness-tests-2xh100
-  timeout_in_minutes: 75
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/"
  device: h100
  optional: true
@@ -30,7 +30,7 @@ steps:

 - label: AsyncTP Correctness Tests (2xH100)
  key: asynctp-correctness-tests-2xh100
-  timeout_in_minutes: 30
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/"
  device: h100
  optional: true
@@ -41,7 +41,7 @@ steps:

 - label: AsyncTP Correctness Tests (B200)
  key: asynctp-correctness-tests-b200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/"
  device: b200-k8s
  optional: true
@@ -52,7 +52,7 @@ steps:

 - label: Distributed Compile Unit Tests (2xH100)
  key: distributed-compile-unit-tests-2xh100
-  timeout_in_minutes: 45
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/"
  device: h100
  num_devices: 2
@@ -66,7 +66,7 @@ steps:

 - label: Fusion and Compile Unit Tests (2xB200)
  key: fusion-and-compile-unit-tests-2xb200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/"
  device: b200-k8s
  source_file_dependencies:
@@ -96,7 +96,7 @@ steps:

 - label: Fusion E2E Quick (H100)
  key: fusion-e2e-quick-h100
-  timeout_in_minutes: 25
+  timeout_in_minutes: 15
  working_dir: "/vllm-workspace/"
  device: h100
  num_devices: 1
@@ -115,7 +115,7 @@ steps:

 - label: Fusion E2E Config Sweep (H100)
  key: fusion-e2e-config-sweep-h100
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/"
  device: h100
  num_devices: 1
@@ -149,7 +149,7 @@ steps:

 - label: Fusion E2E TP2 Quick (H100)
  key: fusion-e2e-tp2-quick-h100
-  timeout_in_minutes: 35
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/"
  device: h100
  num_devices: 2
@@ -167,7 +167,7 @@ steps:

 - label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
  key: fusion-e2e-tp2-ar-rms-config-sweep-h100
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
  working_dir: "/vllm-workspace/"
  device: h100
  num_devices: 2
@@ -207,7 +207,7 @@ steps:

 - label: Fusion E2E TP2 (B200)
  key: fusion-e2e-tp2-b200
-  timeout_in_minutes: 45
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/"
  device: b200-k8s
  num_devices: 2
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Platform Tests
  key: platform-tests
-  timeout_in_minutes: 20
+  timeout_in_minutes: 15
  device: h200_18gb
  source_file_dependencies:
  - vllm/envs.py
@@ -19,7 +19,7 @@ steps:

 - label: Cudagraph
  key: cudagraph
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
  source_file_dependencies:
  - tests/v1/cudagraph
  - vllm/v1/cudagraph_dispatcher.py
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Distributed NixlConnector PD accuracy (4 GPUs)
  key: distributed-nixlconnector-pd-accuracy-4-gpus
-  timeout_in_minutes: 55
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -16,7 +16,7 @@ steps:
  mirror:
    amd:
      device: mi300_4
-      timeout_in_minutes: 85
+      timeout_in_minutes: 110
      depends_on:
        - image-build-amd
      source_file_dependencies:
@@ -29,7 +29,7 @@ steps:

 - label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
  key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
-  timeout_in_minutes: 55
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -39,19 +39,6 @@ steps:
    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
    - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh

- label: Push NixlConnector PP prefill PD accuracy (4 GPUs)
-  key: push-nixlconnector-pp-prefill-pd-accuracy-4-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - tests/v1/kv_connector/nixl_integration/
-    - tests/v1/kv_connector/nixl_push_integration/
-  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
-    - bash v1/kv_connector/nixl_push_integration/config_sweep_accuracy_test.sh
-
 - label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
  key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
  timeout_in_minutes: 30
@@ -66,7 +53,7 @@ steps:
  mirror:
    amd:
      device: mi300_4
-      timeout_in_minutes: 60
+      timeout_in_minutes: 50
      depends_on:
        - image-build-amd
      source_file_dependencies:
@@ -79,7 +66,7 @@ steps:

 - label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
  key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 55
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -91,7 +78,7 @@ steps:
  mirror:
    amd:
      device: mi300_4
-      timeout_in_minutes: 85
+      timeout_in_minutes: 110
      depends_on:
        - image-build-amd
      source_file_dependencies:
@@ -104,7 +91,7 @@ steps:

 - label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
  key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -116,7 +103,7 @@ steps:
  mirror:
    amd:
      device: mi300_4
-      timeout_in_minutes: 80
+      timeout_in_minutes: 60
      depends_on:
        - image-build-amd
      source_file_dependencies:
@@ -143,7 +130,7 @@ steps:

 - label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -158,7 +145,7 @@ steps:

 - label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
  key: nixlconnector-pd-spec-decode-acceptance-2-gpus
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
  device: a100
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
@@ -172,7 +159,7 @@ steps:
  mirror:
    amd:
      device: mi300_2
-      timeout_in_minutes: 70
+      timeout_in_minutes: 60
      depends_on:
        - image-build-amd
      source_file_dependencies:
@@ -186,7 +173,7 @@ steps:

 - label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
  key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Distributed Comm Ops
  key: distributed-comm-ops
-  timeout_in_minutes: 25
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -18,7 +18,7 @@ steps:

 - label: Distributed DP Tests (2 GPUs)
  key: distributed-dp-tests-2-gpus
-  timeout_in_minutes: 35
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -55,7 +55,7 @@ steps:

 - label: Distributed Compile + RPC Tests (2 GPUs)
  key: distributed-compile-rpc-tests-2-gpus
-  timeout_in_minutes: 65
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -78,7 +78,7 @@ steps:

 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
  key: distributed-torchrun-shutdown-tests-2-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -133,7 +133,7 @@ steps:

 - label: Distributed DP Tests (4 GPUs)
  key: distributed-dp-tests-4-gpus
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -154,7 +154,7 @@ steps:

 - label: Distributed Compile + Comm (4 GPUs)
  key: distributed-compile-comm-4-gpus
-  timeout_in_minutes: 70
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -176,7 +176,7 @@ steps:

 - label: Distributed Tests (8xH100)
  key: distributed-tests-8xh100
-  timeout_in_minutes: 20
+  timeout_in_minutes: 10
  device: h100
  num_devices: 8
  working_dir: "/vllm-workspace/tests"
@@ -212,7 +212,7 @@ steps:

 - label: Distributed Tests (2xH100-2xMI300)
  key: distributed-tests-2xh100-2xmi300
-  timeout_in_minutes: 30
+  timeout_in_minutes: 15
  device: h100
  optional: true
  working_dir: "/vllm-workspace/"
@@ -259,7 +259,7 @@ steps:

 - label: Pipeline + Context Parallelism (4 GPUs)
  key: pipeline-context-parallelism-4-gpus
-  timeout_in_minutes: 55
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -274,7 +274,7 @@ steps:

 - label: RayExecutorV2 (4 GPUs)
  key: rayexecutorv2-4-gpus
-  timeout_in_minutes: 45
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -3,7 +3,7 @@ depends_on:
  - image-build-cpu
 steps:
 - label: Docker Build Metadata
-  timeout_in_minutes: 20
+  timeout_in_minutes: 10
  device: cpu-small
  source_file_dependencies:
    - .buildkite/release-pipeline.yaml
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: DeepSeek V2-Lite Sync EPLB Accuracy (4xH100)
  key: deepseek-v2-lite-sync-eplb-accuracy-4xh100
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
  device: h100
  optional: true
  num_devices: 4
@@ -14,7 +14,7 @@ steps:

 - label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (4xH100)
  key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-4xh100
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
  device: h100
  optional: true
  num_devices: 4
@@ -24,7 +24,7 @@ steps:

 - label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (2xB200)
  key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-2xb200
-  timeout_in_minutes: 20
+  timeout_in_minutes: 60
  device: b200-k8s
  optional: true
  num_devices: 2
@@ -34,7 +34,7 @@ steps:

 - label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
  key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
  device: h100
  optional: true
  num_devices: 4
@@ -44,7 +44,7 @@ steps:

 - label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
  key: deepseek-v2-lite-prefetch-offload-accuracy-h100
-  timeout_in_minutes: 20
+  timeout_in_minutes: 60
  device: h100
  optional: true
  num_devices: 1
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Engine
  key: engine
-  timeout_in_minutes: 30
+  timeout_in_minutes: 15
  device: h200_18gb
  source_file_dependencies:
  - vllm/compilation/
@@ -29,13 +29,13 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 50
+      timeout_in_minutes: 60
      depends_on:
      - image-build-amd

 - label: Engine (1 GPU)
  key: engine-1-gpu
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
  source_file_dependencies:
    - vllm/v1/engine/
    - tests/v1/engine/
@@ -45,13 +45,13 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 55
+      timeout_in_minutes: 40
      depends_on:
      - image-build-amd

 - label: e2e Scheduling (1 GPU)
  key: e2e-scheduling-1-gpu
-  timeout_in_minutes: 35
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/
@@ -60,15 +60,15 @@ steps:
    - pytest -v -s v1/e2e/general/test_async_scheduling.py
  mirror:
    amd:
-      device: mi325_1
-      timeout_in_minutes: 70
+      device: mi250_1
+      timeout_in_minutes: 60
      depends_on:
      - image-build-amd

 - label: e2e Core (1 GPU)
  device: h200_35gb
  key: e2e-core-1-gpu
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  source_file_dependencies:
    - vllm/v1/
    - tests/v1/e2e/general/
@@ -76,8 +76,8 @@ steps:
    - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
  mirror:
    amd:
-      device: mi325_1
-      timeout_in_minutes: 60
+      device: mi250_1
+      timeout_in_minutes: 35
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -87,7 +87,7 @@ steps:

 - label: V1 e2e (2 GPUs)
  key: v1-e2e-2-gpus
-  timeout_in_minutes: 25 # TODO: Fix timeout after we have more confidence in the test stability
+  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
  optional: true
  num_devices: 2
  source_file_dependencies:
@@ -120,7 +120,7 @@ steps:

 - label: V1 e2e (4 GPUs)
  key: v1-e2e-4-gpus
-  timeout_in_minutes: 20 # TODO: Fix timeout after we have more confidence in the test stability
+  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
  optional: true
  num_devices: 4
  source_file_dependencies:
@@ -148,7 +148,7 @@ steps:

 - label: V1 e2e (4xH100)
  key: v1-e2e-4xh100
-  timeout_in_minutes: 35
+  timeout_in_minutes: 60
  device: h100
  num_devices: 4
  optional: true
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Entrypoints Unit Tests
  key: entrypoints-unit-tests
-  timeout_in_minutes: 25
+  timeout_in_minutes: 10
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/entrypoints
@@ -16,7 +16,7 @@ steps:

 - label: Entrypoints Integration (LLM)
  key: entrypoints-integration-llm
-  timeout_in_minutes: 60
+  timeout_in_minutes: 40
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -37,7 +37,7 @@ steps:
 - label: Entrypoints Integration (API Server)
  key: entrypoints-integration-api-server
  device: h200_35gb
-  timeout_in_minutes: 50
+  timeout_in_minutes: 130
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -56,7 +56,7 @@ steps:

 - label: Entrypoints Integration (API Server OpenAI - Part 1)
  key: entrypoints-integration-api-server-openai-part-1
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -68,13 +68,13 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 65
+      timeout_in_minutes: 80
      depends_on:
      - image-build-amd

 - label: Entrypoints Integration (API Server OpenAI - Part 2)
  key: entrypoints-integration-api-server-openai-part-2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -109,7 +109,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 65
+      timeout_in_minutes: 60
      depends_on:
      - image-build-amd

@@ -126,7 +126,7 @@ steps:
 - label: Entrypoints Integration (Speech to Text)
  device: h200_35gb
  key: entrypoints-integration-speech_to_text
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -138,7 +138,7 @@ steps:
 - label: Entrypoints Integration (Multimodal)
  device: h200_35gb
  key: entrypoints-integration-multimodal
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -160,7 +160,7 @@ steps:

 - label: OpenAI API Correctness
  key: openai-api-correctness
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
  - csrc/
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: EPLB Algorithm
  key: eplb-algorithm
-  timeout_in_minutes: 20
+  timeout_in_minutes: 15
  device: h200_18gb
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
@@ -27,7 +27,7 @@ steps:

 - label: EPLB Execution # 17min
  key: eplb-execution
-  timeout_in_minutes: 25
+  timeout_in_minutes: 27
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -39,7 +39,7 @@ steps:

 - label: Elastic EP Scaling Test
  key: elastic-ep-scaling-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
  device: h100
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: vLLM IR Tests
  key: vllm-ir-tests
-  timeout_in_minutes: 35
+  timeout_in_minutes: 10
  device: h200_18gb
  working_dir: "/vllm-workspace/"
  source_file_dependencies:
@@ -16,19 +16,18 @@ steps:

 - label: Kernels Core Operation Test
  key: kernels-core-operation-test
-  timeout_in_minutes: 120
+  timeout_in_minutes: 75
  source_file_dependencies:
  - csrc/
  - tests/kernels/core
  - tests/kernels/test_concat_mla_q.py
  - tests/kernels/test_fused_qk_norm_rope_gate.py
  commands:
-    - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 3
+    - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.py

 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
  key: kernels-minimax-reduce-rms-test-2-gpus
-  timeout_in_minutes: 20
+  timeout_in_minutes: 15
  num_devices: 2
  device: h100
  source_file_dependencies:
@@ -42,7 +41,7 @@ steps:

 - label: Deepseek V4 Kernel Test (H100)
  key: deepseek-v4-kernel-test-h100
-  timeout_in_minutes: 30
+  timeout_in_minutes: 15
  device: h100
  source_file_dependencies:
  - csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -55,7 +54,7 @@ steps:

 - label: Deepseek V4 Kernel Test (B200)
  key: deepseek-v4-kernel-test-b200
-  timeout_in_minutes: 20
+  timeout_in_minutes: 15
  device: b200-k8s
  source_file_dependencies:
  - csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -66,7 +65,7 @@ steps:

 - label: Kernels Attention Test %N
  key: kernels-attention-test
-  timeout_in_minutes: 65
+  timeout_in_minutes: 35
  source_file_dependencies:
  - csrc/attention/
  - vllm/v1/attention
@@ -80,7 +79,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 90
+      timeout_in_minutes: 55
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -107,7 +106,7 @@ steps:

 - label: Kernels Quantization Test %N
  key: kernels-quantization-test
-  timeout_in_minutes: 60
+  timeout_in_minutes: 90
  source_file_dependencies:
  - csrc/quantization/
  - vllm/model_executor/layers/quantization
@@ -132,7 +131,7 @@ steps:

 - label: Kernels MoE Test %N
  key: kernels-moe-test
-  timeout_in_minutes: 50
+  timeout_in_minutes: 25
  source_file_dependencies:
  - csrc/quantization/cutlass_w8a8/moe/
  - csrc/moe/
@@ -148,7 +147,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 65
+      timeout_in_minutes: 50
      source_file_dependencies:
      - csrc/quantization/cutlass_w8a8/moe/
      - csrc/moe/
@@ -164,7 +163,7 @@ steps:

 - label: Kernels Mamba Test
  key: kernels-mamba-test
-  timeout_in_minutes: 40
+  timeout_in_minutes: 45
  source_file_dependencies:
  - csrc/mamba/
  - tests/kernels/mamba
@@ -173,7 +172,7 @@ steps:
    - pytest -v -s kernels/mamba

 - label: Kernels KDA Test
-  timeout_in_minutes: 25
+  timeout_in_minutes: 20
  device: h200_18gb
  source_file_dependencies:
  - vllm/model_executor/layers/fla/ops/kda.py
@@ -185,7 +184,7 @@ steps:

 - label: Kernels DeepGEMM Test (H100)
  key: kernels-deepgemm-test-h100
-  timeout_in_minutes: 35
+  timeout_in_minutes: 45
  device: h100
  num_devices: 1
  source_file_dependencies:
@@ -212,7 +211,7 @@ steps:

 - label: Kernels (B200)
  key: kernels-b200
-  timeout_in_minutes: 80
+  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/"
  device: b200-k8s
  # optional: true
@@ -265,20 +264,19 @@ steps:

 - label: Kernels Helion Test
  key: kernels-helion-test
-  timeout_in_minutes: 115
+  timeout_in_minutes: 30
  device: h100
  source_file_dependencies:
  - vllm/utils/import_utils.py
  - tests/kernels/helion/
  commands:
    - pip install helion==1.1.0
-    - pytest -v -s kernels/helion/ --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s kernels/helion/

 
 - label: Kernels FP8 MoE Test (1xH100)
  key: kernels-fp8-moe-test-1xh100
-  timeout_in_minutes: 40
+  timeout_in_minutes: 90
  device: h100
  num_devices: 1
  optional: true
@@ -295,7 +293,7 @@ steps:

 - label: Kernels FP8 MoE Test (2xH100)
  key: kernels-fp8-moe-test-2xh100
-  timeout_in_minutes: 45
+  timeout_in_minutes: 90
  device: h100
  num_devices: 2
  optional: true
@@ -305,7 +303,7 @@ steps:

 - label: Kernels Fp4 MoE Test (B200)
  key: kernels-fp4-moe-test-b200
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
  device: b200-k8s
  num_devices: 1
  optional: true
@@ -318,7 +316,7 @@ steps:

 - label: Kernels FusedMoE Layer Test (2 H100s)
  key: kernels-fusedmoe-layer-test-2-h100s
-  timeout_in_minutes: 30
+  timeout_in_minutes: 90
  device: h100
  num_devices: 2
  source_file_dependencies:
@@ -5,7 +5,7 @@ steps:
 - label: LM Eval Small Models
  device: h200_35gb
  key: lm-eval-small-models
-  timeout_in_minutes: 45
+  timeout_in_minutes: 75
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
@@ -56,7 +56,7 @@ steps:

 - label: LM Eval Small Models (1xB200)
  key: lm-eval-small-models-1xb200
-  timeout_in_minutes: 50
+  timeout_in_minutes: 120
  device: b200-k8s
  optional: true
  source_file_dependencies:
@@ -80,7 +80,7 @@ steps:

 - label: LM Eval Large Models EP (2xB200)
  key: lm-eval-large-models-ep-2xb200
-  timeout_in_minutes: 60
+  timeout_in_minutes: 120
  device: b200-k8s
  optional: true
  num_devices: 2
@@ -92,7 +92,7 @@ steps:

 - label: LM Eval Qwen3.5 Models (2xB200)
  key: lm-eval-qwen3-5-models-2xb200
-  timeout_in_minutes: 45
+  timeout_in_minutes: 120
  device: b200-k8s
  optional: true
  num_devices: 2
@@ -109,7 +109,7 @@ steps:

 - label: LM Eval Large Models (8xH200)
  key: lm-eval-large-models-8xh200
-  timeout_in_minutes: 50
+  timeout_in_minutes: 60
  device: h200
  optional: true
  num_devices: 8
@@ -118,7 +118,7 @@ steps:
  mirror:
    amd:
      device: mi300_8
-      timeout_in_minutes: 60
+      timeout_in_minutes: 180
      depends_on:
      - image-build-amd
      commands:
@@ -150,9 +150,9 @@ steps:
  commands:
    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt

- label: LM Eval Humming f16 (A100 - TEMPORARY)
-  key: lm-eval-humming-f16-a100
-  timeout_in_minutes: 75
+- label: LM Eval Humming (A100 - TEMPORARY)
+  key: lm-eval-humming-a100
+  timeout_in_minutes: 30
  device: a100
  optional: true
  num_devices: 1
@@ -160,29 +160,13 @@ steps:
  - vllm/model_executor/layers/quantization/humming.py
  - vllm/model_executor/layers/quantization/utils/humming_utils.py
  - vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/
-  - vllm/model_executor/kernels/linear/
+  - vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config.txt

- label: LM Eval Humming Act int8 (A100 - TEMPORARY)
-  key: lm-eval-humming-act-a100
-  timeout_in_minutes: 45
-  device: a100
-  optional: true
-  num_devices: 1
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization/humming.py
-  - vllm/model_executor/layers/quantization/utils/humming_utils.py
-  - vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/
-  - vllm/model_executor/kernels/linear/
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-int8.txt
-
- label: LM Eval Humming f16 (H100 - TEMPORARY)
-  key: lm-eval-humming-f16-h100
-  timeout_in_minutes: 70
+- label: LM Eval Humming (H100 - TEMPORARY)
+  key: lm-eval-humming-h100
+  timeout_in_minutes: 30
  device: h100
  optional: true
  num_devices: 1
@@ -190,30 +174,14 @@ steps:
  - vllm/model_executor/layers/quantization/humming.py
  - vllm/model_executor/layers/quantization/utils/humming_utils.py
  - vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/
-  - vllm/model_executor/kernels/linear/
+  - vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config.txt
-
- label: LM Eval Humming Act fp8/int8 (H100 - TEMPORARY)
-  key: lm-eval-humming-act-h100
-  timeout_in_minutes: 70
-  device: h100
-  optional: true
-  num_devices: 1
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization/humming.py
-  - vllm/model_executor/layers/quantization/utils/humming_utils.py
-  - vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/
-  - vllm/model_executor/kernels/linear/
-  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-fp8.txt
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-int8.txt

- label: LM Eval Humming f16 (B200 - TEMPORARY)
-  key: lm-eval-humming-f16-b200
-  timeout_in_minutes: 50
+- label: LM Eval Humming (B200 - TEMPORARY)
+  key: lm-eval-humming-b200
+  timeout_in_minutes: 30
  device: b200-k8s
  optional: true
  num_devices: 1
@@ -221,30 +189,14 @@ steps:
  - vllm/model_executor/layers/quantization/humming.py
  - vllm/model_executor/layers/quantization/utils/humming_utils.py
  - vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/
-  - vllm/model_executor/kernels/linear/
+  - vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config.txt
-
- label: LM Eval Humming Act fp8/int8 (B200 - TEMPORARY)
-  key: lm-eval-humming-act-b200
-  timeout_in_minutes: 50
-  device: b200-k8s
-  optional: true
-  num_devices: 1
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization/humming.py
-  - vllm/model_executor/layers/quantization/utils/humming_utils.py
-  - vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/
-  - vllm/model_executor/kernels/linear/
-  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-fp8.txt
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-int8.txt

 - label: LM Eval TurboQuant KV Cache
  key: lm-eval-turboquant-kv-cache
-  timeout_in_minutes: 55
+  timeout_in_minutes: 75
  device: h200_18gb
  source_file_dependencies:
  - vllm/model_executor/layers/quantization/turboquant/
@@ -256,7 +208,7 @@ steps:

 - label: GPQA Eval (GPT-OSS) (2xH100)
  key: gpqa-eval-gpt-oss-2xh100
-  timeout_in_minutes: 35
+  timeout_in_minutes: 120
  device: h100
  optional: true
  num_devices: 2
@@ -270,7 +222,7 @@ steps:

 - label: GPQA Eval (GPT-OSS) (2xB200)
  key: gpqa-eval-gpt-oss-2xb200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 120
  device: b200-k8s
  optional: true
  num_devices: 2
@@ -284,7 +236,7 @@ steps:

 - label: GPQA Eval (GPT-OSS) (DGX Spark)
  key: gpqa-eval-gpt-oss-spark
-  timeout_in_minutes: 35
+  timeout_in_minutes: 120
  device: dgx-spark
  optional: true
  num_devices: 1
@@ -298,50 +250,9 @@ steps:
    - uv pip install --system 'gpt-oss[eval]==0.0.5'
    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-spark.txt

- label: LM Eval KV-Offload (1xH200)
-  key: kv-offload-small
-  timeout_in_minutes: 30
-  device: h200_35gb
-  source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-  - vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
-  - vllm/v1/kv_offload/
-  - vllm/v1/simple_kv_offload/
-  - tests/evals/gsm8k/test_gsm8k_offloading.py
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "nemotron-h-8b or gemma-4-e4b-it"
-
- label: LM Eval KV-Offload (2xH100)
-  key: kv-offload-medium
-  timeout_in_minutes: 30
-  device: h100
-  num_devices: 2
-  source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-  - vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
-  - vllm/v1/kv_offload/
-  - vllm/v1/simple_kv_offload/
-  - tests/evals/gsm8k/test_gsm8k_offloading.py
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "qwen3.5-35b"
-
- label: LM Eval KV-Offload (4xH100)
-  key: kv-offload-large
-  timeout_in_minutes: 40
-  device: h100
-  num_devices: 4
-  source_file_dependencies:
-  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-  - vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
-  - vllm/v1/kv_offload/
-  - vllm/v1/simple_kv_offload/
-  - tests/evals/gsm8k/test_gsm8k_offloading.py
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "deepseek-v4-flash"
-
 - label: MRCR Eval Small Models
  device: h200_35gb
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
  source_file_dependencies:
  - tests/evals/mrcr/
  commands:
@@ -5,7 +5,7 @@ steps:
 - label: LoRA %N
  device: h200_35gb
  key: lora
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  source_file_dependencies:
  - vllm/lora
  - tests/lora
@@ -16,7 +16,7 @@ steps:
    amd:
      device: mi325_1
      working_dir: "/vllm-workspace/tests"
-      timeout_in_minutes: 65
+      timeout_in_minutes: 60
      source_file_dependencies:
      - vllm/lora
      - tests/lora
@@ -27,7 +27,7 @@ steps:

 - label: LoRA TP (Distributed)
  key: lora-tp-distributed
-  timeout_in_minutes: 60
+  timeout_in_minutes: 30
  num_devices: 4
  source_file_dependencies:
  - vllm/lora
@@ -5,7 +5,7 @@ steps:
 - label: V1 Spec Decode
  device: h200_35gb
  key: v1-spec-decode
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  source_file_dependencies:
    - vllm/config/
    - vllm/distributed/
@@ -24,13 +24,13 @@ steps:
  mirror:
    amd:
      device: mi300_1
-      timeout_in_minutes: 75
+      timeout_in_minutes: 65
      depends_on:
      - image-build-amd

 - label: V1 Sample + Logits
  key: v1-sample-logits
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
    - vllm/config/
@@ -64,7 +64,7 @@ steps:

 - label: V1 Core + KV + Metrics
  key: v1-core-kv-metrics
-  timeout_in_minutes: 60
+  timeout_in_minutes: 30
  source_file_dependencies:
    - vllm/config/
    - vllm/distributed/
@@ -89,7 +89,6 @@ steps:
    - tests/v1/simple_kv_offload
    - tests/v1/worker
    - tests/v1/kv_connector/unit
-    - tests/v1/ec_connector/unit
    - tests/v1/metrics
    - tests/entrypoints/openai/correctness/test_lmeval.py
  commands:
@@ -102,15 +101,14 @@ steps:
    - pytest -v -s v1/simple_kv_offload
    - pytest -v -s v1/worker
    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-    - pytest -v -s -m 'not cpu_test' v1/ec_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/metrics
    # Integration test for streaming correctness (requires special branch).
-    - pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
+    - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 75
+      timeout_in_minutes: 60
      depends_on:
      - image-build-amd

@@ -174,7 +172,7 @@ steps:

 - label: Regression
  key: regression
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
  device: h200_18gb
  source_file_dependencies:
  - vllm/config/
@@ -197,7 +195,7 @@ steps:
 - label: Examples
  device: h200_35gb
  key: examples
-  timeout_in_minutes: 40
+  timeout_in_minutes: 45
  working_dir: "/vllm-workspace/examples"
  source_file_dependencies:
  - vllm/entrypoints
@@ -239,7 +237,7 @@ steps:

 - label: Metrics, Tracing (2 GPUs)
  key: metrics-tracing-2-gpus
-  timeout_in_minutes: 25
+  timeout_in_minutes: 20
  num_devices: 2
  source_file_dependencies:
  - vllm/config/
@@ -283,7 +281,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 45
+      timeout_in_minutes: 20
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -294,7 +292,7 @@ steps:
 - label: Async Engine, Inputs, Utils, Worker
  device: h200_35gb
  key: async-engine-inputs-utils-worker
-  timeout_in_minutes: 25
+  timeout_in_minutes: 50
  source_file_dependencies:
  - vllm/assets/
  - vllm/config/
@@ -321,7 +319,7 @@ steps:
  key: async-engine-inputs-utils-worker-config-cpu
  depends_on:
  - image-build-cpu
-  timeout_in_minutes: 65
+  timeout_in_minutes: 30
  source_file_dependencies:
  - vllm/assets/
  - vllm/config/
@@ -353,7 +351,6 @@ steps:
  - tests/test_outputs.py
  - tests/test_pooling_params.py
  - tests/test_ray_env.py
-  - tests/test_sampling_params.py
  - tests/multimodal
  - tests/renderers
  - tests/standalone_tests/lazy_imports.py
@@ -371,7 +368,6 @@ steps:
  - pytest -v -s test_outputs.py
  - pytest -v -s test_pooling_params.py
  - pytest -v -s test_ray_env.py
-  - pytest -v -s test_sampling_params.py
  - pytest -v -s -m 'cpu_test' multimodal
  - pytest -v -s renderers
  - pytest -v -s reasoning
@@ -383,7 +379,7 @@ steps:

 - label: Batch Invariance (A100)
  key: batch-invariance-a100
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  device: a100
  source_file_dependencies:
    - vllm/v1/attention
@@ -397,7 +393,7 @@ steps:

 - label: Batch Invariance (H100)
  key: batch-invariance-h100
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  device: h100
  source_file_dependencies:
    - vllm/v1/attention
@@ -413,7 +409,7 @@ steps:

 - label: Batch Invariance (B200)
  key: batch-invariance-b200
-  timeout_in_minutes: 35
+  timeout_in_minutes: 30
  device: b200-k8s
  source_file_dependencies:
    - vllm/v1/attention
@@ -432,7 +428,7 @@ steps:
 - label: Acceptance Length Test (Large Models) # optional
  device: h200_35gb
  key: acceptance-length-test-large-models
-  timeout_in_minutes: 20
+  timeout_in_minutes: 25
  gpu: h100
  optional: true
  num_gpus: 1
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Model Executor
  key: model-executor
-  timeout_in_minutes: 45
+  timeout_in_minutes: 35
  source_file_dependencies:
  - vllm/engine/arg_utils.py
  - vllm/config/model.py
@@ -5,7 +5,7 @@ steps:
 - label: Model Runner V2 Core Tests
  device: h200_35gb
  key: model-runner-v2-core-tests
-  timeout_in_minutes: 35
+  timeout_in_minutes: 45
  source_file_dependencies:
  - vllm/v1/worker/gpu/
  - vllm/v1/worker/gpu_worker.py
@@ -27,7 +27,7 @@ steps:
 - label: Model Runner V2 Examples
  device: h200_35gb
  key: model-runner-v2-examples
-  timeout_in_minutes: 35
+  timeout_in_minutes: 45
  working_dir: "/vllm-workspace/examples"
  source_file_dependencies:
    - vllm/v1/worker/gpu/
@@ -63,7 +63,7 @@ steps:

 - label: Model Runner V2 Distributed (2 GPUs)
  key: model-runner-v2-distributed-2-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -84,7 +84,7 @@ steps:

 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
  key: model-runner-v2-pipeline-parallelism-4-gpus
-  timeout_in_minutes: 50
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/tests"
  num_devices: 4
  source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Models Tests (Initialization)
  key: basic-models-tests-initialization
-  timeout_in_minutes: 25
+  timeout_in_minutes: 45
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -17,7 +17,7 @@ steps:
 - label: Basic Models Tests (Extra Initialization) %N
  device: h200_35gb
  key: basic-models-tests-extra-initialization
-  timeout_in_minutes: 100
+  timeout_in_minutes: 45
  source_file_dependencies:
  - vllm/model_executor/models/
  - tests/models/test_initialization.py
@@ -27,19 +27,19 @@ steps:
    # subset of supported models (the complement of the small subset in the above
    # test.) Also run if model initialization test file is modified
    - pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-  parallelism: 4
+  parallelism: 2

 - label: Basic Models Tests (Other)
  device: h200_35gb
  key: basic-models-tests-other
-  timeout_in_minutes: 35
+  timeout_in_minutes: 45
  source_file_dependencies:
  - vllm/
  - tests/models/test_terratorch.py
-  - tests/models/transformers/test_backend.py
+  - tests/models/test_transformers.py
  - tests/models/test_registry.py
  commands:
-    - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
+    - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
  mirror:
    amd:
      device: mi325_1
@@ -50,12 +50,11 @@ steps:
  key: basic-models-test-other-cpu
  depends_on:
  - image-build-cpu
-  timeout_in_minutes: 20
+  timeout_in_minutes: 10
  source_file_dependencies:
  - vllm/
  - tests/models/test_utils.py
  - tests/models/test_vision.py
-  - tests/models/transformers/fusers/
  device: cpu-small
  commands:
-    - pytest -v -s models/test_utils.py models/test_vision.py models/transformers/fusers/
+    - pytest -v -s models/test_utils.py models/test_vision.py
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Distributed Model Tests (2 GPUs)
  key: distributed-model-tests-2-gpus
-  timeout_in_minutes: 60
+  timeout_in_minutes: 50
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -17,7 +17,7 @@ steps:
  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
  # Avoid importing model tests that cause CUDA reinitialization error
-  - pytest models/transformers/test_backend.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
  - pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Language Models Tests (Standard)
  key: language-models-tests-standard
-  timeout_in_minutes: 30
+  timeout_in_minutes: 25
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -21,7 +21,7 @@ steps:

 - label: Language Models Tests (Extra Standard) %N
  key: language-models-tests-extra-standard
-  timeout_in_minutes: 40
+  timeout_in_minutes: 45
  source_file_dependencies:
  - vllm/model_executor/models/
  - tests/models/language/pooling/test_embedding.py
@@ -52,7 +52,7 @@ steps:

 - label: Language Models Tests (Hybrid) %N
  key: language-models-tests-hybrid
-  timeout_in_minutes: 65
+  timeout_in_minutes: 75
  source_file_dependencies:
  - vllm/
  - tests/models/language/generation
@@ -67,7 +67,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 70
+      timeout_in_minutes: 90
      depends_on:
      - image-build-amd
      commands:
@@ -78,7 +78,7 @@ steps:
 - label: Language Models Test (Extended Generation) # 80min
  device: h200_35gb
  key: language-models-test-extended-generation
-  timeout_in_minutes: 65
+  timeout_in_minutes: 110
  optional: true
  source_file_dependencies:
  - vllm/
@@ -92,7 +92,7 @@ steps:

 - label: Language Models Test (PPL)
  key: language-models-test-ppl
-  timeout_in_minutes: 30
+  timeout_in_minutes: 110
  device: h200_18gb
  optional: true
  source_file_dependencies:
@@ -104,7 +104,7 @@ steps:
 - label: Language Models Test (Extended Pooling)  # 36min
  device: h200_35gb
  key: language-models-test-extended-pooling
-  timeout_in_minutes: 70
+  timeout_in_minutes: 50
  optional: true
  source_file_dependencies:
  - vllm/
@@ -120,7 +120,7 @@ steps:

 - label: Language Models Test (MTEB)
  key: language-models-test-mteb
-  timeout_in_minutes: 45
+  timeout_in_minutes: 110
  device: h200_18gb
  optional: true
  source_file_dependencies:
@@ -20,14 +20,13 @@ steps:

 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
  key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
  device: h200_18gb
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
-    - pytest -v -s models/multimodal/generation/test_mm_prefix_lm.py -m core_model
    - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
  mirror:
    amd:
@@ -38,7 +37,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
  device: h200_35gb
  key: multi-modal-models-standard-3-llava-qwen2-vl
-  timeout_in_minutes: 40
+  timeout_in_minutes: 45
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
@@ -54,12 +53,12 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
  device: h200_35gb
  key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
-    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_mm_prefix_lm.py --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/generation/test_vit_cudagraph.py --ignore models/multimodal/processing
+    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/generation/test_vit_cudagraph.py --ignore models/multimodal/processing
    - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
    - pytest models/multimodal/generation/test_memory_leak.py -m core_model
    - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
@@ -69,23 +68,22 @@ steps:
      depends_on:
      - image-build-amd

- label: Multi-Modal Processor (CPU) %N
+- label: Multi-Modal Processor (CPU)
  key: multi-modal-processor-cpu
  depends_on:
  - image-build-cpu
-  timeout_in_minutes: 125
+  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  - tests/models/registry.py
  device: cpu-medium
  commands:
-    - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-  parallelism: 4
+    - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py

 - label: Multi-Modal Processor # 44min
  key: multi-modal-processor
-  timeout_in_minutes: 65
+  timeout_in_minutes: 60
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -97,7 +95,7 @@ steps:
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
  device: h200_35gb
  key: multi-modal-accuracy-eval-small-models
-  timeout_in_minutes: 30
+  timeout_in_minutes: 70
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - vllm/multimodal/
@@ -165,7 +163,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 75
+      timeout_in_minutes: 60
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Plugin Tests (2 GPUs)
  key: plugin-tests-2-gpus
-  timeout_in_minutes: 35
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  source_file_dependencies:
@@ -37,11 +37,6 @@ steps:
  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
  - pip uninstall dummy_stat_logger -y
  # end stat_logger plugins test
-  # begin endpoint plugins test
-  - pip install -e ./plugins/vllm_add_dummy_endpoint_plugin
-  - pytest -v -s plugins_tests/test_endpoint_plugins.py
-  - pip uninstall vllm_add_dummy_endpoint_plugin -y
-  # end endpoint plugins test
  # other tests continue here:
  - pytest -v -s plugins_tests/test_scheduler_plugins.py
  - pip install -e ./plugins/vllm_add_dummy_model
@@ -5,7 +5,7 @@ steps:
 - label: PyTorch Compilation Unit Tests
  device: h200_35gb
  key: pytorch-compilation-unit-tests
-  timeout_in_minutes: 90
+  timeout_in_minutes: 10
  source_file_dependencies:
    - vllm/__init__.py
    - vllm/_aiter_ops.py
@@ -78,7 +78,7 @@ steps:

 - label: PyTorch Compilation Passes Unit Tests
  key: pytorch-compilation-passes-unit-tests
-  timeout_in_minutes: 45
+  timeout_in_minutes: 20
  source_file_dependencies:
    - vllm/__init__.py
    - vllm/_aiter_ops.py
@@ -110,13 +110,13 @@ steps:
  mirror:
    amd:
      device: mi300_1
-      timeout_in_minutes: 65
+      timeout_in_minutes: 180
      depends_on:
      - image-build-amd

 - label: PyTorch Fullgraph Smoke Test
  key: pytorch-fullgraph-smoke-test
-  timeout_in_minutes: 60
+  timeout_in_minutes: 35
  source_file_dependencies:
  - vllm/__init__.py
  - vllm/_aiter_ops.py
@@ -152,7 +152,7 @@ steps:

 - label: PyTorch Fullgraph
  key: pytorch-fullgraph
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
  - vllm/__init__.py
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Quantization
  key: quantization
-  timeout_in_minutes: 60
+  timeout_in_minutes: 90
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
@@ -23,7 +23,7 @@ steps:

 - label: Quantized Fusions
  key: quantized-fusions
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  source_file_dependencies:
  - tests/fusion
  - vllm/model_executor/layers/fusion
@@ -35,7 +35,7 @@ steps:

 - label: Quantized MoE Test (B200)
  key: quantized-moe-test-b200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/"
  device: b200-k8s
  source_file_dependencies:
@@ -53,7 +53,7 @@ steps:

 - label: Quantized Models Test
  key: quantized-models-test
-  timeout_in_minutes: 50
+  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/model_executor/layers/quantization
  - tests/models/quantization
@@ -3,7 +3,7 @@ depends_on:
  - image-build
 steps:
 - label: Rust Frontend OpenAI Coverage
-  timeout_in_minutes: 30
+  timeout_in_minutes: 90
  device: h200_18gb
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
@@ -15,30 +15,28 @@ steps:
  - tests/utils.py
  - tests/benchmarks/test_serve_cli.py
  - tests/entrypoints/openai/chat_completion/test_chat_completion.py
-  - tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
+  # - tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py

  # - tests/entrypoints/openai/completion/test_prompt_validation.py
  - tests/entrypoints/openai/completion/test_shutdown.py
-  - tests/entrypoints/openai/test_return_token_ids.py
-  - tests/entrypoints/openai/test_uds.py
+  # - tests/entrypoints/openai/test_return_token_ids.py
+  # - tests/entrypoints/openai/test_uds.py
  - tests/v1/sample/test_logprobs_e2e.py
  commands:
  - export VLLM_USE_RUST_FRONTEND=1
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
  - pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py -k "not test_invalid_json_schema and not test_invalid_regex"
-  - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not multiple"
+  # - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not invalid"

  # - pytest -v -s entrypoints/openai/completion/test_prompt_validation.py -k "not prompt_embeds"
  - pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
-  # test_comparison streams differently: Rust emits a separate first (prompt_token_ids) chunk and
-  # finish chunk without logprobs, while the test reads `logprobs.tokens` on every chunk.
-  - pytest -v -s entrypoints/openai/test_return_token_ids.py -k "not test_comparison"
-  - pytest -v -s entrypoints/openai/test_uds.py
+  # - pytest -v -s entrypoints/openai/test_return_token_ids.py
+  # - pytest -v -s entrypoints/openai/test_uds.py
  - pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"

 - label: Rust Frontend Serve/Admin Coverage
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
  device: h200_18gb
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
@@ -47,27 +45,22 @@ steps:
  - vllm/entrypoints/serve/
  - vllm/v1/engine/
  - tests/utils.py
-  - tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
+  # - tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
  - tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
  - tests/entrypoints/serve/instrumentator/test_basic.py
  - tests/entrypoints/serve/instrumentator/test_metrics.py
  # - tests/entrypoints/serve/dev/test_sleep.py
-  - tests/entrypoints/serve/tokenize/test_tokenization.py
  commands:
  - export VLLM_USE_RUST_FRONTEND=1
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
-  # server_load can be flaky under the Rust frontend; keep it excluded for now.
+  # - pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
  - pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
-  # test_generate_logprobs expects Python-style top_logprobs truncation (dedup sampled + cap at max(k, 1)).
  - pytest -v -s entrypoints/scale_out/token_in_token_out/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
  - pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
  # - pytest -v -s entrypoints/serve/dev/test_sleep.py
-  # /tokenizer_info is not implemented in the Rust frontend (the CLI flag is accepted as a no-op).
-  - pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info"

 - label: Rust Frontend Core Correctness
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: h200_18gb
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
@@ -81,7 +74,7 @@ steps:
  - pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine

 - label: Rust Frontend Tool Use
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - rust/
@@ -95,7 +88,7 @@ steps:
  - pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"

 - label: Rust Frontend Distributed
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
  num_devices: 4
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
@@ -4,7 +4,7 @@ steps:
 - label: Rust Frontend Cargo Style + Clippy
  key: rust-frontend-cargo-style-clippy
  depends_on: []
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: cpu-medium
  no_plugin: true
  source_file_dependencies:
@@ -18,7 +18,7 @@ steps:
 - label: Rust Frontend Cargo Tests
  key: rust-frontend-cargo-tests
  depends_on: []
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: cpu-medium
  no_plugin: true
  source_file_dependencies:
@@ -5,7 +5,7 @@ steps:
 - label: Samplers Test
  device: h200_35gb
  key: samplers-test
-  timeout_in_minutes: 40
+  timeout_in_minutes: 75
  source_file_dependencies:
  - vllm/model_executor/layers
  - vllm/sampling_metadata.py
@@ -19,7 +19,7 @@ steps:
    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
  mirror:
    amd:
-      device: mi325_1
+      device: mi250_1
      depends_on:
      - image-build-amd
      commands:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Spec Decode Eagle
  key: spec-decode-eagle
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -15,7 +15,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 60
+      timeout_in_minutes: 45
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -29,7 +29,7 @@ steps:

 - label: Spec Decode Eagle Nightly B200
  key: spec-decode-eagle-nightly-b200
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
  device: b200-k8s
  optional: true
  source_file_dependencies:
@@ -41,7 +41,7 @@ steps:

 - label: Spec Decode Speculators + MTP
  key: spec-decode-speculators-mtp
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -82,7 +82,7 @@ steps:
  
 - label: Spec Decode Ngram + Suffix
  key: spec-decode-ngram-suffix
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -93,7 +93,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 55
+      timeout_in_minutes: 65
      # TODO(akaratza): Test after Torch >= 2.12 bump
      soft_fail: true
      depends_on:
@@ -109,7 +109,7 @@ steps:

 - label: Spec Decode Draft Model
  key: spec-decode-draft-model
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -120,7 +120,7 @@ steps:
  mirror:
    amd:
      device: mi325_1
-      timeout_in_minutes: 55
+      timeout_in_minutes: 50
      depends_on:
      - image-build-amd
      source_file_dependencies:
@@ -134,7 +134,7 @@ steps:

 - label: Spec Decode Draft Model Nightly B200
  key: spec-decode-draft-model-nightly-b200
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
  device: b200-k8s
  optional: true
  source_file_dependencies:
@@ -146,7 +146,7 @@ steps:

 - label: Speculators Correctness
  key: speculators-correctness
-  timeout_in_minutes: 30
+  timeout_in_minutes: 60
  device: h100
  optional: true
  num_devices: 1
@@ -159,7 +159,7 @@ steps:
    - pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test

 - label: Spec Decode MTP hybrid (B200)
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
  device: b200-k8s
  optional: true
  source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Weight Loading Multiple GPU  # 33min
  key: weight-loading-multiple-gpu
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
  working_dir: "/vllm-workspace/tests"
  num_devices: 2
  optional: true
@@ -119,7 +119,7 @@

 # Transformers modeling backend
 /vllm/model_executor/models/transformers @hmellor
-/tests/models/transformers @hmellor
+/tests/models/test_transformers.py @hmellor

 # Docs
 /docs/mkdocs @hmellor
@@ -28,8 +28,7 @@ jobs:
            pull_number: context.payload.pull_request.number,
          });

-          const readyLabels = ['ready', 'ready-run-all-tests'];
-          const hasReadyLabel = pr.labels.some(l => readyLabels.includes(l.name));
+          const hasReadyLabel = pr.labels.some(l => l.name === 'ready');
          const hasVerifiedLabel = pr.labels.some(l => l.name === 'verified');

          const { data: mergedPRs } = await github.rest.search.issuesAndPullRequests({
@@ -41,7 +40,7 @@ jobs:
          if (hasReadyLabel || hasVerifiedLabel || mergedCount >= 4) {
            core.info(`Check passed: verified label=${hasVerifiedLabel}, ready label=${hasReadyLabel}, 4+ merged PRs=${mergedCount >= 4}`);
          } else {
-            core.setFailed(`PR must have the 'verified', 'ready', or 'ready-run-all-tests' label (the ready labels also trigger tests) or the author must have at least 4 merged PRs (found ${mergedCount}).`);
+            core.setFailed(`PR must have the 'verified' or 'ready' (which also triggers tests) label or the author must have at least 4 merged PRs (found ${mergedCount}).`);
          }

  pre-commit:
@@ -29,7 +29,6 @@ Do not open one-off PRs for tiny edits (single typo, isolated style change, one
 - PR descriptions for AI-assisted work **must** include:
    - Why this is not duplicating an existing PR.
    - Test commands run and results.
-    - Model evaluation results when the change affects output, accuracy, or serving.
    - Clear statement that AI assistance was used.

 ### Fail-closed behavior
@@ -67,38 +66,23 @@ VLLM_USE_PRECOMPILED=1 uv pip install -e . --torch-backend=auto
 uv pip install -e . --torch-backend=auto
 ```

-### Tests
+### Running tests

 > Requires [Environment setup](#environment-setup) and [Installing dependencies](#installing-dependencies).

 ```bash
-# Install test dependencies (use cuda.in on non-x86_64):
-uv pip install -r requirements/test/cuda.in
+# Install test dependencies.
+# requirements/test/cuda.txt is pinned to x86_64; on other platforms, use the
+# unpinned source file instead:
+uv pip install -r requirements/test/cuda.in    # resolves for current platform
+# Or on x86_64:
+uv pip install -r requirements/test/cuda.txt

-# Run a specific test file:
+# Run a specific test file (use .venv/bin/python directly;
+# `source activate` does not persist in non-interactive shells):
 .venv/bin/python -m pytest tests/path/to/test_file.py -v
 ```

-When adding tests:
-
- **Design before you write.** Answer four questions first: what is the module
-  for, what is its I/O contract, what failure am I guarding against, and what is
-  the cheapest level that catches it (unit over integration over e2e)?
- **Reuse before create.** Extend existing test files, `conftest.py` fixtures, and
-  helpers; add a new file only when no nearby suite fits.
- **Test behavior with intent.** Assert observable outcomes through public APIs;
-  state why in the name or docstring. Skip trivial wiring; flaky tests are worse
-  than no tests.
- **Keep it minimal.** One behavior per test and the smallest setup that
-  triggers it; if the test diff dwarfs the code change, cut scope.
- **No one-off kernel benchmarks in `tests/`.** Put kernel perf work in
-  `benchmarks/kernels/`; prove correctness in existing pytest suites.
- **Run model evals for model-affecting changes.** Search `tests/evals/` or use
-  `vllm bench` and include results in the PR — do not wait for reviewers to ask.
-
-For model-specific requirements, see
-[`docs/contributing/model/tests.md`](docs/contributing/model/tests.md).
-
 ### Running linters

 > Requires [Environment setup](#environment-setup).
@@ -123,18 +107,23 @@ Use [Google-style docstrings](https://google.github.io/styleguide/pyguide.html#3

 ### Coding style guidelines

- Match existing code style
- Minimize use of comments. Eliminate comments which are redundant, preferring legible and self-documenting code. When used, keep docstrings and comments brief and direct.
+Follow these rules for all code changes in this repository:
+
+- Try to match existing code style.
+- Code should be self-documenting and self-explanatory.
+- Keep comments and docstrings minimal and concise.
 - Assume the reader is familiar with vLLM.

 ### Commit messages

-Add attribution using commit trailers such as `Co-authored-by:` (other projects use `Assisted-by:` or `Generated-by:`):
+Add attribution using commit trailers such as `Co-authored-by:` (other projects use `Assisted-by:` or `Generated-by:`). For example:

 ```text
 Your commit message here

-Co-authored-by: Agent Name Here
+Co-authored-by: GitHub Copilot
+Co-authored-by: Claude
+Co-authored-by: gemini-code-assist
 Signed-off-by: Your Name <your.email@example.com>
 ```

@@ -70,15 +70,6 @@ endif()
 #
 set(TORCH_SUPPORTED_VERSION_CUDA "2.11.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.11.0")
-# TORCH_NIGHTLY=1 builds run against unpinned nightly wheels, so the supported-
-# version check would always warn. Only treat it as a nightly build when the
-# value is exactly "1" (the bootstrap exports TORCH_NIGHTLY=0 by default, which
-# must NOT suppress the warning for normal builds).
-if (DEFINED ENV{TORCH_NIGHTLY} AND "$ENV{TORCH_NIGHTLY}" STREQUAL "1")
-  set(TORCH_NIGHTLY_BUILD TRUE)
-else()
-  set(TORCH_NIGHTLY_BUILD FALSE)
-endif()

 #
 # Try to find python package with an executable that exactly matches
@@ -184,7 +175,7 @@ endif()
 if (NOT HIP_FOUND AND NOT PYTORCH_FOUND_HIP AND CUDA_FOUND)
  set(VLLM_GPU_LANG "CUDA")

-  if (NOT TORCH_NIGHTLY_BUILD AND NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_CUDA})
+  if (NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_CUDA})
    message(WARNING "Pytorch version ${TORCH_SUPPORTED_VERSION_CUDA} "
      "expected for CUDA build, saw ${Torch_VERSION} instead.")
  endif()
@@ -197,7 +188,7 @@ elseif(HIP_FOUND OR PYTORCH_FOUND_HIP)
  enable_language(HIP)

  # ROCm 5.X and 6.X
-  if (NOT TORCH_NIGHTLY_BUILD AND ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
+  if (ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
      Torch_VERSION VERSION_LESS ${TORCH_SUPPORTED_VERSION_ROCM})
    message(WARNING "Pytorch version >= ${TORCH_SUPPORTED_VERSION_ROCM} "
      "expected for ROCm build, saw ${Torch_VERSION} instead.")
@@ -390,7 +381,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
    "csrc/libtorch_stable/cuda_view.cu"
    "csrc/libtorch_stable/cuda_utils_kernels.cu"
    "csrc/libtorch_stable/activation_kernels.cu"
-    "csrc/libtorch_stable/ngram_embedding_kernels.cu"
    "csrc/libtorch_stable/quantization/activation_kernels.cu"
    "csrc/libtorch_stable/quantization/w8a8/int8/scaled_quant.cu"
    "csrc/libtorch_stable/quantization/w8a8/fp8/common.cu"
@@ -410,6 +400,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
    "csrc/libtorch_stable/topk.cu"
    "csrc/libtorch_stable/mamba/selective_scan_fwd.cu"
    "csrc/libtorch_stable/cache_kernels.cu"
+    "csrc/libtorch_stable/cache_kernels.cu"
    "csrc/libtorch_stable/cache_kernels_fused.cu"
    "csrc/libtorch_stable/custom_all_reduce.cu"
    "csrc/libtorch_stable/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
@@ -12,7 +12,7 @@ from dataclasses import dataclass, field
 import aiohttp
 import huggingface_hub.constants
 from tqdm.asyncio import tqdm
-from transformers import AutoTokenizer, PythonBackend, TokenizersBackend
+from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast

 # NOTE(simon): do not import vLLM here so the benchmark script
 # can run without vLLM installed.
@@ -609,7 +609,7 @@ def get_tokenizer(
    tokenizer_mode: str = "auto",
    trust_remote_code: bool = False,
    **kwargs,
-) -> PythonBackend | TokenizersBackend:
+) -> PreTrainedTokenizer | PreTrainedTokenizerFast:
    if pretrained_model_name_or_path is not None and not os.path.exists(
        pretrained_model_name_or_path
    ):
@@ -132,10 +132,8 @@ def benchmark_function(
    reset_memory_stats()

    # Benchmark
-    start_events = [
-        torch.cuda.Event(enable_timing=True) for _ in range(benchmark_iters)
-    ]
-    end_events = [torch.cuda.Event(enable_timing=True) for _ in range(benchmark_iters)]
+    start_events = [torch.Event(enable_timing=True) for _ in range(benchmark_iters)]
+    end_events = [torch.Event(enable_timing=True) for _ in range(benchmark_iters)]

    for i in range(benchmark_iters):
        logits_copy = logits.clone()
@@ -17,7 +17,7 @@ from vllm.model_executor.layers.fused_moe.fused_flydsl_moe import fused_flydsl_m
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
    compressed_tensors_moe_w4a16_flydsl,
 )
-from vllm.utils.platform_utils import get_device_name_as_file_name
+from vllm.platforms import current_platform

 RoutingBuffers = tuple[
    torch.Tensor,  # sorted_token_ids
@@ -259,7 +259,7 @@ def tune_flydsl_moe_w4a16(
                            )
                            us_best = us
                            tuned_config[str(num_tokens)] = tile_config
-            device_name = get_device_name_as_file_name()
+            device_name = current_platform.get_device_name().replace(" ", "_")
            tuned_config_file_name = (
                f"E={num_experts},N={inter_dim},device_name={device_name},"
                f"dtype=int4_w4a16,backend=flydsl.json"
@@ -134,8 +134,8 @@ def benchmark_config(
    torch.accelerator.synchronize()

    # Benchmark
-    start = torch.cuda.Event(enable_timing=True)
-    end = torch.cuda.Event(enable_timing=True)
+    start = torch.Event(enable_timing=True)
+    end = torch.Event(enable_timing=True)
    start.record()
    for _ in range(num_iters):
        with override_config(config):
@@ -1,108 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Benchmark ReLUSquaredActivation: custom CUDA kernel vs forward_native, both
-# eager and under torch.compile (Inductor fuses relu+square into one kernel).
-
-import itertools
-
-import torch
-import torch.nn.functional as F
-
-import vllm.model_executor.layers.activation  # noqa: F401
-from vllm.benchmarks.lib.utils import default_vllm_config
-from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
-
-# Capped so the largest tensor stays under 2**31 elements: the shared activation
-# kernel computes the per-token pointer offset (blockIdx.x * d) in 32-bit, which
-# overflows for tensors with >2**32 elements. Realistic token counts are well
-# below this; the kernel-vs-native gap is already clear at these sizes.
-batch_size_range = [1, 16, 128]
-seq_len_range = [1, 16, 64, 1024]
-intermediate_size = [3072, 9728, 12288]
-configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))
-
-
-@default_vllm_config()
-def benchmark_relu_squared(
-    batch_size: int,
-    seq_len: int,
-    intermediate_size: int,
-    provider: str,
-    dtype: torch.dtype,
-):
-    device = "cuda"
-    num_tokens = batch_size * seq_len
-    set_random_seed(42)
-    torch.set_default_device(device)
-
-    x = torch.randn(num_tokens, intermediate_size, dtype=dtype, device=device)
-    out = torch.empty_like(x)
-
-    def native(x: torch.Tensor) -> torch.Tensor:
-        return torch.square(F.relu(x))
-
-    # Verify the custom kernel matches the native implementation before timing.
-    ref = native(x)
-    torch.ops._C.relu_squared(out, x)
-    torch.testing.assert_close(out, ref)
-
-    if provider == "custom":
-        # Custom CUDA kernel — single fused kernel.
-        fn = lambda: torch.ops._C.relu_squared(out, x)
-    elif provider == "native":
-        # forward_native, eager — relu and square as separate ops.
-        fn = lambda: native(x)
-    elif provider == "native_compiled":
-        # forward_native under torch.compile — Inductor fuses relu+square.
-        # This is the real production baseline (custom ops are off when
-        # Inductor is enabled), so it is the comparison reviewers care about.
-        compiled = torch.compile(native)
-        compiled(x)  # warm up / trigger compilation before timing
-        fn = lambda: compiled(x)
-
-    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-        fn, quantiles=[0.5, 0.2, 0.8]
-    )
-    return ms, max_ms, min_ms
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(
-        description="Benchmark ReLUSquaredActivation: custom kernel vs native."
-    )
-    parser.add_argument(
-        "--dtype",
-        type=str,
-        choices=["half", "bfloat16", "float"],
-        default="bfloat16",
-    )
-    args = parser.parse_args()
-
-    dtype = STR_DTYPE_TO_TORCH_DTYPE[args.dtype]
-
-    perf_report = triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=["batch_size", "seq_len", "intermediate_size"],
-            x_vals=configs,
-            line_arg="provider",
-            line_vals=["custom", "native_compiled", "native"],
-            line_names=[
-                "Custom Kernel",
-                "Native (torch.compile)",
-                "Native (eager)",
-            ],
-            styles=[("blue", "-"), ("green", "-"), ("red", "-")],
-            ylabel="ms",
-            plot_name="relu_squared-eager-performance",
-            args={},
-        )
-    )
-
-    perf_report(
-        lambda batch_size, seq_len, intermediate_size, provider: benchmark_relu_squared(
-            batch_size, seq_len, intermediate_size, provider, dtype
-        )
-    ).run(print_data=True)
@@ -18,9 +18,10 @@ DSV3_SUPPORTED_HIDDEN_SIZES = [7168]
 GPT_OSS_SUPPORTED_NUM_EXPERTS = [32, 128]
 GPT_OSS_SUPPORTED_HIDDEN_SIZES = [2880]

-# Dimensions supported by the fp32 specialized kernel (MiniMax-M2)
+# Dimensions supported by the fp32 specialized kernel
+#   (3072, 256) -> MiniMax-M2/M2.5,  (6144, 256) -> GLM-5
 FP32_SUPPORTED_NUM_EXPERTS = [256]
-FP32_SUPPORTED_HIDDEN_SIZES = [3072]
+FP32_SUPPORTED_HIDDEN_SIZES = [3072, 6144]
 FP32_MAX_TOKENS = 32


@@ -33,6 +34,7 @@ def get_model_params(config):
        "DeepseekV2ForCausalLM",
        "DeepseekV3ForCausalLM",
        "DeepseekV32ForCausalLM",
+        "GlmMoeDsaForCausalLM",
    ):
        num_experts = config.n_routed_experts
        hidden_size = config.hidden_size
@@ -107,7 +109,8 @@ def get_benchmark(model, max_batch_size, trust_remote_code):
        if provider == "torch":

            def runner():
-                if allow_fp32_router_gemm:
+                if is_fp32_router_model:
+                    # fp32 weights: reference always computes in fp32
                    F.linear(mat_a.float(), mat_b)
                elif has_bias:
                    F.linear(mat_a, mat_b, bias)
@@ -170,8 +170,8 @@ def benchmark_config(
                graph.replay()
            torch.accelerator.synchronize()

-            start = torch.cuda.Event(enable_timing=True)
-            end = torch.cuda.Event(enable_timing=True)
+            start = torch.Event(enable_timing=True)
+            end = torch.Event(enable_timing=True)
            latencies: list[float] = []
            for _ in range(num_iters):
                start.record()
@@ -19,7 +19,6 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.platform_utils import get_device_name_as_file_name

 mp.set_start_method("spawn", force=True)

@@ -265,7 +264,7 @@ def save_configs(
    input_type="fp8",
 ) -> None:
    os.makedirs(save_path, exist_ok=True)
-    device_name = get_device_name_as_file_name()
+    device_name = current_platform.get_device_name().replace(" ", "_")
    json_file_name = (
        f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
        f"block_shape=[{block_n},{block_k}].json"
@@ -1,241 +0,0 @@
-#!/bin/bash
-set -eoux pipefail
-
-########################################
-# Resolve repo root (IMPORTANT)
-########################################
-REPO_ROOT="$(pwd)"
-
-cd "$REPO_ROOT"
-
-########################################
-# DevPI configuration
-########################################
-
-IBM_DEVPI_URL=${IBM_DEVPI_URL:-"https://wheels.developerfirst.ibm.com/ppc64le/linux/+simple/"}
-RHOAI_INDEX_URL=${RHOAI_INDEX_URL:-"https://console.redhat.com/api/pypi/public-rhai/rhoai/3.4/cpu-ubi9/simple/"}
-
-########################################
-# wheel dir
-########################################
-
-WHEEL_DIR=${WHEEL_DIR:-"/tmp/wheels"}
-mkdir -p "$WHEEL_DIR"
-
-########################################
-# Helpers
-########################################
-try_install_from_devpi() {
-    local pkg=$1
-    uv pip install \
-        --extra-index-url "${IBM_DEVPI_URL}" \
-        --index-strategy unsafe-best-match \
-        --no-build-isolation \
-        "${pkg}"
-}
-
-########################################
-# Package Versions
-########################################
-cd "$REPO_ROOT"
-TORCH_VERSION=${TORCH_VERSION:-$(grep -E '^torch==.+==\s*"ppc64le"' requirements/cpu.txt | grep -Eo '\b[0-9\.]+\b' || true)}
-TORCH_VERSION=${TORCH_VERSION:-2.11.0}
-
-TORCHVISION_VERSION=${TORCHVISION_VERSION:-0.26.0}
-TORCHAUDIO_VERSION=${TORCHAUDIO_VERSION:-${TORCH_VERSION}}
-
-export TORCH_VERSION
-export TORCHVISION_VERSION
-export TORCHAUDIO_VERSION
-export OPENCV_VERSION=${OPENCV_VERSION:-4.13.0.92}
-export XGRAMMAR_VERSION=${XGRAMMAR_VERSION:-0.2.1}
-
-########################################
-# install system dependencies
-########################################
-
-rpm -ivh https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm || true
-
-microdnf install -y \
-    python3.12 python3.12-devel python3.12-pip gcc \
-    git jq gcc-toolset-14 gcc-toolset-14-libatomic-devel \
-    automake libtool clang-devel openssl-devel \
-    harfbuzz-devel kmod lcms2-devel libimagequant-devel libjpeg-turbo-devel \
-    llvm15-devel libraqm-devel libtiff-devel libwebp-devel libxcb-devel \
-    ninja-build openjpeg2-devel pkgconfig \
-    tcl-devel tk-devel xsimd-devel zeromq-devel zlib-devel patchelf file openblas openblas-devel protobuf numactl numactl-devel openmpi openmpi-devel
-    
-rpm -ivh --nodeps \
-    https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-lite-devel-3.14.0-17.el9.ppc64le.rpm
-
-rpm -ivh --nodeps \
-    https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-devel-3.14.0-17.el9.ppc64le.rpm
-
-rpm -ivh --nodeps \
-    https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-compiler-3.14.0-17.el9.ppc64le.rpm
-
-########################################
-# Python 3.12 virtual environment
-########################################
-
-python3.12 -m venv /opt/vllm
-source /opt/vllm/bin/activate
-
-export PATH=/opt/vllm/bin:$PATH
-
-python --version
-
-########################################
-# install build tools (stable uv)
-########################################
-
-pip install -U pip setuptools-rust
-pip install uv
-pip install "setuptools<70" build wheel cmake auditwheel
-uv pip install "setuptools<70" cython meson-python pybind11 "sympy>=1.13.3" --no-build-isolation
-
-########################################
-# Rust
-########################################
-
-curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-source /root/.cargo/env
-
-########################################
-# Compiler env
-########################################
-
-source /opt/rh/gcc-toolset-14/enable
-
-export PATH=/usr/lib64/llvm15/bin:$PATH
-export LLVM_CONFIG=/usr/lib64/llvm15/bin/llvm-config
-export CMAKE_ARGS="-DPython3_EXECUTABLE=python"
-
-export MAX_JOBS=${MAX_JOBS:-$(nproc)}
-export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
-
-########################################
-# Install Packages From Devpi
-########################################
-uv pip install numpy==2.3.5 pillow==12.2.0 --extra-index-url "$IBM_DEVPI_URL"
-try_install_from_devpi "opencv-python-headless==${OPENCV_VERSION}"
-try_install_from_devpi "torch==${TORCH_VERSION}"
-try_install_from_devpi "torchvision==${TORCHVISION_VERSION}"
-
-########################################
-# torch audio
-########################################
-
-TEMP_BUILD_DIR=$(mktemp -d)
-cd "${TEMP_BUILD_DIR}"
-export BUILD_SOX=1 BUILD_KALDI=1 BUILD_RNNT=1 USE_FFMPEG=0 USE_ROCM=0 USE_CUDA=0
-export TORCHAUDIO_TEST_ALLOW_SKIP_IF_NO_FFMPEG=1
-git clone --recursive https://github.com/pytorch/audio.git -b v${TORCHAUDIO_VERSION}
-cd audio
-#patching 
-sed -i '
-s|_CSRC_DIR / "_torchaudio.cpp"|str(_CSRC_DIR / "_torchaudio.cpp")|;
-s|_CSRC_DIR / "utils.cpp"|str(_CSRC_DIR / "utils.cpp")|;
-s|sources=\[_CSRC_DIR / s for s in sources\]|sources=[str(_CSRC_DIR / s) for s in sources]|;
-' tools/setup_helpers/extension.py
-MAX_JOBS=${MAX_JOBS:-$(nproc)} \
-BUILD_VERSION=${TORCHAUDIO_VERSION} \
-uv build --wheel --out-dir "${WHEEL_DIR}" --no-build-isolation
-uv pip install "${WHEEL_DIR}"/torchaudio*.whl
-cd "${REPO_ROOT}"
-rm -rf "${TEMP_BUILD_DIR}"
-
-########################################
-# Xgrammar
-########################################
-uv pip install \
-   "scikit-build-core==0.11.6" \
-   "pyproject-metadata<0.8" \
-    pathspec \
-    packaging \
-    distro \
-   "setuptools<70" \
-    setuptools_scm \
-    cmake \
-    ninja \
-    pybind11 \
-    nanobind
-uv pip install apache-tvm-ffi==0.1.12 \
-  --no-build-isolation \
-  --no-cache
-
-TEMP_BUILD_DIR=$(mktemp -d)
-
-pushd "${TEMP_BUILD_DIR}"
-
-export CFLAGS="-fno-lto -mcpu=power9"
-export CXXFLAGS="-fno-lto -mcpu=power9"
-export LDFLAGS="-fno-lto"
-export PATH=/opt/vllm/bin:$PATH
-
-export Python_EXECUTABLE=/opt/vllm/bin/python3
-export Python3_EXECUTABLE=/opt/vllm/bin/python3
-export PYTHON_EXECUTABLE=/opt/vllm/bin/python3
-
-export Python_ROOT_DIR=/opt/vllm
-export Python3_ROOT_DIR=/opt/vllm
-
-git clone \
-    --recursive \
-    https://github.com/mlc-ai/xgrammar \
-    -b "v${XGRAMMAR_VERSION}"
-
-cd xgrammar
-
-cp cmake/config.cmake .
-export PYTHONPATH=/opt/vllm/lib64/python3.12/site-packages:/opt/vllm/lib/python3.12/site-packages:${PYTHONPATH:-}
-
-uv build \
-    --wheel \
-    --out-dir "${WHEEL_DIR}" \
-    --no-build-isolation
-
-uv pip install "${WHEEL_DIR}"/xgrammar*.whl -v
-
-popd
-
-rm -rf "${TEMP_BUILD_DIR}"
-cd "${REPO_ROOT}"
-
-########################################
-# RHOAI Binary Downloads
-########################################
-pip download \
-    --index-url "${RHOAI_INDEX_URL}" \
-    --only-binary=:all: \
-    --no-deps \
-    llvmlite==0.47.0 \
-    -d "${WHEEL_DIR}"
-
-pip download \
-    --index-url "${RHOAI_INDEX_URL}" \
-    --only-binary=:all: \
-    --no-deps \
-    Numba==0.65.0 \
-    -d "${WHEEL_DIR}"
-
-########################################
-# install built wheels
-########################################
-uv pip install setuptools_scm maturin setuptools-rust ninja scikit-build-core pybind11 nanobind \
-    --no-build-isolation
-uv pip install "${WHEEL_DIR}"/*.whl
-
-########################################
-# install remaining deps
-########################################
-
-sed -i.bak -e 's/.*torch.*//g' pyproject.toml requirements/*.txt
-
-uv pip install "setuptools>=78.1.1" --no-build-isolation
-
-export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/local/lib64/pkgconfig:/usr/lib64/pkgconfig
-
-uv pip install -r requirements/common.txt \
-               -r requirements/cpu.txt \
-               -r requirements/build/cpu.txt --index-strategy unsafe-best-match
@@ -15,7 +15,6 @@ endif()
 #
 set(ENABLE_X86_ISA $ENV{VLLM_CPU_X86})
 set(ENABLE_ARM_BF16 $ENV{VLLM_CPU_ARM_BF16})
-set(ENABLE_RVV_BF16 $ENV{VLLM_CPU_RVV_BF16})

 include_directories("${CMAKE_SOURCE_DIR}/csrc")

@@ -111,13 +110,6 @@ else()
        set(ARM_BF16_FOUND ON)
        message(STATUS "ARM BF16 support enabled via VLLM_CPU_ARM_BF16 environment variable")
    endif()
-    # Some kernels (e.g. Bianbu on Spacemit X100) do not report zvfbfmin
-    # in /proc/cpuinfo despite hardware support. VLLM_CPU_RVV_BF16=1
-    # overrides the detection result.
-    if (ENABLE_RVV_BF16)
-        set(RVV_BF16_FOUND ON)
-        message(STATUS "RVV BF16 support enabled via VLLM_CPU_RVV_BF16 environment variable")
-    endif()
 endif()

 if (CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64|amd64" OR ENABLE_X86_ISA)
@@ -186,10 +178,7 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
    # Override with -DVLLM_RVV_VLEN=128 or -DVLLM_RVV_VLEN=256 for RVV.
    if(NOT DEFINED VLLM_RVV_VLEN)
        # Auto-detect: find the largest zvl<N>b in /proc/cpuinfo isa line.
-        # Skip when cross-compiling — /proc/cpuinfo describes the build host.
-        if(CMAKE_CROSSCOMPILING)
-            message(STATUS "Cross-compiling: skipping VLEN auto-detection from /proc/cpuinfo")
-        elseif(EXISTS /proc/cpuinfo)
+        if(EXISTS /proc/cpuinfo)
            file(READ /proc/cpuinfo _cpuinfo)
            set(_best 0)
            foreach(_n IN ITEMS 128 256 512 1024)
@@ -197,13 +186,6 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
                    set(_best ${_n})
                endif()
            endforeach()
-            # Only VLEN=128 and VLEN=256 are supported by the RVV kernels.
-            if(_best GREATER 256)
-                message(WARNING
-                    "Detected VLEN=${_best} but only 128/256 are supported; "
-                    "clamping to 256")
-                set(_best 256)
-            endif()
            if(_best GREATER 0)
                set(VLLM_RVV_VLEN ${_best})
            endif()
@@ -213,9 +195,9 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
        if(NOT DEFINED VLLM_RVV_VLEN AND (RVV_FP16_FOUND OR RVV_BF16_FOUND))
            message(FATAL_ERROR
                "RISC-V RVV is available but VLEN could not be auto-detected. "
-                "Please specify VLEN explicitly via CMAKE_ARGS:\n"
-                "  CMAKE_ARGS='-DVLLM_RVV_VLEN=128'   (for VLEN=128 hardware)\n"
-                "  CMAKE_ARGS='-DVLLM_RVV_VLEN=256'   (for VLEN=256 hardware, e.g. Spacemit X100)")
+                "Please specify VLEN explicitly:\n"
+                "  -DVLLM_RVV_VLEN=128   (for VLEN=128 hardware)\n"
+                "  -DVLLM_RVV_VLEN=256   (for VLEN=256 hardware, e.g. Spacemit X100)")
        endif()
    endif()
    if(VLLM_RVV_VLEN AND VLLM_RVV_VLEN GREATER 0)
@@ -227,7 +209,7 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
            message(STATUS "BF16 extension detected")
            set(MARCH_FLAGS -march=rv64gcv_zvfh_zfbfmin_zvfbfmin_zvl${VLLM_RVV_VLEN}b -mrvv-vector-bits=zvl -mabi=lp64d)
        elseif(RVV_FP16_FOUND)
-            message(WARNING "BF16 functionality is not available.")
+            message(WARNING "BF16 functionality is not available")
            set(MARCH_FLAGS -march=rv64gcv_zvfh_zvl${VLLM_RVV_VLEN}b -mrvv-vector-bits=zvl -mabi=lp64d)
        else()
            message(STATUS "compile riscv with scalar (no FP16/BF16)")
@@ -19,7 +19,7 @@ else()
  FetchContent_Declare(
        flashmla
        GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG b70aff3d110a2b1a037e62eac295166b5143643a
+        GIT_TAG a6ec2ba7bd0a7dff98b3f4d3e6b52b159c48d78b
        GIT_PROGRESS TRUE
        CONFIGURE_COMMAND ""
        BUILD_COMMAND ""
@@ -17,7 +17,7 @@ else()
  FetchContent_Declare(
    fmha_sm100
    GIT_REPOSITORY https://github.com/vllm-project/MSA.git
-    GIT_TAG 2e63ec37a0fc29bc20f39cd1a52e0f5affc33a73
+    GIT_TAG fee783153f3efe57e3e933c5cb7e267a7cebcfb5
    GIT_PROGRESS TRUE
    CONFIGURE_COMMAND ""
    BUILD_COMMAND ""
@@ -39,7 +39,7 @@ else()
  FetchContent_Declare(
          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG bb9a72e7dde0dc614ffc663e052cd6a19ce73a42
+          GIT_TAG 2c839c33742309ec41e620bf837495ec9926c56e
          GIT_PROGRESS TRUE
          # Don't share the vllm-flash-attn build between build types
          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
@@ -13,8 +13,7 @@ static inline cpu_attention::Fp8KVCacheDataType parse_fp8_kv_dtype(

 bool cpu_attn_has_isa(const std::string& isa) {
  if (isa == "rvv") {
-#if defined(__riscv) && defined(__riscv_v_min_vlen) && \
-    (__riscv_v_min_vlen == 128 || __riscv_v_min_vlen == 256)
+#if defined(__riscv) && defined(__riscv_v_min_vlen) && __riscv_v_min_vlen == 128
    return true;
 #else
    return false;
@@ -417,10 +417,8 @@ class AttentionScheduler {
      has_decode_request = has_decode_request || (q_token_num == 1);
      decode_only_batch = decode_only_batch && (q_token_num == 1);
    }
-    const int32_t original_q_head_per_kv =
-        input.num_heads_q / input.num_heads_kv;
-    int32_t q_head_per_kv = original_q_head_per_kv;
-    const bool supports_gqa = original_q_head_per_kv <= max_num_q_per_iter;
+    int32_t q_head_per_kv = input.num_heads_q / input.num_heads_kv;
+    const bool supports_gqa = q_head_per_kv <= max_num_q_per_iter;
    const bool use_gqa_fast_path = supports_gqa && decode_only_batch;
    const bool use_gqa_scratchpad = supports_gqa && has_decode_request;
    if (!use_gqa_scratchpad) {
@@ -673,62 +671,22 @@ class AttentionScheduler {
    metadata_ptr->effective_thread_num = effective_thread_num;

    {
+      // when q_tile_size = max_num_q_per_iter, requires max
+      // attention_scratchpad_size
      AttentionScratchPad sc(0, *metadata_ptr, 0x0);
-      int64_t max_attention_scratchpad_size = 0;
-
-      for (const AttentionWorkItemGroup& item : workitems) {
-        const bool curr_use_gqa =
-            use_gqa_fast_path || (supports_gqa && item.q_token_num == 1);
-        const int32_t curr_q_heads_per_kv =
-            curr_use_gqa ? original_q_head_per_kv : 1;
-        const int32_t curr_default_q_tile_token_num =
-            default_tile_size / curr_q_heads_per_kv;
-
-        for (int32_t q_token_offset = 0; q_token_offset < item.q_token_num;
-             q_token_offset += curr_default_q_tile_token_num) {
-          const int32_t actual_q_token_num = std::min(
-              curr_default_q_tile_token_num, item.q_token_num - q_token_offset);
-          const int32_t q_head_tile_size =
-              actual_q_token_num * curr_q_heads_per_kv;
-          const int32_t rounded_q_head_tile_size =
-              ((q_head_tile_size + max_num_q_per_iter - 1) /
-               max_num_q_per_iter) *
-              max_num_q_per_iter;
-
-          const int64_t n = AttentionScheduler::calcu_tile_size_with_constant_q(
-              cache_size, input.head_dim, input.elem_size,
-              input.q_buffer_elem_size, input.logits_buffer_elem_size,
-              input.output_buffer_elem_size, max_num_q_per_iter,
-              kv_len_alignment, rounded_q_head_tile_size,
-              rounded_q_head_tile_size <= max_num_q_per_iter);
-
-          sc.update(input.head_dim, input.q_buffer_elem_size,
-                    input.logits_buffer_elem_size,
-                    input.output_buffer_elem_size, max_num_q_per_iter,
-                    rounded_q_head_tile_size, n);
-
-          max_attention_scratchpad_size = std::max(
-              max_attention_scratchpad_size, sc.get_thread_scratchpad_size());
-        }
-      }
-
+      int64_t n = AttentionScheduler::calcu_tile_size_with_constant_q(
+          cache_size, input.head_dim, input.elem_size, input.q_buffer_elem_size,
+          input.logits_buffer_elem_size, input.output_buffer_elem_size,
+          max_num_q_per_iter, kv_len_alignment, max_num_q_per_iter, true);
+      sc.update(input.head_dim, input.q_buffer_elem_size,
+                input.logits_buffer_elem_size, input.output_buffer_elem_size,
+                max_num_q_per_iter, max_num_q_per_iter, n);
      metadata_ptr->attention_scratchpad_size_per_thread =
-          ((max_attention_scratchpad_size + 63) / 64) * 64;
-
-      int32_t max_reduction_q_head_tile_size = 0;
-      for (const ReductionWorkItemGroup& item : reduce_workitems) {
-        const bool curr_use_gqa =
-            use_gqa_fast_path || (supports_gqa && item.q_token_id_num == 1);
-        const int32_t curr_q_heads_per_kv =
-            curr_use_gqa ? original_q_head_per_kv : 1;
-
-        max_reduction_q_head_tile_size =
-            std::max(max_reduction_q_head_tile_size,
-                     item.q_token_id_num * curr_q_heads_per_kv);
-      }
+          ((sc.get_thread_scratchpad_size() + 63) / 64) * 64;

      sc.update(0, metadata_ptr->reduction_split_num, input.head_dim,
-                max_reduction_q_head_tile_size, input.output_buffer_elem_size);
+                q_head_per_kv * split_kv_q_token_num_threshold,
+                input.output_buffer_elem_size);
      metadata_ptr->reduction_scratchpad_size_per_kv_head =
          ((sc.get_reduction_scratchpad_size() + 63) / 64) * 64;
    }
@@ -323,6 +323,8 @@ class AttentionImpl<ISA::VSX, scalar_t, head_dim> {
      const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
      const int64_t block_size, const int64_t block_size_stride,
      const float k_inv = 0.0f, const float v_inv = 0.0f) {
+    // k_inv and v_inv are unused on VSX: FP8 KV cache is not supported on
+    // PowerPC. The parameters are present to match the common interface.
 #pragma omp parallel for collapse(2)
    for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
      for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -4,7 +4,7 @@
 #if defined(__x86_64__)
  // x86 implementation
  #include "cpu_types_x86.hpp"
-#elif defined(__powerpc__)
+#elif defined(__POWER9_VECTOR__)
  // ppc implementation
  #include "cpu_types_vsx.hpp"
 #elif defined(__s390x__)
@@ -41,4 +41,4 @@ inline int get_max_threads() {
 }
 }  // namespace cpu_utils

-#endif
+#endif
@@ -214,18 +214,11 @@ struct BF16Vec32 : public Vec<BF16Vec32> {

  explicit BF16Vec32(const BF16Vec8& v) {
    fixed_u16x8_t u16_val = bf16_to_u16(v.reg);
-    // Widen LMUL_128 → LMUL_256 so vslideup operands share a type.
-    // At VLEN=256 this is mf2→m1 (both integer); at VLEN=128 it is m1→m2.
-    fixed_u16x16_t ext =
-        RVVI4(__riscv_vlmul_ext_v_u16, LMUL_128, _u16, LMUL_256)(u16_val);
-    // Build 16-element half: place the 8 elements at offsets 0 and 8.
-    fixed_u16x16_t half = RVVI(__riscv_vmv_v_x_u16, LMUL_256)(0, 16);
-    half = RVVI(__riscv_vslideup_vx_u16, LMUL_256)(half, ext, 0, 8);
-    half = RVVI(__riscv_vslideup_vx_u16, LMUL_256)(half, ext, 8, 16);
-    // Double to LMUL_512 (m1→m2 at VLEN=256, m2→m4 at VLEN=128).
-    fixed_u16x32_t dst =
-        RVVI4(__riscv_vcreate_v_u16, LMUL_256, _u16, LMUL_512)(half, half);
-    reg = RVVI4(__riscv_vreinterpret_v_u16, LMUL_512, _bf16, LMUL_512)(dst);
+    fixed_u16x32_t u16_combined =
+        RVVI4(__riscv_vcreate_v_u16, LMUL_128, _u16, LMUL_512)(
+            u16_val, u16_val, u16_val, u16_val);
+    reg = RVVI4(__riscv_vreinterpret_v_u16, LMUL_512, _bf16,
+                LMUL_512)(u16_combined);
  };

  void save(void* ptr) const {
@@ -630,29 +623,17 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
            data.reg, data.reg)) {};
  explicit FP32Vec16(const FP32Vec16& data) : reg(data.reg) {};
  explicit FP32Vec16(int64_t value, const FP32Vec16& lut) {
-    // Split into two 32-bit halves to avoid u64 @ LMUL_1024 (m8 on
-    // VLEN=128 / m4 on VLEN=256), which causes heavy register spilling.
-    constexpr int HALF = VEC_ELEM_NUM / 2;
-    const auto q = static_cast<uint64_t>(value);
-    const uint32_t lo = static_cast<uint32_t>(q);
-    const uint32_t hi = static_cast<uint32_t>(q >> 32);
-
-    auto lane_ids = RVVI(__riscv_vid_v_u32, LMUL_256)(HALF);
-    auto shifts = RVVI(__riscv_vsll_vx_u32, LMUL_256)(lane_ids, 2, HALF);
-
-    auto packed_lo = RVVI(__riscv_vmv_v_x_u32, LMUL_256)(lo, HALF);
-    auto idx_lo = RVVI(__riscv_vand_vx_u32, LMUL_256)(
-        RVVI(__riscv_vsrl_vv_u32, LMUL_256)(packed_lo, shifts, HALF), 0xF,
-        HALF);
-
-    auto packed_hi = RVVI(__riscv_vmv_v_x_u32, LMUL_256)(hi, HALF);
-    auto idx_hi = RVVI(__riscv_vand_vx_u32, LMUL_256)(
-        RVVI(__riscv_vsrl_vv_u32, LMUL_256)(packed_hi, shifts, HALF), 0xF,
-        HALF);
-
-    auto idx =
-        RVVI4(__riscv_vcreate_v_u32, LMUL_256, _u32, LMUL_512)(idx_lo, idx_hi);
-    reg = RVVI(__riscv_vrgather_vv_f32, LMUL_512)(lut.reg, idx, VEC_ELEM_NUM);
+    const uint64_t q_values = static_cast<uint64_t>(value);
+    auto packed = RVVI(__riscv_vmv_v_x_u64, LMUL_1024)(q_values, VEC_ELEM_NUM);
+    auto lane_ids = RVVI(__riscv_vid_v_u64, LMUL_1024)(VEC_ELEM_NUM);
+    auto shifts =
+        RVVI(__riscv_vsll_vx_u64, LMUL_1024)(lane_ids, 2, VEC_ELEM_NUM);
+    auto shifted =
+        RVVI(__riscv_vsrl_vv_u64, LMUL_1024)(packed, shifts, VEC_ELEM_NUM);
+    auto idx64 =
+        RVVI(__riscv_vand_vx_u64, LMUL_1024)(shifted, 0xF, VEC_ELEM_NUM);
+    auto idx32 = RVVI(__riscv_vnsrl_wx_u32, LMUL_512)(idx64, 0, VEC_ELEM_NUM);
+    reg = RVVI(__riscv_vrgather_vv_f32, LMUL_512)(lut.reg, idx32, VEC_ELEM_NUM);
  }
  explicit FP32Vec16(const FP16Vec16& v);

@@ -344,133 +344,53 @@ struct FP32Vec8 : public Vec<FP32Vec8> {

    return result;
  }
+
  FP32Vec8 exp() const {
-    f32x4x2_t out;
-    const __vector float log2e = vec_splats(1.44269504088896341f);
-    const __vector float one = vec_splats(1.0f);
-    const __vector float min_x = vec_splats(-87.3f);
-    const __vector float max_x = vec_splats(88.7f);
-
-    // 5th-degree minimax polynomial for 2^r (r in [0,1))
-    const __vector float c1 = vec_splats(0.6931471805599453f);
-    const __vector float c2 = vec_splats(0.240226506959101f);
-    const __vector float c3 = vec_splats(0.05550410866482158f);
-    const __vector float c4 = vec_splats(0.009618129107628477f);
-    const __vector float c5 = vec_splats(0.0013333558146428443f);
-
-    for (int i = 0; i < 2; i++) {
-      __vector float x = reg.val[i];
-      x = vec_max(x, min_x);
-      x = vec_min(x, max_x);
-
-      __vector float y = vec_mul(x, log2e);
-
-      __vector float kf = vec_floor(y);
-      __vector float r = vec_sub(y, kf);
-
-      // Convert float to signed integer. Use vec_cts for PowerPC AltiVec
-      // compatibility.
-      __vector signed int k = vec_cts(kf, 0);
-      const __vector signed int min_k = vec_splats((signed int)-126);
-      const __vector signed int max_k = vec_splats((signed int)127);
-      k = vec_min(vec_max(k, min_k), max_k);
-
-      // Build 2^k from exponent bits
-      __vector signed int exp_int = vec_add(k, vec_splats((signed int)127));
-      __vector unsigned int bits = (__vector unsigned int)exp_int;
-      bits = vec_sl(bits, vec_splats((unsigned int)23));
-      __vector float pow2k = (__vector float)bits;
-
-      // Improved minimax polynomial
-      __vector float poly = vec_madd(c5, r, c4);
-      poly = vec_madd(poly, r, c3);
-      poly = vec_madd(poly, r, c2);
-      poly = vec_madd(poly, r, c1);
-      poly = vec_madd(poly, r, one);
-
-      out.val[i] = vec_mul(pow2k, poly);
-    }
-    return FP32Vec8(out);
+    // TODO: Vectorize this
+    AliasReg ar;
+    ar.reg = reg;
+    f32x4x4_t ret;
+    ret.val[0][0] = std::exp(ar.values[0]);
+    ret.val[0][1] = std::exp(ar.values[1]);
+    ret.val[0][2] = std::exp(ar.values[2]);
+    ret.val[0][3] = std::exp(ar.values[3]);
+    ret.val[1][0] = std::exp(ar.values[4]);
+    ret.val[1][1] = std::exp(ar.values[5]);
+    ret.val[1][2] = std::exp(ar.values[6]);
+    ret.val[1][3] = std::exp(ar.values[7]);
+    return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
  }

  FP32Vec8 tanh() const {
-    const __vector float one = vec_splats(1.0f);
-    const __vector float two = vec_splats(2.0f);
-    const __vector float zero = vec_splats(0.0f);
-    const __vector float sat = vec_splats(9.0f);
-
-    f32x4x2_t out;
-
-    for (int i = 0; i < 2; i++) {
-      __vector float x = reg.val[i];
-      __vector float ax = vec_abs(x);
-
-      __vector bool int mask = vec_cmpge(x, zero);
-      __vector float sign = vec_sel(vec_splats(-1.0f), one, mask);
-
-      __vector bool int saturated = vec_cmpge(ax, sat);
-
-      __vector float two_x = vec_mul(x, two);
-      f32x4x2_t tmp;
-      tmp.val[0] = two_x;
-      tmp.val[1] = two_x;
-      FP32Vec8 temp_vec(tmp);
-      vector float e = temp_vec.exp().reg.val[0];
-
-      vector float num = vec_sub(e, one);
-      vector float den = vec_add(e, one);
-      vector float t = vec_div(num, den);
-
-      out.val[i] = vec_sel(t, sign, saturated);
-    }
-    return FP32Vec8(out);
+    // TODO: Vectorize this
+    AliasReg ar;
+    ar.reg = reg;
+    f32x4x4_t ret;
+    ret.val[0][0] = std::tanh(ar.values[0]);
+    ret.val[0][1] = std::tanh(ar.values[1]);
+    ret.val[0][2] = std::tanh(ar.values[2]);
+    ret.val[0][3] = std::tanh(ar.values[3]);
+    ret.val[1][0] = std::tanh(ar.values[4]);
+    ret.val[1][1] = std::tanh(ar.values[5]);
+    ret.val[1][2] = std::tanh(ar.values[6]);
+    ret.val[1][3] = std::tanh(ar.values[7]);
+    return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
  }

  FP32Vec8 er() const {
-    const vector float a1 = vec_splats(0.254829592f);
-    const vector float a2 = vec_splats(-0.284496736f);
-    const vector float a3 = vec_splats(1.421413741f);
-    const vector float a4 = vec_splats(-1.453152027f);
-    const vector float a5 = vec_splats(1.061405429f);
-    const vector float p = vec_splats(0.3275911f);
-    const vector float one = vec_splats(1.0f);
-    const vector float zero = vec_splats(0.0f);
-    const vector float sat = vec_splats(6.0f);
-
-    f32x4x2_t ret;
-
-    for (int i = 0; i < 2; i++) {
-      vector float x = reg.val[i];
-      vector float ax = vec_abs(x);
-
-      vector bool int mask = vec_cmpge(x, zero);
-      vector float sign = vec_sel(vec_splats(-1.0f), one, mask);
-
-      vector bool int saturated = vec_cmpge(ax, sat);
-
-      vector float t = vec_div(one, vec_madd(p, ax, one));
-
-      vector float poly = a5;
-      poly = vec_madd(poly, t, a4);
-      poly = vec_madd(poly, t, a3);
-      poly = vec_madd(poly, t, a2);
-      poly = vec_madd(poly, t, a1);
-      poly = vec_mul(poly, t);
-
-      vector float x_squared = vec_mul(x, x);
-      vector float neg_x_squared = vec_mul(vec_splats(-1.0f), x_squared);
-      f32x4x2_t tmp;
-      tmp.val[0] = neg_x_squared;
-      tmp.val[1] = neg_x_squared;
-      FP32Vec8 exp_input(tmp);
-      vector float exp_term = exp_input.exp().reg.val[0];
-
-      vector float y = vec_nmsub(poly, exp_term, one);
-      vector float erf_val = vec_mul(sign, y);
-
-      ret.val[i] = vec_sel(erf_val, sign, saturated);
-    }
-    return FP32Vec8(ret);
+    // TODO: Vectorize this
+    AliasReg ar;
+    ar.reg = reg;
+    f32x4x4_t ret;
+    ret.val[0][0] = std::erf(ar.values[0]);
+    ret.val[0][1] = std::erf(ar.values[1]);
+    ret.val[0][2] = std::erf(ar.values[2]);
+    ret.val[0][3] = std::erf(ar.values[3]);
+    ret.val[1][0] = std::erf(ar.values[4]);
+    ret.val[1][1] = std::erf(ar.values[5]);
+    ret.val[1][2] = std::erf(ar.values[6]);
+    ret.val[1][3] = std::erf(ar.values[7]);
+    return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
  }

  FP32Vec8 operator*(const FP32Vec8& b) const {
@@ -278,8 +278,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
  ops.def(
      "dynamic_4bit_int_moe("
      "Tensor x, Tensor topk_ids, Tensor topk_weights,"
-      "Tensor w13_packed, Tensor w2_packed,"
-      "int hidden_size, int intermediate_size,"
+      "Tensor w13_packed, Tensor w2_packed, int H, int I, int I2,"
      "int group_size, bool apply_router_weight_on_input, int activation_kind"
      ") -> Tensor");

@@ -76,14 +76,14 @@ inline int64_t get_available_l2_size() {
    if (l2_cache_size == 0) {
      l2_cache_size = 256 * 1024;
    }
-    return static_cast<int64_t>(l2_cache_size) >> 1;
+    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;
+    return l2_cache_size >> 1;  // use 50% of L2 cache
  }();
  return size;
 #endif
@@ -669,14 +669,6 @@ __device__ __forceinline__ T gelu_quick_kernel(const T& x) {
  return (T)(((float)x) / (1.0f + expf(-1.702f * (float)x)));
 }

-template <typename T>
-__device__ __forceinline__ T relu_squared_kernel(const T& x) {
-  // relu(x)^2 — introduced in https://arxiv.org/abs/2109.08668v2
-  const float f = (float)x;
-  const float val = f > 0.0f ? f : 0.0f;
-  return (T)(val * val);
-}
-
 }  // namespace vllm

 void gelu_new(torch::stable::Tensor& out,    // [..., d]
@@ -696,9 +688,3 @@ void gelu_quick(torch::stable::Tensor& out,    // [..., d]
 {
  LAUNCH_ACTIVATION_KERNEL(vllm::gelu_quick_kernel);
 }
-
-void relu_squared(torch::stable::Tensor& out,    // [..., d]
-                  torch::stable::Tensor& input)  // [..., d]
-{
-  LAUNCH_ACTIVATION_KERNEL(vllm::relu_squared_kernel);
-}
@@ -1,18 +1,13 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 //
-// Router GEMM: activation(T) x weight(fp32) -> fp32, M<=32, for the
-// supported (E, H) pairs listed at the bottom of this file.
+// Router GEMM: activation(T) x weight(fp32) -> fp32, H=3072, E=256, M<=32.
 // Supports bf16 or fp32 activation; weight is always fp32.
 // Adapted from dsv3_router_gemm_float_out.cu.
-// (E=256, H=6144) bf16 uses a B300-tuned wide-block geometry; see
-// invokeFp32RouterGemm.

 #include <cuda_bf16.h>
 #include <cuda_runtime.h>

-#include <type_traits>
-
 // ---------------------------------------------------------------------------
 // Load helpers
 // ---------------------------------------------------------------------------
@@ -78,113 +73,94 @@ __device__ __forceinline__ void load_activation<__nv_bfloat16, 8>(
 // InputT : type of activation (float or __nv_bfloat16)
 // Weight is always fp32; output is always fp32.
 // VPT = 16 / sizeof(InputT):  4 for fp32, 8 for bf16
-// Each block computes kEPB expert columns; wider blocks / kEPB > 1 are
-// selected per (shape, M) in invokeFp32RouterGemm (B300-tuned, see below).
-// kTGroups > 1 splits the tokens across groups of kBlockSize threads within
-// the block: all groups scan the same weight K-slices (group 0 misses to
-// DRAM, later groups hit L1) so weight traffic stays 1x, while per-thread
-// accumulator registers drop by kTGroups (at M=16 the 32 fp32 accumulators
-// push the kernel to 128 regs/thread and 1 block/SM).
-template <typename InputT, int kBlockSize, int kNumTokens, int kEPB,
-          int kNumExperts, int kHiddenDim, int kTGroups = 1>
-__global__ __launch_bounds__(
-    kBlockSize* kTGroups, 1) void fp32_router_gemm_kernel(float* out,
-                                                          InputT const* mat_a,
-                                                          float const* mat_b) {
+template <typename InputT, int kBlockSize, int kNumTokens, int kNumExperts,
+          int kHiddenDim>
+__global__ __launch_bounds__(128, 1) void fp32_router_gemm_kernel(
+    float* out, InputT const* mat_a, float const* mat_b) {
  constexpr int VPT = 16 / sizeof(InputT);
  constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
  constexpr int k_iterations = kHiddenDim / k_elems_per_k_iteration;
-  static_assert(kHiddenDim % k_elems_per_k_iteration == 0);
-  static_assert(kNumTokens % kTGroups == 0);
  constexpr int kWarpSize = 32;
-  constexpr int kNumWarps = kBlockSize / kWarpSize;  // per token group
-  constexpr int kMG = kNumTokens / kTGroups;         // tokens per group
+  constexpr int kNumWarps = kBlockSize / kWarpSize;

-  int const e_base = blockIdx.x * kEPB;
-  int const tid = threadIdx.x % kBlockSize;
-  int const m0 = (threadIdx.x / kBlockSize) * kMG;
+  int const n_idx = blockIdx.x;
+  int const tid = threadIdx.x;
  int const warpId = tid / kWarpSize;
  int const laneId = tid % kWarpSize;

-  float acc[kMG][kEPB] = {};
-  __shared__ float sm_reduction[kNumTokens][kEPB][kNumWarps];
+  float acc[kNumTokens] = {};
+  __shared__ float sm_reduction[kNumTokens][kNumWarps];
+
+  float const* b_col = mat_b + n_idx * kHiddenDim;
+
+  int k_bases[k_iterations];
+#pragma unroll
+  for (int ki = 0; ki < k_iterations; ki++) {
+    k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
+  }

 #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
  cudaGridDependencySynchronize();
-  // Fire the PDL trigger right after our own wait instead of at kernel end:
-  // a gridsync-ing consumer is unaffected (its wait always targets full grid
-  // completion), while a consumer that reads none of our outputs (e.g. the
-  // NVFP4 activation quant, which reads the same hidden_states) can launch
-  // now and fully overlap this kernel's body.
-  cudaTriggerProgrammaticLaunchCompletion();
 #endif

-#pragma unroll
  for (int ki = 0; ki < k_iterations; ki++) {
-    int const k_base = ki * k_elems_per_k_iteration + tid * VPT;
+    int const k_base = k_bases[ki];

-    float b_float[kEPB][VPT];
-#pragma unroll
-    for (int e = 0; e < kEPB; e++) {
-      load_weight<VPT>(mat_b + (e_base + e) * kHiddenDim + k_base, b_float[e]);
-    }
+    float b_float[VPT];
+    load_weight<VPT>(b_col + k_base, b_float);

 #pragma unroll
-    for (int m_idx = 0; m_idx < kMG; m_idx++) {
+    for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
      float a_float[VPT];
-      load_activation<InputT, VPT>(
-          mat_a + (size_t)(m0 + m_idx) * kHiddenDim + k_base, a_float);
+      load_activation<InputT, VPT>(mat_a + m_idx * kHiddenDim + k_base,
+                                   a_float);
 #pragma unroll
-      for (int e = 0; e < kEPB; e++) {
-#pragma unroll
-        for (int k = 0; k < VPT; k++) {
-          acc[m_idx][e] += a_float[k] * b_float[e][k];
-        }
+      for (int k = 0; k < VPT; k++) {
+        acc[m_idx] += a_float[k] * b_float[k];
      }
    }
  }

  // Warp-level butterfly reduction
 #pragma unroll
-  for (int m = 0; m < kMG; m++) {
-#pragma unroll
-    for (int e = 0; e < kEPB; e++) {
-      float sum = acc[m][e];
-      sum += __shfl_xor_sync(0xffffffff, sum, 16);
-      sum += __shfl_xor_sync(0xffffffff, sum, 8);
-      sum += __shfl_xor_sync(0xffffffff, sum, 4);
-      sum += __shfl_xor_sync(0xffffffff, sum, 2);
-      sum += __shfl_xor_sync(0xffffffff, sum, 1);
-      if (laneId == 0) sm_reduction[m0 + m][e][warpId] = sum;
-    }
+  for (int m = 0; m < kNumTokens; m++) {
+    float sum = acc[m];
+    sum += __shfl_xor_sync(0xffffffff, sum, 16);
+    sum += __shfl_xor_sync(0xffffffff, sum, 8);
+    sum += __shfl_xor_sync(0xffffffff, sum, 4);
+    sum += __shfl_xor_sync(0xffffffff, sum, 2);
+    sum += __shfl_xor_sync(0xffffffff, sum, 1);
+    if (laneId == 0) sm_reduction[m][warpId] = sum;
  }

  __syncthreads();

-  // Parallel finalize: one thread per (m, e) output.
-  for (int idx = threadIdx.x; idx < kNumTokens * kEPB;
-       idx += kBlockSize * kTGroups) {
-    int const m = idx / kEPB;
-    int const e = idx % kEPB;
-    float final_sum = 0.0f;
+  if (tid == 0) {
 #pragma unroll
-    for (int w = 0; w < kNumWarps; w++) final_sum += sm_reduction[m][e][w];
-    out[m * kNumExperts + e_base + e] = final_sum;
+    for (int m = 0; m < kNumTokens; m++) {
+      float final_sum = 0.0f;
+#pragma unroll
+      for (int w = 0; w < kNumWarps; w++) final_sum += sm_reduction[m][w];
+      out[m * kNumExperts + n_idx] = final_sum;
+    }
  }
+
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
+  cudaTriggerProgrammaticLaunchCompletion();
+#endif
 }

 // ---------------------------------------------------------------------------
 // Launcher
 // ---------------------------------------------------------------------------

-template <typename InputT, int kBlockSize, int kEPB, int kNumTokens,
-          int kNumExperts, int kHiddenDim, int kTGroups = 1>
-static void launchFp32RouterGemm(float* output, InputT const* mat_a,
-                                 float const* mat_b, cudaStream_t stream) {
-  static_assert(kNumExperts % kEPB == 0);
+template <typename InputT, int kNumTokens, int kNumExperts, int kHiddenDim>
+void invokeFp32RouterGemm(float* output, InputT const* mat_a,
+                          float const* mat_b, cudaStream_t stream) {
+  constexpr int kBlockSize = 128;
  cudaLaunchConfig_t config;
-  config.gridDim = kNumExperts / kEPB;
-  config.blockDim = kBlockSize * kTGroups;
+  config.gridDim = kNumExperts;
+  config.blockDim = kBlockSize;
  config.dynamicSmemBytes = 0;
  config.stream = stream;
  cudaLaunchAttribute attrs[1];
@@ -192,112 +168,16 @@ static void launchFp32RouterGemm(float* output, InputT const* mat_a,
  attrs[0].val.programmaticStreamSerializationAllowed = 1;
  config.numAttrs = 1;
  config.attrs = attrs;
-  cudaLaunchKernelEx(
-      &config,
-      fp32_router_gemm_kernel<InputT, kBlockSize, kNumTokens, kEPB, kNumExperts,
-                              kHiddenDim, kTGroups>,
-      output, mat_a, mat_b);
-}
-
-static bool isBlackwellFamily() {
-  static int sm = []() {
-    int dev = 0, major = 0, minor = 0;
-    cudaGetDevice(&dev);
-    cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, dev);
-    cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, dev);
-    return major * 10 + minor;
-  }();
-  return sm >= 100;
-}
-
-template <typename InputT, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeFp32RouterGemm(float* output, InputT const* mat_a,
-                          float const* mat_b, cudaStream_t stream) {
-  // Geometry tuned on B300 per supported shape, bf16 activation, under a
-  // production-fidelity harness (CUDA-graph replay, per-layer cold weights).
-  // GLM-5.2 (E=256, H=6144):
-  //   M <= 4        : BS=768, EPB=1 (2.7us vs cast+cuBLAS 8.1us at M=1)
-  //   M in [5, 15]
-  //   or odd        : BS=384, EPB=2 (crossover vs BS=768 measured in (4, 8))
-  //   M >= 16, even : BS=192, EPB=2, 2 token groups (M=16 4.79us vs 5.04,
-  //                   M=24 5.71 vs 6.38, M=32 6.79 vs 7.72; M=12 loses at
-  //                   0.97x, so the boundary is 16).
-  // Only enabled on the Blackwell family where it was validated; Hopper and
-  // other shapes / fp32 activation keep the legacy geometry.
-  if constexpr (std::is_same_v<InputT, __nv_bfloat16> && kNumExperts == 256 &&
-                kHiddenDim == 6144) {
-    if (!isBlackwellFamily()) {
-      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-      return;
-    }
-    if constexpr (kNumTokens <= 4) {
-      launchFp32RouterGemm<InputT, 768, 1, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-    } else if constexpr (kNumTokens >= 16 && kNumTokens % 2 == 0) {
-      launchFp32RouterGemm<InputT, 192, 2, kNumTokens, kNumExperts, kHiddenDim,
-                           2>(output, mat_a, mat_b, stream);
-    } else {
-      launchFp32RouterGemm<InputT, 384, 2, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-    }
-  } else if constexpr (std::is_same_v<InputT, __nv_bfloat16> &&
-                       kNumExperts == 128 && kHiddenDim == 6144) {
-    // MiniMax-M3. Legacy 128/1 only fills 128 blocks and pays the same
-    // accumulator register cliffs; B300 sweep:
-    //   even M in [6, 10] : BS=384, EPB=1, 2 token groups (1.26-1.43x)
-    //   even M >= 12      : BS=192, EPB=1, 2 token groups (1.59-1.66x at
-    //                       M >= 18; re-measured on B300+B200: 192 also wins
-    //                       M=12/14 by 5-11%% on both, ties 384 at 16)
-    //   M <= 5 / odd      : BS=384, EPB=1 (1.03-1.19x)
-    if (!isBlackwellFamily()) {
-      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-      return;
-    }
-    if constexpr (kNumTokens >= 12 && kNumTokens % 2 == 0) {
-      launchFp32RouterGemm<InputT, 192, 1, kNumTokens, kNumExperts, kHiddenDim,
-                           2>(output, mat_a, mat_b, stream);
-    } else if constexpr (kNumTokens >= 6 && kNumTokens % 2 == 0) {
-      launchFp32RouterGemm<InputT, 384, 1, kNumTokens, kNumExperts, kHiddenDim,
-                           2>(output, mat_a, mat_b, stream);
-    } else {
-      launchFp32RouterGemm<InputT, 384, 1, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-    }
-  } else if constexpr (std::is_same_v<InputT, __nv_bfloat16> &&
-                       kNumExperts == 256 && kHiddenDim == 3072) {
-    // MiniMax-M2/M2.5. The 3.1MB weight is latency-floor bound at small M
-    // (legacy already optimal); token groups win only at even M >= 8
-    // (1.05-1.17x). EPB crossover measured between 12 and 16.
-    if (!isBlackwellFamily()) {
-      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-      return;
-    }
-    if constexpr (kNumTokens >= 14 && kNumTokens % 2 == 0) {
-      // M=14 originally measured 0.91x and stayed on legacy; two fresh
-      // sweeps (B300 dev1 + B200) both put 192/2/tg2 ahead by 3.5-4%%.
-      launchFp32RouterGemm<InputT, 192, 2, kNumTokens, kNumExperts, kHiddenDim,
-                           2>(output, mat_a, mat_b, stream);
-    } else if constexpr (kNumTokens >= 8 && kNumTokens <= 12 &&
-                         kNumTokens % 2 == 0) {
-      launchFp32RouterGemm<InputT, 192, 1, kNumTokens, kNumExperts, kHiddenDim,
-                           2>(output, mat_a, mat_b, stream);
-    } else {
-      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
-          output, mat_a, mat_b, stream);
-    }
-  } else {
-    launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
-        output, mat_a, mat_b, stream);
-  }
+  cudaLaunchKernelEx(&config,
+                     fp32_router_gemm_kernel<InputT, kBlockSize, kNumTokens,
+                                             kNumExperts, kHiddenDim>,
+                     output, mat_a, mat_b);
 }

 // ---------------------------------------------------------------------------
 // Explicit instantiations: M=1..32, for both input types, for the supported
 // (E, H) pairs:  (256, 3072) [MiniMax-M2/M2.5],  (128, 6144) [MiniMax-M3]
-// and  (256, 6144) [GLM-5.2].
+// and  (256, 6144) [GLM-5].
 // ---------------------------------------------------------------------------

 #define INSTANTIATE(T, M, E, H)                                    \
@@ -26,7 +26,7 @@ static constexpr int FP32_MAX_TOKENS = 32;

 // Supported (hidden_dim, num_experts) pairs (must match the instantiations in
 // fp32_router_gemm.cu): (3072, 256) for MiniMax-M2/M2.5, (6144, 128) for M3,
-// (6144, 256) for GLM-5.2.
+// (6144, 256) for GLM-5.
 static inline bool fp32_router_gemm_supported(int hidden_dim, int num_experts) {
  return (hidden_dim == 3072 && num_experts == 256) ||
         (hidden_dim == 6144 && num_experts == 128) ||
@@ -39,10 +39,12 @@
 * The IQ/IK warps address the index_q/index_k sub-blocks *inside* qkv at the
 * fixed physical offsets (nq+2*nkv)*128 and (nq+2*nkv+niq)*128.
 *
- * Dense vs sparse row layout and index-branch processing are separate template
- * choices. Skip-index-topk reuse layers still have sparse rows and insert main
- * K/V cache entries, but compile away index_q/index_k work and index-cache
- * writes.
+ * Dense vs sparse is a compile-time choice via the ``kIsSparse``/``kInsertKV``
+ * template bools (3 instantiations: dense <false,false>, sparse-profiling
+ * <true,false>, sparse-serving <true,true>), so the index slots, the V slots
+ * and the cache inserts fold away entirely on paths that don't use them. The
+ * dense layer passes no caches/index: norm+RoPE happens in place and the
+ * generic ``Attention`` layer owns the cache write.
 *
 * Q/K and (sparse) index_q/index_k are all rewritten in place inside the fused
 * ``qkv`` tensor.  Caches (bf16) are scatter-written by slot.
@@ -221,25 +223,10 @@ __device__ __forceinline__ void storeCacheElems(
    // model dtype directly. FP8 cache dtypes use the conversion path below.
    storeElems<scalar_t>(reinterpret_cast<scalar_t*>(dst), elems);
  } else {
-#ifdef USE_ROCM
-    // Match ROCm's model-dtype materialization before FP8 cache conversion.
-    using Converter = vllm::_typeConvert<scalar_t>;
-    using rounded_t = typename Converter::hip_type;
-    rounded_t rounded[kElemsPerLane];
-  #pragma unroll
-    for (int i = 0; i < kElemsPerLane; i++) {
-      rounded[i] = Converter::convert(elems[i]);
-    }
-  #pragma unroll
-    for (int i = 0; i < kElemsPerLane; i++) {
-      dst[i] = fp8::scaled_convert<cache_t, rounded_t, kv_dt>(rounded[i], 1.0f);
-    }
-#else
-  #pragma unroll
+#pragma unroll
    for (int i = 0; i < kElemsPerLane; i++) {
      dst[i] = fp8::scaled_convert<cache_t, float, kv_dt>(elems[i], 1.0f);
    }
-#endif
  }
 }

@@ -275,25 +262,20 @@ __device__ __forceinline__ void storeElemsFp8(
 // Grid: 1D, ceil(num_tokens * slots_per_token / warps_per_block).
 // Each warp = one (token, slot).
 //
-// `kHasIndex`, `kProcessIndex`, and `kInsertKV` are compile-time template
-// bools, so branch decisions that distinguish the dense layer from the sparse
-// layer (index slots, KV/index inserts, V slots) fold away per instantiation.
-// Slots per token:
+// `kIsSparse` and `kInsertKV` are compile-time template bools, so all the
+// branch decisions that distinguish the dense layer from the sparse layer
+// (index slots, KV/index inserts, V slots) fold away per instantiation.
+// Three instantiations are built: dense <false,false>, sparse-profiling
+// <true,false> and sparse-serving <true,true>.  Slots per token:
 //     Q : nq                            (always — norm+RoPE)
 //     K : nkv                           (always — norm+RoPE; +K-cache insert)
 //     V : nkv  only if kInsertKV        (V-cache insert; no warps in dense)
-//     IQ: niq  only if kProcessIndex    (norm+RoPE)
-//     IK: 1    only if kProcessIndex    (norm+RoPE; +index-cache insert)
+//     IQ: niq  only if kIsSparse        (norm+RoPE)
+//     IK: 1    only if kIsSparse        (norm+RoPE; +index-cache insert)
 // cache_t/kv_dt: main attention KV-cache dtype (auto/fp8). out_idx_t/kFp8Idx:
 // indexer index-K cache + index-Q output dtype (scalar_t or e4m3 byte).
-// kHasIndex means the qkv row is laid out as sparse [q|k|v|index_q|index_k].
-// kProcessIndex controls whether this launch actually norms/ropes the index
-// branch and writes index_q/index_k outputs. Skip-index-topk reuse layers keep
-// kHasIndex=true but set kProcessIndex=false.
 template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt,
-          typename out_idx_t, bool kHasIndex, bool kInsertKV,
-          bool kProcessIndex,
-          bool kFp8Idx>
+          typename out_idx_t, bool kIsSparse, bool kInsertKV, bool kFp8Idx>
 __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
    scalar_t* __restrict__ qkv,  // [N, qkv_row] in/out (packs index if sparse)
    scalar_t* __restrict__ q_out,         // [N, nq*128] contiguous, or nullptr
@@ -306,15 +288,16 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
    int64_t const* __restrict__ positions,       // [N] i64
    int64_t const* __restrict__ slot_mapping,    // main K/V slots or nullptr
    int64_t const* __restrict__ index_slot_mapping,  // index K slots/nullptr
-    cache_t* __restrict__ kv_cache,       // [nb,nkv,bs,2*128] or nullptr
+    cache_t* __restrict__ kv_cache,       // [nb,2,bs,nkv,128] or nullptr
    out_idx_t* __restrict__ index_cache,  // [nb*bs, 128]; scalar_t or e4m3 byte
    float const eps, int const rotary_dim, int const num_tokens, int const nq,
    int const nkv, int const niq, int const block_size,
-    // kv_cache strides (in elements) for logical shape [nb, nkv, bs, 2*128].
-    // The content (last) dim is always innermost-contiguous (stride 1), so the
-    // NHD/HND layout choice is captured by the head/token strides.
-    int64_t const kv_s_block, int64_t const kv_s_head, int64_t const kv_s_token,
-    int64_t const kv_s_dim) {
+    // kv_cache strides (in elements) for logical shape [nb, 2, bs, nkv, 128].
+    // The head_dim (last) dim is always innermost-contiguous (stride 1), so the
+    // NHD/HND layout choice is fully captured by these four strides: NHD keeps
+    // s_token < s_head, HND swaps them. dim_base addresses head_dim directly.
+    int64_t const kv_s_block, int64_t const kv_s_kv, int64_t const kv_s_token,
+    int64_t const kv_s_head) {
 #if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
  // _typeConvert<BFloat16> is unavailable on pre-Ampere; the M3 kernel only
  // runs with bf16/fp16 inputs in practice.  Discard the bf16 body there.
@@ -326,12 +309,9 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
    int const laneId = threadIdx.x % 32;
    int const globalWarpIdx = blockIdx.x * warpsPerBlock + (threadIdx.x / 32);

-    static_assert(!kProcessIndex || kHasIndex,
-                  "index processing requires sparse row layout");
-
    // Slot layout (compile-time gated: dense has neither V nor index slots).
    int const v_slots = kInsertKV ? nkv : 0;
-    int const idx_slots = kProcessIndex ? niq + 1 : 0;
+    int const idx_slots = kIsSparse ? niq + 1 : 0;
    int const slots_per_token = nq + nkv + v_slots + idx_slots;

    int const tokenIdx = globalWarpIdx / slots_per_token;
@@ -342,14 +322,14 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
    int const k_begin = nq;
    int const v_begin = nq + nkv;             // valid only when kInsertKV
    int const iq_begin = nq + nkv + v_slots;  // index block start
-    int const ik_slot = iq_begin + niq;       // valid only when kProcessIndex
+    int const ik_slot = iq_begin + niq;       // valid only when kIsSparse

    bool const isQ = slot < k_begin;
    bool const isK = slot >= k_begin && slot < v_begin;
    bool isV = false;
    if constexpr (kInsertKV) isV = slot >= v_begin && slot < v_begin + nkv;
    bool isIQ = false, isIK = false;
-    if constexpr (kProcessIndex) {
+    if constexpr (kIsSparse) {
      isIQ = slot >= iq_begin && slot < ik_slot;
      isIK = slot == ik_slot;
    }
@@ -357,7 +337,7 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
    int const dim_base = laneId * kElemsPerLane;
    // Physical row width of qkv: the dense layer packs [q|k|v]; the sparse
    // layer additionally packs [index_q (niq heads) | index_k (1 head)].
-    int const qkv_row = (nq + 2 * nkv + (kHasIndex ? (niq + 1) : 0)) * kHeadDim;
+    int const qkv_row = (nq + 2 * nkv + (kIsSparse ? (niq + 1) : 0)) * kHeadDim;

    // ── Resolve source pointer + per-branch parameters. ────────────────────
    scalar_t* row_ptr = nullptr;       // in-place output location
@@ -388,13 +368,10 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
      row_ptr = qkv + static_cast<int64_t>(tokenIdx) * qkv_row +
                (nq + 2 * nkv + ih) * kHeadDim;
      norm_w = iq_norm_w;
-    } else if (isIK) {
-      // Single shared index key at (nq+2*nkv+niq)*128.
+    } else {  // isIK -- single shared index key at (nq+2*nkv+niq)*128.
      row_ptr = qkv + static_cast<int64_t>(tokenIdx) * qkv_row +
                (nq + 2 * nkv + niq) * kHeadDim;
      norm_w = ik_norm_w;
-    } else {
-      return;
    }

    // Store destination.  Q and index_q are gathered into dedicated contiguous
@@ -450,12 +427,9 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
    // ── Cache inserts (sparse serving only). ───────────────────────────────
    if constexpr (kInsertKV) {
      // Guard (not early-return) so every thread reaches the PDL trigger below.
-      int64_t sm = -1;
-      if (isK || isV) {
-        sm = slot_mapping[tokenIdx];
-      } else if constexpr (kProcessIndex) {
-        if (isIK) sm = index_slot_mapping[tokenIdx];
-      }
+      int64_t const sm = (isK || isV)
+                             ? slot_mapping[tokenIdx]
+                             : (isIK ? index_slot_mapping[tokenIdx] : -1);
      if (sm >= 0) {  // skip padded / unscheduled tokens
        if (isIK) {
          if constexpr (kFp8Idx) {
@@ -464,16 +438,16 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
            storeElems<scalar_t>(index_cache + sm * kHeadDim + dim_base, elems);
          }
        } else if (isK || isV) {
-          // kv_cache logical shape [num_blocks, nkv, block_size, 2*head_dim].
+          // kv_cache logical shape [num_blocks, 2, block_size, nkv, head_dim].
          // Paging is logical (block = sm/block_size, token = sm%block_size);
          // the physical NHD/HND layout is honoured via the passed strides.
          int64_t const b = sm / block_size;
          int64_t const t = sm % block_size;
          int const kv = isK ? 0 : 1;
-          int64_t const off = b * kv_s_block + head * kv_s_head +
-                              t * kv_s_token +
-                              (kv * kHeadDim + dim_base) * kv_s_dim;
-          storeCacheElems<scalar_t, cache_t, kv_dt>(kv_cache + off, elems);
+          int64_t const off =
+              b * kv_s_block + kv * kv_s_kv + t * kv_s_token + head * kv_s_head;
+          storeCacheElems<scalar_t, cache_t, kv_dt>(kv_cache + off + dim_base,
+                                                    elems);
        }
      }
    }
@@ -500,14 +474,14 @@ void launchFusedMiniMaxM3(
    int64_t const* index_slot_mapping, cache_t* kv_cache, void* index_cache,
    float const eps, int const rotary_dim, int const num_tokens, int const nq,
    int const nkv, int const niq, int const block_size,
-    int64_t const kv_s_block, int64_t const kv_s_head, int64_t const kv_s_token,
-    int64_t const kv_s_dim, bool const has_index, bool const insert_kv,
-    bool const process_index, bool const fp8_idx, cudaStream_t stream) {
+    int64_t const kv_s_block, int64_t const kv_s_kv, int64_t const kv_s_token,
+    int64_t const kv_s_head, bool const has_index, bool const insert_kv,
+    bool const fp8_idx, cudaStream_t stream) {
  // Index outputs are scalar_t (bf16) or e4m3 bytes (uint8_t); reinterpret the
  // void* pointers per instantiation in the LAUNCH macro.
  // Slot count must match the kernel's compile-time gating.
  int const v_slots = insert_kv ? nkv : 0;
-  int const idx_slots = process_index ? niq + 1 : 0;
+  int const idx_slots = has_index ? niq + 1 : 0;
  int const slots_per_token = nq + nkv + v_slots + idx_slots;

  constexpr int kBlockSize = 256;
@@ -534,59 +508,50 @@ void launchFusedMiniMaxM3(
  config.attrs = attrs;
  config.numAttrs = (sm_version >= 90) ? 1 : 0;

-  #define LAUNCH(HAS_INDEX, INSERT, PROCESS_INDEX, FP8, OUT_T)                 \
+  #define LAUNCH(IS_SPARSE, INSERT, FP8, OUT_T)                                \
    cudaLaunchKernelEx(                                                        \
        &config,                                                               \
        fusedMiniMaxM3QNormRopeKVInsertKernel<scalar_t, cache_t, kv_dt, OUT_T, \
-                                              HAS_INDEX, INSERT,               \
-                                              PROCESS_INDEX, FP8>,             \
+                                              IS_SPARSE, INSERT, FP8>,         \
        qkv, q_out, reinterpret_cast<OUT_T*>(index_q_out), q_norm_w, k_norm_w, \
        iq_norm_w, ik_norm_w, cos_sin_cache, positions, slot_mapping,          \
        index_slot_mapping, kv_cache, reinterpret_cast<OUT_T*>(index_cache),   \
        eps, rotary_dim, num_tokens, nq, nkv, niq, block_size, kv_s_block,     \
-        kv_s_head, kv_s_token, kv_s_dim)
+        kv_s_kv, kv_s_token, kv_s_head)
 #else
  // ROCm: standard kernel launch syntax (no PDL/stream serialization).
  // clang-format off
-  #define LAUNCH(HAS_INDEX, INSERT, PROCESS_INDEX, FP8, OUT_T)              \
-    fusedMiniMaxM3QNormRopeKVInsertKernel<                                  \
-        scalar_t, cache_t, kv_dt, OUT_T, HAS_INDEX, INSERT, PROCESS_INDEX,  \
-        FP8><<<grid, kBlockSize, 0, stream>>>(                              \
-        qkv, q_out, reinterpret_cast<OUT_T*>(index_q_out), q_norm_w,        \
-        k_norm_w, iq_norm_w, ik_norm_w, cos_sin_cache, positions,           \
-        slot_mapping, index_slot_mapping, kv_cache,                         \
-        reinterpret_cast<OUT_T*>(index_cache), eps, rotary_dim, num_tokens, \
-        nq, nkv, niq, block_size, kv_s_block, kv_s_head, kv_s_token,         \
-        kv_s_dim)
+  #define LAUNCH(IS_SPARSE, INSERT, FP8, OUT_T)                              \
+    fusedMiniMaxM3QNormRopeKVInsertKernel<scalar_t, cache_t, kv_dt, OUT_T,   \
+                                          IS_SPARSE, INSERT, FP8>            \
+        <<<grid, kBlockSize, 0, stream>>>(                                   \
+            qkv, q_out, reinterpret_cast<OUT_T*>(index_q_out), q_norm_w,     \
+            k_norm_w, iq_norm_w, ik_norm_w, cos_sin_cache, positions,        \
+            slot_mapping, index_slot_mapping, kv_cache,                      \
+            reinterpret_cast<OUT_T*>(index_cache), eps, rotary_dim,          \
+            num_tokens, nq, nkv, niq, block_size, kv_s_block, kv_s_kv,       \
+            kv_s_token, kv_s_head)
  // clang-format on
 #endif

  if (has_index) {
-    if (!process_index) {
-      if (insert_kv) {
-        LAUNCH(true, true, false, false, scalar_t);
-      } else {
-        LAUNCH(true, false, false, false, scalar_t);
-      }
-    } else if (insert_kv) {
+    if (insert_kv) {
      if (fp8_idx) {
-        LAUNCH(true, true, true, true,
-               uint8_t);  // sparse serving, fp8 index outputs
+        LAUNCH(true, true, true, uint8_t);  // sparse serving, fp8 index outputs
      } else {
-        LAUNCH(true, true, true, false, scalar_t);  // sparse serving, bf16
+        LAUNCH(true, true, false, scalar_t);  // sparse serving, bf16
      }
    } else {
      if (fp8_idx) {
-        LAUNCH(true, false, true, true,
-               uint8_t);  // sparse profiling, fp8 index_q
+        LAUNCH(true, false, true, uint8_t);  // sparse profiling, fp8 index_q
      } else {
-        LAUNCH(true, false, true, false, scalar_t);  // sparse profiling, bf16
+        LAUNCH(true, false, false, scalar_t);  // sparse profiling, bf16
      }
    }
  } else {
    // Dense layer: never has an index branch and never inserts here (the
    // generic Attention layer owns the KV insert).
-    LAUNCH(false, false, false, false, scalar_t);
+    LAUNCH(false, false, false, scalar_t);
  }
 #undef LAUNCH
 }
@@ -594,7 +559,6 @@ void launchFusedMiniMaxM3(
 }  // namespace minimax_m3_fused_ops
 }  // namespace vllm

-// clang-format off
 #define CALL_FUSED_MINIMAX_M3(_RAW_T, CACHE_T, KV_DTYPE)                       \
  vllm::minimax_m3_fused_ops::launchFusedMiniMaxM3<st, CACHE_T, KV_DTYPE>(     \
      reinterpret_cast<st*>(qkv.data_ptr()),                                   \
@@ -604,29 +568,24 @@ void launchFusedMiniMaxM3(
          : nullptr,                                                           \
      reinterpret_cast<st const*>(q_norm_weight.data_ptr()),                   \
      reinterpret_cast<st const*>(k_norm_weight.data_ptr()),                   \
-      process_index                                                            \
-          ? reinterpret_cast<st const*>(index_q_norm_weight->data_ptr())       \
-          : nullptr,                                                           \
-      process_index                                                            \
-          ? reinterpret_cast<st const*>(index_k_norm_weight->data_ptr())       \
-          : nullptr,                                                           \
+      has_index ? reinterpret_cast<st const*>(index_q_norm_weight->data_ptr()) \
+                : nullptr,                                                     \
+      has_index ? reinterpret_cast<st const*>(index_k_norm_weight->data_ptr()) \
+                : nullptr,                                                     \
      reinterpret_cast<st const*>(cos_sin_cache.data_ptr()),                   \
      reinterpret_cast<int64_t const*>(positions.data_ptr()),                  \
      insert_kv ? reinterpret_cast<int64_t const*>(slot_mapping->data_ptr())   \
                : nullptr,                                                     \
-      (insert_kv && process_index)                                             \
-          ? reinterpret_cast<int64_t const*>(                                  \
-                effective_index_slot_mapping->data_ptr())                      \
-          : nullptr,                                                           \
+      insert_kv ? reinterpret_cast<int64_t const*>(                            \
+                      effective_index_slot_mapping->data_ptr())                \
+                : nullptr,                                                     \
      insert_kv ? reinterpret_cast<CACHE_T*>(kv_cache->data_ptr()) : nullptr,  \
-      (insert_kv && process_index)                                             \
+      (insert_kv && has_index)                                                 \
          ? reinterpret_cast<void*>(index_cache->data_ptr())                   \
          : nullptr,                                                           \
      static_cast<float>(eps), static_cast<int>(rotary_dim), num_tokens, nq,   \
-      nkv, niq, static_cast<int>(block_size), kv_s_block, kv_s_head,           \
-      kv_s_token, kv_s_dim, has_index, insert_kv, process_index, fp8_idx,       \
-      stream)
-// clang-format on
+      nkv, niq, static_cast<int>(block_size), kv_s_block, kv_s_kv, kv_s_token, \
+      kv_s_head, has_index, insert_kv, fp8_idx, stream)

 // ────────────────────────────────────────────────────────────────────────────
 // Torch op wrapper
@@ -643,13 +602,13 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
    int64_t num_index_heads,                                  // niq; 0 => dense
    std::optional<torch::stable::Tensor> slot_mapping,        // [N] i64
    std::optional<torch::stable::Tensor> index_slot_mapping,  // [N] i64
-    std::optional<torch::stable::Tensor> kv_cache,     // [nb,nkv,bs,2*128]
+    std::optional<torch::stable::Tensor> kv_cache,     // [nb,2,bs,nkv,128]
    std::optional<torch::stable::Tensor> index_cache,  // [nb,bs,128]
    int64_t block_size,
    std::optional<torch::stable::Tensor> q_out,  // [N, nq*128] contiguous
    std::optional<torch::stable::Tensor>
        index_q_out,  // [N, niq*128] contiguous
-    const std::string& kv_cache_dtype, bool skip_index_branch) {
+    const std::string& kv_cache_dtype) {
  STD_TORCH_CHECK(qkv.is_cuda() && qkv.is_contiguous(),
                  "qkv must be contiguous CUDA");
  STD_TORCH_CHECK(
@@ -688,7 +647,6 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
  // (1 head)]) right after [q|k|v] in the same row; the dense layer does not.
  bool const has_index = niq > 0;
  bool const insert_kv = kv_cache.has_value();
-  bool const process_index = has_index && !skip_index_branch;
  vllm::Fp8KVCacheDataType const kv_dt =
      vllm::get_fp8_kv_cache_data_type(kv_cache_dtype);
  int const kHeadDim = vllm::minimax_m3_fused_ops::kHeadDim;
@@ -704,9 +662,7 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
  STD_TORCH_CHECK(
      !insert_kv || has_index,
      "insert mode (kv_cache) requires the index branch (sparse layer)");
-  STD_TORCH_CHECK(has_index || !skip_index_branch,
-                  "skip_index_branch requires sparse qkv rows");
-  if (process_index) {
+  if (has_index) {
    STD_TORCH_CHECK(
        index_q_norm_weight.has_value() && index_k_norm_weight.has_value(),
        "index branch requires both index norm weights");
@@ -717,26 +673,24 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
                        index_k_norm_weight->numel() == kHeadDim,
                    "index norm weights must have 128 elements");
  }
-  // kv_cache strides (logical shape [nb, nkv, bs, 2*head_dim]). Read straight
+  // kv_cache strides (logical shape [nb, 2, bs, nkv, head_dim]). Read straight
  // off the tensor so the kernel honours whatever physical layout the attention
-  // backend allocated (NHD: stride order (0,2,1,3); HND: (0,1,2,3)). No new
+  // backend allocated (NHD: stride order (0,1,2,3,4); HND: (0,1,3,2,4)). No new
  // op argument is needed -- the strides ride along with the tensor itself.
-  int64_t kv_s_block = 0, kv_s_head = 0, kv_s_token = 0, kv_s_dim = 0;
+  int64_t kv_s_block = 0, kv_s_kv = 0, kv_s_token = 0, kv_s_head = 0;
  torch::stable::Tensor const* effective_index_slot_mapping = nullptr;
  if (insert_kv) {
    STD_TORCH_CHECK(
        slot_mapping.has_value() && slot_mapping->is_cuda() &&
            slot_mapping->scalar_type() == torch::headeronly::ScalarType::Long,
        "insert mode requires int64 CUDA slot_mapping");
-    if (process_index) {
-      STD_TORCH_CHECK(
-          !index_slot_mapping.has_value() ||
-              (index_slot_mapping->is_cuda() &&
-               index_slot_mapping->scalar_type() ==
-                   torch::headeronly::ScalarType::Long &&
-               index_slot_mapping->numel() == slot_mapping->numel()),
-          "index_slot_mapping must be int64 CUDA with slot_mapping length");
-    }
+    STD_TORCH_CHECK(
+        !index_slot_mapping.has_value() ||
+            (index_slot_mapping->is_cuda() &&
+             index_slot_mapping->scalar_type() ==
+                 torch::headeronly::ScalarType::Long &&
+             index_slot_mapping->numel() == slot_mapping->numel()),
+        "index_slot_mapping must be int64 CUDA with slot_mapping length");
    // Main attention KV cache: auto matches qkv, fp8 uses uint8 storage.
    if (kv_dt == vllm::Fp8KVCacheDataType::kAuto) {
      STD_TORCH_CHECK(kv_cache->scalar_type() == qkv.scalar_type(),
@@ -747,26 +701,22 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
          "fp8 kv_cache must use uint8 storage");
    }
    // Indexer index-K cache: independent dtype -- qkv dtype or fp8 e4m3.
-    if (process_index) {
-      STD_TORCH_CHECK(
-          index_cache.has_value() &&
-              (index_cache->scalar_type() == qkv.scalar_type() ||
-               index_cache->scalar_type() ==
-                   torch::headeronly::ScalarType::Float8_e4m3fn),
-          "insert mode requires index_cache matching qkv dtype or fp8 e4m3");
-    }
-    STD_TORCH_CHECK(kv_cache->dim() == 4 && kv_cache->stride(3) == 1,
-                    "kv_cache must be [nb,nkv,bs,2*head_dim] with contiguous "
-                    "content dim (stride(3)==1)");
+    STD_TORCH_CHECK(
+        index_cache.has_value() &&
+            (index_cache->scalar_type() == qkv.scalar_type() ||
+             index_cache->scalar_type() ==
+                 torch::headeronly::ScalarType::Float8_e4m3fn),
+        "insert mode requires index_cache matching qkv dtype or fp8 e4m3");
+    STD_TORCH_CHECK(kv_cache->dim() == 5 && kv_cache->stride(4) == 1,
+                    "kv_cache must be [nb,2,bs,nkv,head_dim] with contiguous "
+                    "head_dim (stride(4)==1)");
    kv_s_block = kv_cache->stride(0);
-    kv_s_head = kv_cache->stride(1);
+    kv_s_kv = kv_cache->stride(1);
    kv_s_token = kv_cache->stride(2);
-    kv_s_dim = kv_cache->stride(3);
-    if (process_index) {
-      effective_index_slot_mapping = index_slot_mapping.has_value()
-                                         ? &index_slot_mapping.value()
-                                         : &slot_mapping.value();
-    }
+    kv_s_head = kv_cache->stride(3);
+    effective_index_slot_mapping = index_slot_mapping.has_value()
+                                       ? &index_slot_mapping.value()
+                                       : &slot_mapping.value();
  }
  // Optional contiguous gather targets: when given, the normed/roped q (and
  // index_q) are written here instead of in place, so callers avoid a separate
@@ -781,8 +731,9 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
        "q_out must have num_tokens * num_heads * 128 elements");
  }
  if (index_q_out.has_value()) {
-    STD_TORCH_CHECK(process_index,
-                    "index_q_out requires index branch processing");
+    STD_TORCH_CHECK(
+        has_index,
+        "index_q_out requires the index branch (num_index_heads > 0)");
    STD_TORCH_CHECK(
        index_q_out->is_cuda() && index_q_out->is_contiguous() &&
            (index_q_out->scalar_type() == qkv.scalar_type() ||
@@ -799,9 +750,8 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
  // q/k/v + q_out stay qkv dtype. Both index outputs must agree.
  auto const kFp8 = torch::headeronly::ScalarType::Float8_e4m3fn;
  bool const fp8_idx =
-      process_index &&
-      ((index_cache.has_value() && index_cache->scalar_type() == kFp8) ||
-       (index_q_out.has_value() && index_q_out->scalar_type() == kFp8));
+      (index_cache.has_value() && index_cache->scalar_type() == kFp8) ||
+      (index_q_out.has_value() && index_q_out->scalar_type() == kFp8);
  if (fp8_idx) {
    STD_TORCH_CHECK(
        !index_cache.has_value() || index_cache->scalar_type() == kFp8,
@@ -286,52 +286,3 @@ template void invokeRouterGemmBf16Output<__nv_bfloat16, 15, 384, 7168>(

 template void invokeRouterGemmBf16Output<__nv_bfloat16, 16, 384, 7168>(
    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-// Template instantiations for GLM-5 (DEFAULT_NUM_EXPERTS, hidden_dim=6144)
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 1, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 2, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 3, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 4, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 5, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 6, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 7, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 8, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 9, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 10, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 11, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 12, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 13, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 14, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 15, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 16, 256, 6144>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
@@ -41,7 +41,6 @@ inline int getSMVersion() {
 static constexpr int DEFAULT_NUM_EXPERTS = 256;
 static constexpr int KIMI_K2_NUM_EXPERTS = 384;
 static constexpr int DEFAULT_HIDDEN_DIM = 7168;
-static constexpr int GLM_5_HIDDEN_DIM = 6144;

 template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
 void invokeRouterGemmFloatOutput(float* output, T const* mat_a, T const* mat_b,
@@ -122,21 +121,14 @@ void dsv3_router_gemm(

  STD_TORCH_CHECK(mat_a.size(1) == mat_b.size(1),
                  "mat_a and mat_b must have the same hidden_dim");
-  STD_TORCH_CHECK(
-      hidden_dim == DEFAULT_HIDDEN_DIM || hidden_dim == GLM_5_HIDDEN_DIM,
-      "Expected hidden_dim=", DEFAULT_HIDDEN_DIM,
-      " or hidden_dim=", GLM_5_HIDDEN_DIM, ", but got hidden_dim=", hidden_dim);
+  STD_TORCH_CHECK(hidden_dim == DEFAULT_HIDDEN_DIM,
+                  "Expected hidden_dim=", DEFAULT_HIDDEN_DIM,
+                  ", but got hidden_dim=", hidden_dim);
  STD_TORCH_CHECK(
      num_experts == DEFAULT_NUM_EXPERTS || num_experts == KIMI_K2_NUM_EXPERTS,
      "Expected num_experts=", DEFAULT_NUM_EXPERTS,
      " or num_experts=", KIMI_K2_NUM_EXPERTS,
      ", but got num_experts=", num_experts);
-  // KIMI_K2_NUM_EXPERTS is only instantiated for the default hidden_dim.
-  STD_TORCH_CHECK(
-      hidden_dim == DEFAULT_HIDDEN_DIM || num_experts == DEFAULT_NUM_EXPERTS,
-      "hidden_dim=", GLM_5_HIDDEN_DIM,
-      " only supports num_experts=", DEFAULT_NUM_EXPERTS,
-      ", but got num_experts=", num_experts);
  STD_TORCH_CHECK(num_tokens >= 1 && num_tokens <= 16,
                  "currently num_tokens must be less than or equal to 16 for "
                  "router_gemm");
@@ -165,42 +157,30 @@ void dsv3_router_gemm(

  if (output.scalar_type() == torch::headeronly::ScalarType::Float) {
    float* out_ptr = reinterpret_cast<float*>(output.mutable_data_ptr());
-    if (hidden_dim == DEFAULT_HIDDEN_DIM) {
-      if (num_experts == DEFAULT_NUM_EXPERTS) {
-        LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS,
-                     DEFAULT_HIDDEN_DIM>::unroll_float_output(num_tokens,
-                                                              out_ptr, a_ptr,
-                                                              b_ptr, stream);
-      } else {
-        LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS,
-                     DEFAULT_HIDDEN_DIM>::unroll_float_output(num_tokens,
-                                                              out_ptr, a_ptr,
-                                                              b_ptr, stream);
-      }
-    } else {  // GLM_5_HIDDEN_DIM
+    if (num_experts == DEFAULT_NUM_EXPERTS) {
      LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS,
-                   GLM_5_HIDDEN_DIM>::unroll_float_output(num_tokens, out_ptr,
-                                                          a_ptr, b_ptr, stream);
+                   DEFAULT_HIDDEN_DIM>::unroll_float_output(num_tokens, out_ptr,
+                                                            a_ptr, b_ptr,
+                                                            stream);
+    } else {
+      LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS,
+                   DEFAULT_HIDDEN_DIM>::unroll_float_output(num_tokens, out_ptr,
+                                                            a_ptr, b_ptr,
+                                                            stream);
    }
  } else if (output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
    __nv_bfloat16* out_ptr =
        reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr());
-    if (hidden_dim == DEFAULT_HIDDEN_DIM) {
-      if (num_experts == DEFAULT_NUM_EXPERTS) {
-        LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS,
-                     DEFAULT_HIDDEN_DIM>::unroll_bf16_output(num_tokens,
-                                                             out_ptr, a_ptr,
-                                                             b_ptr, stream);
-      } else {
-        LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS,
-                     DEFAULT_HIDDEN_DIM>::unroll_bf16_output(num_tokens,
-                                                             out_ptr, a_ptr,
-                                                             b_ptr, stream);
-      }
-    } else {  // GLM_5_HIDDEN_DIM
+    if (num_experts == DEFAULT_NUM_EXPERTS) {
      LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS,
-                   GLM_5_HIDDEN_DIM>::unroll_bf16_output(num_tokens, out_ptr,
-                                                         a_ptr, b_ptr, stream);
+                   DEFAULT_HIDDEN_DIM>::unroll_bf16_output(num_tokens, out_ptr,
+                                                           a_ptr, b_ptr,
+                                                           stream);
+    } else {
+      LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS,
+                   DEFAULT_HIDDEN_DIM>::unroll_bf16_output(num_tokens, out_ptr,
+                                                           a_ptr, b_ptr,
+                                                           stream);
    }
  }
 }
@@ -286,52 +286,3 @@ template void invokeRouterGemmFloatOutput<__nv_bfloat16, 15, 384, 7168>(

 template void invokeRouterGemmFloatOutput<__nv_bfloat16, 16, 384, 7168>(
    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-// Template instantiations for GLM-5 (DEFAULT_NUM_EXPERTS, hidden_dim=6144)
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 1, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 2, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 3, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 4, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 5, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 6, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 7, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 8, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 9, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 10, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 11, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 12, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 13, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 14, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 15, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 16, 256, 6144>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
@@ -82,21 +82,6 @@ __global__ void batched_moe_align_block_size_kernel(
 }
 }  // namespace batched_moe_align_block_size

-template <typename scalar_t>
-__device__ __forceinline__ int get_local_expert_id(
-    size_t idx, const scalar_t* __restrict__ topk_ids,
-    int32_t* __restrict__ expert_map, int32_t num_experts,
-    bool has_expert_map) {
-  int expert_id = topk_ids[idx];
-  if (expert_id >= num_experts || expert_id < 0) {
-    return -1;
-  }
-  if (has_expert_map) {
-    expert_id = expert_map[expert_id];
-  }
-  return expert_id;
-}
-
 template <typename scalar_t>
 __device__ void _moe_align_block_size(
    const scalar_t* __restrict__ topk_ids,
@@ -141,15 +126,20 @@ __device__ void _moe_align_block_size(
  const size_t stride = blockDim.x;

  for (size_t i = tid; i < numel; i += stride) {
-    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
-                                            num_experts, has_expert_map);
-        expert_id != -1) {
-      int warp_idx = expert_id / experts_per_warp;
-      int expert_offset = expert_id % experts_per_warp;
-      int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
-      atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset],
-                mask);
+    int expert_id = topk_ids[i];
+    if (expert_id >= num_experts) {
+      continue;
    }
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid experts
+      if (expert_id == -1) continue;
+    }
+    int warp_idx = expert_id / experts_per_warp;
+    int expert_offset = expert_id % experts_per_warp;
+    int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
+    atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset],
+              mask);
  }

  __syncthreads();
@@ -237,12 +227,14 @@ __device__ void _moe_align_block_size_small_batch_expert(
  }

  for (size_t i = tid; i < numel; i += stride) {
-    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
-                                            num_experts, has_expert_map);
-        expert_id != -1) {
-      int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
-      tokens_cnts[(tid + 1) * num_experts + expert_id] += mask;
+    int32_t expert_id = topk_ids[i];
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid expert
+      if (expert_id == -1) continue;
    }
+    int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
+    tokens_cnts[(tid + 1) * num_experts + expert_id] += mask;
  }

  __syncthreads();
@@ -284,16 +276,18 @@ __device__ void _moe_align_block_size_small_batch_expert(
  }

  for (size_t i = tid; i < numel; i += stride) {
-    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
-                                            num_experts, has_expert_map);
-        expert_id != -1) {
-      int32_t rank_post_pad =
-          tokens_cnts[tid * num_experts + expert_id] + cumsum[expert_id];
+    int32_t expert_id = topk_ids[i];
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid expert
+      if (expert_id == -1) continue;
+    }
+    int32_t rank_post_pad =
+        tokens_cnts[tid * num_experts + expert_id] + cumsum[expert_id];

-      if (token_mask == nullptr || token_mask[i / topk_num]) {
-        sorted_token_ids[sorted_token_ids_offset + rank_post_pad] = i;
-        ++tokens_cnts[tid * num_experts + expert_id];
-      }
+    if (token_mask == nullptr || token_mask[i / topk_num]) {
+      sorted_token_ids[sorted_token_ids_offset + rank_post_pad] = i;
+      ++tokens_cnts[tid * num_experts + expert_id];
    }
  }
 }
@@ -309,15 +303,22 @@ __device__ void _count_and_sort_expert_tokens(
  const size_t stride = blockDim.x * gridDim.y;

  for (size_t i = tid; i < numel; i += stride) {
-    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
-                                            num_experts, has_expert_map);
-        expert_id != -1) {
-      if (token_mask == nullptr || token_mask[i / topk_num]) {
-        int32_t rank_post_pad = atomicAdd(
-            &cumsum_buffer[(model_offset * (num_experts + 1)) + expert_id], 1);
-        sorted_token_ids[max_num_tokens_padded * model_offset + rank_post_pad] =
-            i;
-      }
+    int32_t expert_id = topk_ids[i];
+    if (expert_id >= num_experts) {
+      continue;
+    }
+
+    if (has_expert_map) {
+      expert_id = expert_map[expert_id];
+      // filter invalid experts
+      if (expert_id == -1) continue;
+    }
+
+    if (token_mask == nullptr || token_mask[i / topk_num]) {
+      int32_t rank_post_pad = atomicAdd(
+          &cumsum_buffer[(model_offset * (num_experts + 1)) + expert_id], 1);
+      sorted_token_ids[max_num_tokens_padded * model_offset + rank_post_pad] =
+          i;
    }
  }
 }
@@ -15,8 +15,7 @@ void topk_sigmoid(torch::stable::Tensor& topk_weights,
                  torch::stable::Tensor& topk_indices,
                  torch::stable::Tensor& token_expert_indices,
                  torch::stable::Tensor& gating_output, bool renormalize,
-                  std::optional<torch::stable::Tensor> bias,
-                  double routed_scaling_factor);
+                  std::optional<torch::stable::Tensor> bias);

 void topk_softplus_sqrt(
    torch::stable::Tensor& topk_weights, torch::stable::Tensor& topk_indices,
@@ -173,8 +173,7 @@ __launch_bounds__(TPB) __global__ void moeTopK(
    const int start_expert,
    const int end_expert,
    const bool renormalize,
-    const float* bias,
-    const double routed_scaling_factor)
+    const float* bias)
 {

    using cub_kvp = cub::KeyValuePair<int, float>;
@@ -242,16 +241,14 @@ __launch_bounds__(TPB) __global__ void moeTopK(
        __syncthreads();
    }

-    // Apply renormalization and routed scaling factor to final weights.
-    if (threadIdx.x == 0) {
-        float scale = static_cast<float>(routed_scaling_factor);
-        if (renormalize) {
+    // Renormalize the k weights for this row to sum to 1, if requested.
+    if (renormalize) {
+        if (threadIdx.x == 0) {
            const float denom = selected_sum > 0.f ? selected_sum : 1.f;
-            scale /= denom;
-        }
-        for (int k_idx = 0; k_idx < k; ++k_idx) {
-            const int idx = k * block_row + k_idx;
-            output[idx] = output[idx] * scale;
+            for (int k_idx = 0; k_idx < k; ++k_idx) {
+                const int idx = k * block_row + k_idx;
+                output[idx] = output[idx] / denom;
+            }
        }
    }
 }
@@ -277,7 +274,7 @@ template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WA
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
    void topkGating(const InputType* input, const bool* finished, float* output, const int num_rows, IndType* indices,
        int* source_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-        const float* bias, const double routed_scaling_factor)
+        const float* bias)
 {
    static_assert(std::is_same_v<InputType, float> || std::is_same_v<InputType, __nv_bfloat16> ||
                      std::is_same_v<InputType, __half>,
@@ -573,17 +570,17 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
        }
    }

-    // Apply renormalization and routed scaling factor to final weights.
-    if (thread_group_idx == 0) {
-      float scale = static_cast<float>(routed_scaling_factor);
-      if (renormalize) {
-        const float denom = selected_sum > 0.f ? selected_sum : 1.f;
-        scale /= denom;
-      }
-      for (int k_idx = 0; k_idx < k; ++k_idx) {
-        const int idx = k * thread_row + k_idx;
-        output[idx] = output[idx] * scale;
-      }
+    // Renormalize the k weights for this row to sum to 1, if requested.
+    if (renormalize) {
+        if (thread_group_idx == 0)
+        {
+            const float denom = selected_sum > 0.f ? selected_sum : 1.f;
+            for (int k_idx = 0; k_idx < k; ++k_idx)
+            {
+                const int idx = k * thread_row + k_idx;
+                output[idx] = output[idx] / denom;
+            }
+        }
    }
 }

@@ -605,7 +602,7 @@ struct TopkConstants
 template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType, typename InputType, ScoringFunc SF>
 void topkGatingLauncherHelper(const InputType* input, const bool* finished, float* output, IndType* indices,
    int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-    const float* bias, const double routed_scaling_factor, cudaStream_t stream)
+    const float* bias, cudaStream_t stream)
 {
    static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM, InputType>;
@@ -616,7 +613,7 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa

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

 #ifndef USE_ROCM
@@ -627,7 +624,7 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
                             IndType, InputType, SF>(                         \
        gating_output, nullptr, topk_weights, topk_indices,                   \
        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,  \
-        bias, routed_scaling_factor, stream);
+        bias, stream);
 #else
  #define LAUNCH_TOPK(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                    \
    if (WARP_SIZE == 64) {                                                     \
@@ -635,13 +632,13 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
                               IndType, InputType, SF>(                        \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, routed_scaling_factor, stream);                                                       \
+          bias, stream);                                                       \
    } else if (WARP_SIZE == 32) {                                              \
      topkGatingLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES,       \
                               IndType, InputType, SF>(                        \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, routed_scaling_factor, stream);                                                       \
+          bias, stream);                                                       \
    } else {                                                                   \
      assert(false &&                                                          \
             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
@@ -660,7 +657,6 @@ void topkGatingKernelLauncher(
    const int topk,
    const bool renormalize,
    const float* bias,
-    const double routed_scaling_factor,
    cudaStream_t stream) {
    static constexpr int WARPS_PER_TB = 4;
    static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
@@ -736,7 +732,7 @@ void topkGatingKernelLauncher(
            }
            moeTopK<TPB><<<num_tokens, TPB, 0, stream>>>(
                workspace, nullptr, topk_weights, topk_indices, token_expert_indices,
-                num_experts, topk, 0, num_experts, renormalize, bias, routed_scaling_factor);
+                num_experts, topk, 0, num_experts, renormalize, bias);
        }
    }
 }
@@ -754,7 +750,6 @@ void dispatch_topk_launch(
    torch::stable::Tensor& softmax_workspace,
    int num_tokens, int num_experts, int topk, bool renormalize,
    std::optional<torch::stable::Tensor> bias,
-    double routed_scaling_factor,
    cudaStream_t stream)
 {
    const float* bias_ptr = nullptr;
@@ -777,7 +772,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, stream);
    } else if (topk_indices.scalar_type() == torch::headeronly::ScalarType::UInt32) {
        vllm::moe::topkGatingKernelLauncher<uint32_t, ComputeType, SF>(
            reinterpret_cast<const ComputeType*>(gating_output.const_data_ptr()),
@@ -786,7 +781,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, stream);
    } else {
        STD_TORCH_CHECK(topk_indices.scalar_type() == torch::headeronly::ScalarType::Long);
        vllm::moe::topkGatingKernelLauncher<int64_t, ComputeType, SF>(
@@ -796,7 +791,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, stream);
    }
 }

@@ -825,15 +820,15 @@ void topk_softmax(
    if (gating_output.scalar_type() == torch::headeronly::ScalarType::Float) {
        dispatch_topk_launch<float, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, stream);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::Half) {
        dispatch_topk_launch<__half, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, stream);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, stream);
    } else {
        STD_TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
    }
@@ -845,8 +840,7 @@ void topk_sigmoid(
    torch::stable::Tensor& token_expert_indices,        // [num_tokens, topk]
    torch::stable::Tensor& gating_output,               // [num_tokens, num_experts]
    bool renormalize,
-    std::optional<torch::stable::Tensor> bias,
-    double routed_scaling_factor)
+    std::optional<torch::stable::Tensor> bias)
 {
    const int num_experts = gating_output.size(-1);
    const auto num_tokens = gating_output.numel() / num_experts;
@@ -865,15 +859,15 @@ void topk_sigmoid(
    if (gating_output.scalar_type() == torch::headeronly::ScalarType::Float) {
        dispatch_topk_launch<float, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, stream);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::Half) {
        dispatch_topk_launch<__half, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, stream);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, stream);
    } else {
        STD_TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
    }
@@ -13,8 +13,8 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_moe_C, m) {
  // Apply topk sigmoid to the gating outputs.
  m.def(
      "topk_sigmoid(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
-      "token_expert_indices, Tensor gating_output, bool renormalize, "
-      "Tensor? bias, float routed_scaling_factor) -> ()");
+      "token_expert_indices, Tensor gating_output, bool renormalize, Tensor? "
+      "bias) -> ()");

  m.def(
      "topk_softplus_sqrt(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
@@ -1,96 +0,0 @@
-// N-gram embedding index kernel for LongCat-Flash (n-gram embedding variant).
-//
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/main/python/sglang/jit_kernel/csrc/ngram_embedding.cuh
-//
-// For each position, computes the hashed n-gram embedding ids that index the
-// concatenated embedder table. Integer tensors are int32 except ``row_indices``
-// (int64); the token table is ``[max_running_reqs, max_context_len]`` int32,
-// where a negative entry marks an ignored token (e.g. an EOS boundary).
-
-#include "torch_utils.h"
-
-#include "ops.h"
-
-#include <cstdint>
-
-namespace vllm::ngram_embedding {
-
-constexpr int kBlockThreads = 256;
-
-__global__ void ComputeNGramIdsKernel(
-    int batch_size, int ne_n, int ne_k,
-    int* ne_weights,                       // [ne_n-1, ne_k, ne_n]
-    int* ne_mods,                          // [ne_n-1, ne_k]
-    int* exclusive_ne_embedder_size_sums,  // [(ne_n-1)*ne_k + 1]
-    int* exclusive_req_len_sums,           // [batch_size + 1]
-    int* ne_token_table,  // [max_running_reqs, max_context_len]
-    int max_context_len,
-    const int64_t* __restrict__ row_indices,  // [batch_size]
-    int* column_starts,                       // [batch_size]
-    int* n_gram_ids                           // [token_num, (ne_n-1)*ne_k]
-) {
-  const int req_id = blockIdx.x % batch_size;
-  const int config_id = (blockIdx.x - req_id) / batch_size;
-  // n and k are offset from their physical meaning: n = real_n - 2, k = real_k
-  // - 1 (they index into ne_weights / ne_mods).
-  const int k = config_id % ne_k;
-  const int n = (config_id - config_id % ne_k) / ne_k;
-  const int ne_weight_base_idx = n * ne_k * ne_n + k * ne_n;
-  const int ne_mod = ne_mods[n * ne_k + k];
-  for (int i = exclusive_req_len_sums[req_id] + threadIdx.x;
-       i < exclusive_req_len_sums[req_id + 1]; i += blockDim.x) {
-    uint64_t n_gram_id = 0;
-    const int64_t current_token_offset = i - exclusive_req_len_sums[req_id];
-    const int64_t req_token_table_index =
-        row_indices[req_id] * static_cast<int64_t>(max_context_len);
-    const int64_t current_token_table_index =
-        req_token_table_index + column_starts[req_id] + current_token_offset;
-    for (int j = 0; j < n + 2; j++) {
-      if (current_token_table_index - j < req_token_table_index) {
-        break;  // outside this request's range
-      }
-      if (ne_token_table[current_token_table_index - j] < 0) {
-        break;  // ignored token
-      }
-      const uint64_t term =
-          (uint64_t)ne_token_table[current_token_table_index - j] *
-          (uint64_t)ne_weights[ne_weight_base_idx + j];
-      n_gram_id += term % ne_mod;
-    }
-    n_gram_id %= ne_mod;
-    n_gram_id += exclusive_ne_embedder_size_sums[n * ne_k + k];
-    n_gram_ids[i * (ne_n - 1) * ne_k + n * ne_k + k] = (int)(n_gram_id);
-  }
-}
-
-}  // namespace vllm::ngram_embedding
-
-void ngram_compute_n_gram_ids(
-    int64_t ne_n, int64_t ne_k, torch::stable::Tensor& ne_weights,
-    torch::stable::Tensor& ne_mods,
-    torch::stable::Tensor& exclusive_ne_embedder_size_sums,
-    torch::stable::Tensor& exclusive_req_len_sums,
-    torch::stable::Tensor& ne_token_table, torch::stable::Tensor& row_indices,
-    torch::stable::Tensor& column_starts, torch::stable::Tensor& n_gram_ids) {
-  const int batch_size = static_cast<int>(exclusive_req_len_sums.size(0) - 1);
-  const int max_context_len = static_cast<int>(ne_token_table.size(1));
-  const int num_configs = (static_cast<int>(ne_n) - 1) * static_cast<int>(ne_k);
-  const int grid_size = num_configs * batch_size;
-  if (grid_size <= 0) return;
-
-  const torch::stable::accelerator::DeviceGuard device_guard(
-      ne_weights.get_device_index());
-  const cudaStream_t stream = get_current_cuda_stream();
-  vllm::ngram_embedding::ComputeNGramIdsKernel<<<
-      grid_size, vllm::ngram_embedding::kBlockThreads, 0, stream>>>(
-      batch_size, static_cast<int>(ne_n), static_cast<int>(ne_k),
-      ne_weights.mutable_data_ptr<int32_t>(),
-      ne_mods.mutable_data_ptr<int32_t>(),
-      exclusive_ne_embedder_size_sums.mutable_data_ptr<int32_t>(),
-      exclusive_req_len_sums.mutable_data_ptr<int32_t>(),
-      ne_token_table.mutable_data_ptr<int32_t>(), max_context_len,
-      row_indices.const_data_ptr<int64_t>(),
-      column_starts.mutable_data_ptr<int32_t>(),
-      n_gram_ids.mutable_data_ptr<int32_t>());
-}
@@ -313,7 +313,7 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
    std::optional<torch::stable::Tensor> index_cache, int64_t block_size,
    std::optional<torch::stable::Tensor> q_out,
    std::optional<torch::stable::Tensor> index_q_out,
-    const std::string& kv_cache_dtype, bool skip_index_branch);
+    const std::string& kv_cache_dtype);

 // Sampler kernels (shared CUDA/ROCm)
 void apply_repetition_penalties_(
@@ -414,8 +414,6 @@ void gelu_new(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void gelu_fast(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void gelu_quick(torch::stable::Tensor& out, torch::stable::Tensor& input);

-void relu_squared(torch::stable::Tensor& out, torch::stable::Tensor& input);
-
 // INT8 quantization kernels (shared CUDA/ROCm)
 void static_scaled_int8_quant(torch::stable::Tensor& out,
                              torch::stable::Tensor const& input,
@@ -556,12 +554,3 @@ void cp_gather_indexer_k_quant_cache(
                                                // quant_block_size * 4]
    const torch::stable::Tensor& block_table,   // [batch_size, num_blocks]
    const torch::stable::Tensor& cu_seq_lens);  // [batch_size + 1]
-
-// LongCat n-gram embedding index kernel (see ngram_embedding_kernels.cu).
-void ngram_compute_n_gram_ids(
-    int64_t ne_n, int64_t ne_k, torch::stable::Tensor& ne_weights,
-    torch::stable::Tensor& ne_mods,
-    torch::stable::Tensor& exclusive_ne_embedder_size_sums,
-    torch::stable::Tensor& exclusive_req_len_sums,
-    torch::stable::Tensor& ne_token_table, torch::stable::Tensor& row_indices,
-    torch::stable::Tensor& column_starts, torch::stable::Tensor& n_gram_ids);
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Jee Jee Li	f67c1ad263	FIX Signed-off-by: Jee Jee Li <jeejeelee@inferact.ai>	2026-07-04 13:45:35 +00:00
Jee Jee Li	46ef0dfe19	Merge remote-tracking branch 'origin/main' into glm5--fp32-router	2026-07-03 15:40:18 +00:00
Jee Jee Li	8c6f498c22	Add bench config Signed-off-by: Jee Jee Li <jeejeelee@inferact.ai>	2026-07-03 11:25:06 +00:00
Jee Jee Li	f03d7b96f4	Init Signed-off-by: Jee Jee Li <jeejeelee@inferact.ai>	2026-07-03 10:27:36 +00:00