Patch protobuf for CVE-2026-0994 (#34253 )

Signed-off-by: Seiji Eicher <seiji@anyscale.com> Co-authored-by: Kevin H. Luu <khluu000@gmail.com> (cherry picked from commit 5045d5c983)
[XPU][9/N] clean up existing ipex code/doc (#34111 )
2026-02-11 02:33:40 -08:00 · 2026-02-11 02:33:27 -08:00 · 2026-02-11 02:33:15 -08:00 · 2026-02-11 02:33:04 -08:00 · 2026-02-11 02:32:52 -08:00 · 2026-02-11 02:32:41 -08:00
4358 changed files with 156573 additions and 743321 deletions
@@ -8,9 +8,8 @@ run_all_patterns:
  - "CMakeLists.txt"
  - "requirements/common.txt"
  - "requirements/cuda.txt"
-  - "requirements/kv_connectors.txt"
-  - "requirements/build/cuda.txt"
-  - "requirements/test/cuda.txt"
+  - "requirements/build.txt"
+  - "requirements/test.txt"
  - "setup.py"
  - "csrc/"
  - "cmake/"
@@ -1,23 +0,0 @@
-name: vllm_intel_ci
-job_dirs:
-  - ".buildkite/intel_jobs"
-run_all_patterns:
-  - "docker/Dockerfile"
-  - "CMakeLists.txt"
-  - "requirements/common.txt"
-  - "requirements/xpu.txt"
-  - "requirements/build/cuda.txt"
-  - "requirements/test/cuda.txt"
-  - "setup.py"
-  - "csrc/"
-  - "cmake/"
-run_all_exclude_patterns:
-  - "docker/Dockerfile."
-  - "csrc/cpu/"
-  - "csrc/rocm/"
-  - "cmake/hipify.py"
-  - "cmake/cpu_extension.cmake"
-registries: public.ecr.aws/q9t5s3a7
-repositories:
-  main: "vllm-ci-test-repo"
-  premerge: "vllm-ci-test-repo"
@@ -1,23 +0,0 @@
-name: vllm_rocm_ci
-job_dirs:
-  - ".buildkite/hardware_tests"
-run_all_patterns:
-  - "docker/Dockerfile.rocm"
-  - "docker/Dockerfile.rocm_base"
-  - "docker/ci-rocm.hcl"
-  - "docker/docker-bake-rocm.hcl"
-  - ".buildkite/hardware_tests/amd.yaml"
-  - ".buildkite/scripts/ci-bake-rocm.sh"
-  - ".buildkite/scripts/hardware_ci/run-amd-test.py"
-  - ".buildkite/scripts/hardware_ci/run-amd-test.sh"
-  - "CMakeLists.txt"
-  - "requirements/common.txt"
-  - "requirements/rocm.txt"
-  - "requirements/build/rocm.txt"
-  - "requirements/test/rocm.txt"
-  - "setup.py"
-  - "csrc/"
-  - "cmake/"
-run_all_exclude_patterns:
-  - "csrc/cpu/"
-  - "cmake/cpu_extension.cmake"
@@ -1,73 +1,29 @@
-group: Hardware - AMD Build
+group: Hardware
 steps:
-  # 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
-  # differ ci_base is rebuilt and pushed automatically.
-  - label: "AMD: :docker: ensure ci_base"
-    key: ensure-ci-base-amd
+  - label: "AMD: :docker: build image"
    depends_on: []
    device: amd_cpu
    no_plugin: true
    commands:
-      - bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
+    - >
+      docker build
+      --build-arg max_jobs=16
+      --build-arg REMOTE_VLLM=1
+      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942'
+      --build-arg VLLM_BRANCH=$BUILDKITE_COMMIT
+      --tag "rocm/vllm-ci:${BUILDKITE_COMMIT}"
+      -f docker/Dockerfile.rocm
+      --target test
+      --no-cache
+      --progress plain .
+    - docker push "rocm/vllm-ci:${BUILDKITE_COMMIT}"
    env:
      DOCKER_BUILDKIT: "1"
-      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
-      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
-      REMOTE_VLLM: "1"
-      VLLM_BRANCH: "$BUILDKITE_COMMIT"
    retry:
      automatic:
        - exit_status: -1  # Agent was lost
          limit: 1
        - exit_status: -10  # Agent was lost
          limit: 1
-
-  - label: "AMD: :docker: build test image and artifacts"
-    key: image-build-amd
-    depends_on:
-      - ensure-ci-base-amd
-    device: amd_cpu
-    no_plugin: true
-    commands:
-      - |
-        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"
-      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
-      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
-      IMAGE_TAG: "rocm/vllm-ci:$BUILDKITE_COMMIT"
-      REMOTE_VLLM: "1"
-      VLLM_BRANCH: "$BUILDKITE_COMMIT"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 1
-        - exit_status: -10  # Agent was lost
+        - exit_status: 1  # Machine occasionally fail
          limit: 1
@@ -3,6 +3,7 @@ depends_on: []
 steps:
 - label: CPU-Kernel Tests
  depends_on: []
+  soft_fail: true
  device: intel_cpu
  no_plugin: true
  source_file_dependencies:
@@ -12,38 +13,17 @@ steps:
  - vllm/_custom_ops.py
  - tests/kernels/attention/test_cpu_attn.py
  - tests/kernels/moe/test_cpu_fused_moe.py
-  - tests/kernels/moe/test_cpu_quant_fused_moe.py
  - tests/kernels/test_onednn.py
-  - 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
  commands:
    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
      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/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
-      pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"
-
-# Note: SDE can't be downloaded from CI host because of AWS WAF
-# - label: CPU-Compatibility Tests
-#   depends_on: []
-#   device: intel_cpu
-#   no_plugin: true
-#   source_file_dependencies:
-#   - cmake/cpu_extension.cmake
-#   - setup.py
-#   - vllm/platforms/cpu.py
-#   commands:
-#     - |
-#       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
-#       bash .buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh"
+      pytest -x -v -s tests/kernels/test_onednn.py"

 - label: CPU-Language Generation and Pooling Model Tests
  depends_on: []
+  soft_fail: true
  device: intel_cpu
  no_plugin: true
  source_file_dependencies:
@@ -53,53 +33,36 @@ steps:
  - tests/models/language/pooling/
  commands:
    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
      pytest -x -v -s tests/models/language/generation -m cpu_model
      pytest -x -v -s tests/models/language/pooling -m cpu_model"

- label: CPU-ModelRunnerV2 Tests
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  soft_fail: true
-  source_file_dependencies:
-  - vllm/v1/worker/cpu/
-  - vllm/v1/worker/gpu/
-  - vllm/v1/sample/ops/topk_topp_triton.py
-  - vllm/v1/sample/ops/topk_topp_sampler.py
-  - tests/v1/sample/test_topk_topp_sampler.py
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
-      uv pip install git+https://github.com/triton-lang/triton-cpu.git@270e696d
-      VLLM_USE_V2_MODEL_RUNNER=1 pytest -x -v -s tests/models/language/generation/test_granite.py -m cpu_model
-      # TODO: move to CPU-Kernel Tests once triton-cpu has a pre-built wheel
-      pytest -x -v -s tests/v1/sample/test_topk_topp_sampler.py::TestTritonTopkTopp"
-
 - label: CPU-Quantization Model Tests
  depends_on: []
+  soft_fail: true
  device: intel_cpu
  no_plugin: true
  source_file_dependencies:
  - csrc/cpu/
-  - vllm/model_executor/layers/quantization/auto_gptq.py
+  - vllm/model_executor/layers/quantization/cpu_wna16.py
+  - vllm/model_executor/layers/quantization/gptq_marlin.py
  - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
-  - vllm/model_executor/kernels/linear/mixed_precision/cpu.py
-  - vllm/model_executor/kernels/linear/scaled_mm/cpu.py
-  - vllm/model_executor/layers/fused_moe/experts/cpu_moe.py
+  - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
+  - vllm/model_executor/layers/quantization/kernels/mixed_precision/cpu.py
  - tests/quantization/test_compressed_tensors.py
  - tests/quantization/test_cpu_wna16.py
  commands:
    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
      pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
      pytest -x -v -s tests/quantization/test_cpu_wna16.py"
      
- label: CPU-Distributed Tests (PP+TP)
+- label: CPU-Distributed Tests
  depends_on: []
+  soft_fail: true
  device: intel_cpu
  no_plugin: true
-  source_file_dependencies: &cpu_distributed_deps
+  source_file_dependencies:
  - csrc/cpu/shm.cpp
  - vllm/v1/worker/cpu_worker.py
  - vllm/v1/worker/gpu_worker.py
@@ -108,24 +71,14 @@ steps:
  - vllm/platforms/cpu.py
  - vllm/distributed/parallel_state.py
  - vllm/distributed/device_communicators/cpu_communicator.py
-  - .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
  commands:
    - |
      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh tp_pp"
-
- label: CPU-Distributed Tests (DP+TP)
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies: *cpu_distributed_deps
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh dp_tp"
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"

 - label: CPU-Multi-Modal Model Tests %N
  depends_on: []
+  soft_fail: true
  device: intel_cpu
  no_plugin: true
  source_file_dependencies:
@@ -136,11 +89,11 @@ steps:
    - |
      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
      pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
-  parallelism: 3
+  parallelism: 2

 - label: "Arm CPU Test"
  depends_on: []
-  soft_fail: false
+  soft_fail: true
  device: arm_cpu
  no_plugin: true
  commands: 
@@ -8,3 +8,10 @@ steps:
    commands: 
    - bash .buildkite/scripts/hardware_ci/run-hpu-test.sh

+  - label: "Intel GPU Test"
+    depends_on: []
+    soft_fail: true
+    device: intel_gpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-xpu-test.sh
@@ -1,80 +0,0 @@
-group: Intel
-steps:
-  - label: ":docker: Build XPU image"
-    soft_fail: true
-    optional: true
-    depends_on: []
-    key: image-build-xpu
-    commands:
-      - bash -lc '.buildkite/image_build/image_build_xpu.sh "public.ecr.aws/q9t5s3a7" "vllm-ci-test-repo" "$BUILDKITE_COMMIT"'
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-  - label: "XPU example Test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    optional: true
-    device: intel_gpu
-    agent_tags:
-      label: production
-      gpu: 2+
-      mem: 24+
-    no_plugin: true
-    env:
-      REGISTRY: "public.ecr.aws/q9t5s3a7"
-      REPO: "vllm-ci-test-repo"
-    source_file_dependencies:
-      - .buildkite/hardware_tests/intel_xpu_ci/test-intel.yaml
-      - .buildkite/scripts/hardware_ci/run-intel-ci-test.sh
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'bash .buildkite/scripts/hardware_ci/run-intel-ci-test.sh example'
-  - label: "XPU V1 test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    optional: true
-    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"
-    source_file_dependencies:
-      - .buildkite/hardware_tests/intel_xpu_ci/test-intel.yaml
-      - .buildkite/scripts/hardware_ci/run-intel-ci-test.sh
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'bash .buildkite/scripts/hardware_ci/run-intel-ci-test.sh v1'
-  - label: "XPU server test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    optional: true
-    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"
-    source_file_dependencies:
-      - .buildkite/hardware_tests/intel_xpu_ci/test-intel.yaml
-      - .buildkite/scripts/hardware_ci/run-intel-ci-test.sh
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'bash .buildkite/scripts/hardware_ci/run-intel-ci-test.sh server'
@@ -8,7 +8,7 @@ clean_docker_tag() {
 }

 print_usage_and_exit() {
-    echo "Usage: $0 <registry> <repo> <commit> <branch> <image_tag> [<image_tag_latest>]"
+    echo "Usage: $0 <registry> <repo> <commit> <branch> <vllm_use_precompiled> <vllm_merge_base_commit> <cache_from> <cache_to>"
    exit 1
 }

@@ -92,8 +92,8 @@ check_and_skip_if_image_exists() {
 }

 ecr_login() {
-    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
-    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
+    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
 }

 prepare_cache_tags() {
@@ -142,16 +142,11 @@ resolve_parent_commit() {

 print_bake_config() {
    echo "--- :page_facing_up: Resolved bake configuration"
-    # Write to a temp directory to avoid polluting the repo root (which is the
-    # Docker build context). Files left in the repo root get COPY'd into the
-    # image and can cause duplicate artifact uploads from downstream steps.
-    local bake_tmp
-    bake_tmp="$(mktemp -d)"
-    BAKE_CONFIG_FILE="${bake_tmp}/bake-config-build-${BUILDKITE_BUILD_NUMBER:-local}.json"
+    BAKE_CONFIG_FILE="bake-config-build-${BUILDKITE_BUILD_NUMBER:-local}.json"
    docker buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --print "${TARGET}" | tee "${BAKE_CONFIG_FILE}" || true
    echo "Saved bake config to ${BAKE_CONFIG_FILE}"
    echo "--- :arrow_down: Uploading bake config to Buildkite"
-    (cd "$(dirname "${BAKE_CONFIG_FILE}")" && buildkite-agent artifact upload "$(basename "${BAKE_CONFIG_FILE}")")
+    buildkite-agent artifact upload "${BAKE_CONFIG_FILE}"
 }

 #################################
@@ -159,7 +154,7 @@ print_bake_config() {
 #################################
 print_instance_info

-if [[ $# -lt 5 ]]; then
+if [[ $# -lt 7 ]]; then
    print_usage_and_exit
 fi

@@ -168,8 +163,10 @@ REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
 BRANCH=$4
-IMAGE_TAG=$5
-IMAGE_TAG_LATEST=${6:-} # only used for main branch, optional
+VLLM_USE_PRECOMPILED=$5
+VLLM_MERGE_BASE_COMMIT=$6
+IMAGE_TAG=$7
+IMAGE_TAG_LATEST=${8:-} # only used for main branch, optional

 # build config
 TARGET="test-ci"
@@ -192,11 +189,12 @@ export BUILDKITE_COMMIT
 export PARENT_COMMIT
 export IMAGE_TAG
 export IMAGE_TAG_LATEST
-export COMMIT="${COMMIT:-${BUILDKITE_COMMIT}}"
 export CACHE_FROM
 export CACHE_FROM_BASE_BRANCH
 export CACHE_FROM_MAIN
 export CACHE_TO
+export VLLM_USE_PRECOMPILED
+export VLLM_MERGE_BASE_COMMIT

 # print args
 echo "--- :mag: Arguments"
@@ -204,6 +202,8 @@ echo "REGISTRY: ${REGISTRY}"
 echo "REPO: ${REPO}"
 echo "BUILDKITE_COMMIT: ${BUILDKITE_COMMIT}"
 echo "BRANCH: ${BRANCH}"
+echo "VLLM_USE_PRECOMPILED: ${VLLM_USE_PRECOMPILED}"
+echo "VLLM_MERGE_BASE_COMMIT: ${VLLM_MERGE_BASE_COMMIT}"
 echo "IMAGE_TAG: ${IMAGE_TAG}"
 echo "IMAGE_TAG_LATEST: ${IMAGE_TAG_LATEST}"

@@ -5,61 +5,8 @@ steps:
    depends_on: []
    timeout_in_minutes: 600
    commands:
-    - if [[ "$BUILDKITE_BRANCH" == "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $IMAGE_TAG $IMAGE_TAG_LATEST; else .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $IMAGE_TAG; fi
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-
-  - label: ":docker: :smoking: Non-root smoke tests"
-    key: image-build-smoke-test
-    depends_on:
-      - image-build
-    commands:
-    # Smoke 1: the default (root) image must still be importable
-    # under a non-root UID via `--user 2000:0`. Validates the `vllm` passwd
-    # entry + group-0-writable /home/vllm + uv path cleanup from #31959.
-    # Uses `import vllm` rather than `vllm serve --help` because the latter
-    # instantiates `VllmConfig` which requires a GPU attached to the
-    # container.
-    - docker run --rm --user 2000:0 --entrypoint python3 "$IMAGE_TAG" -c "import vllm; print(vllm.__version__)"
-    # Smoke 2: assert the non-root enabling invariants are baked
-    # into the image. Runs as UID 2000:0 via a shell so we can verify
-    # filesystem perms + passwd/group file state + wrapper presence without
-    # triggering vLLM's GPU-requiring config-init path. The opt-in
-    # `vllm-openai-nonroot` target adds only `USER vllm`, `WORKDIR
-    # /home/vllm`, and an `ENTRYPOINT` override on top of these invariants;
-    # its build correctness is reviewed at the Dockerfile level. Wrapper
-    # logic is covered separately by the pre-commit hook
-    # `test-nonroot-entrypoint` (see .pre-commit-config.yaml).
-    - |
-      docker run --rm --user 2000:0 --entrypoint /bin/sh "$IMAGE_TAG" -ec '
-        if ! getent passwd 2000 | grep -q ^vllm:; then
-          echo FAIL: UID 2000 != vllm
-          exit 1
-        fi
-        if ! id -gn 2>/dev/null | grep -qx root; then
-          echo FAIL: GID 0 not root group
-          exit 1
-        fi
-        touch /home/vllm/.smoke && rm /home/vllm/.smoke
-        touch /opt/uv/cache/.smoke && rm /opt/uv/cache/.smoke
-        if ! test -x /usr/local/bin/vllm-nonroot-entrypoint.sh; then
-          echo FAIL: wrapper missing
-          exit 1
-        fi
-        if ! test -w /etc/passwd; then
-          echo FAIL: /etc/passwd not group-writable
-          exit 1
-        fi
-        if ! test -w /etc/group; then
-          echo FAIL: /etc/group not group-writable
-          exit 1
-        fi
-        echo non-root invariants OK
-      '
+    - if [[ "$BUILDKITE_BRANCH" != "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $IMAGE_TAG; fi
+    - if [[ "$BUILDKITE_BRANCH" == "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $IMAGE_TAG $IMAGE_TAG_LATEST; fi
    retry:
      automatic:
        - exit_status: -1  # Agent was lost
@@ -110,21 +57,3 @@ steps:
          limit: 2
        - exit_status: -10  # Agent was lost
          limit: 2
-
-  - label: ":docker: Build arm64 image"
-    key: arm64-image-build
-    depends_on: []
-    source_file_dependencies:
-      - ".buildkite/image_build/image_build.yaml"
-      - ".buildkite/image_build/image_build_arm64.sh"
-      - "docker/Dockerfile"
-    commands:
-    - .buildkite/image_build/image_build_arm64.sh $REGISTRY $REPO $BUILDKITE_COMMIT
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
@@ -1,37 +0,0 @@
-#!/bin/bash
-set -e
-
-if [[ $# -lt 3 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
-
-# skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-# build (Grace/GH200 is the arm64 GPU target; sm_90)
-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" \
-  --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
@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3

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

 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
+if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
  echo "Image not found, proceeding with build..."
 else
  echo "Image found"
@@ -24,11 +24,13 @@ fi
 # build
 docker build --file docker/Dockerfile.cpu \
  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --build-arg VLLM_CPU_X86=true \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
+  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
+  --build-arg VLLM_CPU_AVX512BF16=true \
+  --build-arg VLLM_CPU_AVX512VNNI=true \
+  --build-arg VLLM_CPU_AMXBF16=true \
+  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
  --target vllm-test \
  --progress plain .

 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu
+docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu
@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3

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

 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
+if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
  echo "Image not found, proceeding with build..."
 else
  echo "Image found"
@@ -24,10 +24,10 @@ fi
 # build
 docker build --file docker/Dockerfile.cpu \
  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
+  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
+  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
  --target vllm-test \
  --progress plain .

 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu
+docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu
@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3

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

 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then
+if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu) ]]; then
  echo "Image not found, proceeding with build..."
 else
  echo "Image found"
@@ -25,10 +25,10 @@ fi
 docker build \
  --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu \
+  --build-arg buildkite_commit=$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
+docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu
@@ -1,68 +0,0 @@
-#!/bin/bash
-set -euo pipefail
-
-# Build a vLLM test image with PyTorch nightly installed.
-# Called by the pipeline generator's "vLLM Against PyTorch Nightly" group.
-
-if [[ $# -lt 5 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit> <branch> <image_tag>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-BRANCH=$4
-IMAGE_TAG=$5
-
-# --- Arguments ---
-echo "--- :mag: Arguments"
-echo "REGISTRY: ${REGISTRY}"
-echo "REPO: ${REPO}"
-echo "BUILDKITE_COMMIT: ${BUILDKITE_COMMIT}"
-echo "BRANCH: ${BRANCH}"
-echo "IMAGE_TAG: ${IMAGE_TAG}"
-
-# --- ECR login ---
-echo "--- :key: ECR login"
-aws ecr-public get-login-password --region us-east-1 \
-  | docker login --username AWS --password-stdin "$REGISTRY"
-aws ecr get-login-password --region us-east-1 \
-  | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
-
-# --- Set up buildx ---
-echo "--- :docker: Setting up buildx"
-docker buildx create --name vllm-builder --driver docker-container --use || true
-docker buildx inspect --bootstrap
-docker buildx ls
-
-# --- Skip if image already exists ---
-echo "--- :mag: Checking if image already exists"
-if docker manifest inspect "$IMAGE_TAG" >/dev/null 2>&1; then
-  echo "Image found: $IMAGE_TAG — skipping build"
-  exit 0
-fi
-echo "Image not found, proceeding with build..."
-
-# --- CUDA 13.0 for nightly builds ---
-# Nightly CI uses CUDA 13.0 while regular CI stays on CUDA 12.9
-NIGHTLY_CUDA_VERSION="13.0.2"
-NIGHTLY_BUILD_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-devel-ubuntu22.04"
-NIGHTLY_FINAL_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-base-ubuntu22.04"
-
-echo "--- :docker: Building torch nightly image (CUDA ${NIGHTLY_CUDA_VERSION})"
-docker buildx build --file docker/Dockerfile \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --build-arg USE_SCCACHE=1 \
-  --build-arg PYTORCH_NIGHTLY=1 \
-  --build-arg CUDA_VERSION="${NIGHTLY_CUDA_VERSION}" \
-  --build-arg BUILD_BASE_IMAGE="${NIGHTLY_BUILD_BASE_IMAGE}" \
-  --build-arg FINAL_BASE_IMAGE="${NIGHTLY_FINAL_BASE_IMAGE}" \
-  --build-arg torch_cuda_arch_list="8.0 8.9 9.0 10.0 12.0" \
-  --tag "$IMAGE_TAG" \
-  --push \
-  --target test \
-  --progress plain .
-
-echo "--- :white_check_mark: Torch nightly image build complete: $IMAGE_TAG"
@@ -1,34 +0,0 @@
-#!/bin/bash
-set -e
-
-if [[ $# -lt 3 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit>"
-  exit 1
-fi
-
-REGISTRY=$1
-REPO=$2
-BUILDKITE_COMMIT=$3
-
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$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 "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-# build
-docker build \
-  --file docker/Dockerfile.xpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu \
-  --progress plain .
-
-# push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu
@@ -1,26 +0,0 @@
-group: Basic Correctness
-depends_on:
-  - image-build-xpu
-steps:
- label: XPU Sleep Mode
-  timeout_in_minutes: 30
-  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/basic_correctness/test_cumem.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      pytest -v -s basic_correctness/test_mem.py::test_end_to_end'
@@ -1,25 +0,0 @@
-group: Engine Intel
-depends_on:
-  - image-build-xpu
-steps:
- label: Engine (1 GPU)
-  timeout_in_minutes: 30
-  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/v1/engine/
-    - tests/v1/engine/
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py'
@@ -1,27 +0,0 @@
-group: Expert Parallelism
-depends_on: 
-  - image-build-xpu
-steps:
- label: EPLB Algorithm
-  key: eplb-algorithm
-  timeout_in_minutes: 45
-  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/distributed/eplb
-  - tests/distributed/test_eplb_algo.py
-  - tests/distributed/test_eplb_utils.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      pytest -v -s distributed/test_eplb_algo.py'
@@ -1,25 +0,0 @@
-group: Kernels Intel
-depends_on: 
-  - image-build-xpu
-steps:
- label: vLLM IR Tests
-  timeout_in_minutes: 30
-  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/ir
-    - vllm/kernels
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      pytest -v -s kernels/ir'
@@ -1,159 +0,0 @@
-group: LoRA Intel
-depends_on:
-  - image-build-xpu
-steps:
- label: LoRA Runtime + Utils
-  timeout_in_minutes: 45
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 1+
-    mem: 24+
-  no_plugin: true
-  working_dir: "."
-  env:
-    REGISTRY: "public.ecr.aws/q9t5s3a7"
-    REPO: "vllm-ci-test-repo"
-    VLLM_TEST_DEVICE: "xpu"
-  source_file_dependencies:
-    - vllm/lora
-    - tests/lora
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      pytest -v -s lora/test_layers.py &&
-      pytest -v -s lora/test_lora_checkpoints.py &&
-      pytest -v -s lora/test_lora_functions.py &&
-      pytest -v -s lora/test_lora_huggingface.py &&
-      pytest -v -s lora/test_lora_manager.py &&
-      pytest -v -s lora/test_lora_utils.py &&
-      pytest -v -s lora/test_peft_helper.py &&
-      pytest -v -s lora/test_resolver.py &&
-      pytest -v -s lora/test_utils.py &&
-      pytest -v -s lora/test_add_lora.py  &&
-      pytest -v -s lora/test_worker.py'
-
- label: LoRA Fused/MoE Kernels
-  timeout_in_minutes: 45
-  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/lora
-    - tests/lora
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      pytest -v -s lora/test_fused_moe_lora_kernel.py && 
-      pytest -v -s lora/test_moe_lora_align_sum.py --deselect="tests/lora/test_moe_lora_align_sum.py::test_moe_lora_align_block_size_mixed_base_and_lora[1]"'
-
- label: LoRA Punica Kernels
-  timeout_in_minutes: 45
-  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/lora
-    - tests/lora
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      set -o pipefail &&
-      pytest -v -s lora/test_punica_ops.py --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-3-43264-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype1-1-2049-64-128-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-1-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-1-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-8-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype0-3-2049-128-8-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-8-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype1-1-2049-256-128-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-3-64256-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-2-29696-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-3-49408-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-2-16384-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-2-51328-32-4-4]"'
-
- label: LoRA Punica FP8/XPU Ops
-  timeout_in_minutes: 45
-  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/lora
-    - tests/lora
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      pytest -v -s lora/test_punica_ops_fp8.py &&
-      pytest -v -s lora/test_punica_xpu_ops.py'
-
- label: LoRA Models
-  timeout_in_minutes: 45
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 2+
-    mem: 24+
-  no_plugin: true
-  working_dir: "."
-  env:
-    REGISTRY: "public.ecr.aws/q9t5s3a7"
-    REPO: "vllm-ci-test-repo"
-    VLLM_TEST_DEVICE: "xpu"
-  source_file_dependencies:
-    - vllm/lora
-    - tests/lora
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      (pytest -v -s lora/test_mixtral.py --deselect="tests/lora/test_mixtral.py::test_mixtral_lora[4]" || true) &&
-      pytest -v -s lora/test_quant_model.py --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model0]" --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model1]" --deselect="tests/lora/test_quant_model.py::test_quant_model_tp_equality[model0]" &&
-      pytest -v -s lora/test_transformers_model.py &&
-      pytest -v -s lora/test_chatglm3_tp.py &&
-      pytest -s -v lora/test_minicpmv_tp.py'
-
- label: LoRA Multimodal
-  timeout_in_minutes: 45
-  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/lora
-    - tests/lora
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      pytest -v -s lora/test_default_mm_loras.py && 
-      pytest -v -s lora/test_whisper.py'
@@ -1,214 +0,0 @@
-group: Miscellaneous Intel
-depends_on:
-  - image-build-xpu
-steps:
- label: V1 Core + KV + Metrics
-  timeout_in_minutes: 30
-  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/v1/core
-    - tests/v1/executor
-    - tests/v1/kv_offload
-    - tests/v1/worker
-    - tests/v1/kv_connector/unit
-    - tests/v1/metrics
-    - tests/entrypoints/openai/correctness/test_lmeval.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install -r requirements/kv_connectors.txt &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      cd tests &&
-      pytest -v -s v1/executor'
-
- label: V1 Sample + Logits
-  timeout_in_minutes: 30
-  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/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/inputs/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
-    - tests/v1/sample
-    - tests/v1/logits_processors
-    - tests/v1/test_oracle.py
-    - tests/v1/test_request.py
-    - tests/v1/test_outputs.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install lm_eval[api]>=0.4.12 &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      cd tests &&
-      pytest -v -s v1/logits_processors --ignore=v1/logits_processors/test_custom_online.py --ignore=v1/logits_processors/test_custom_offline.py &&
-      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/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
-  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/
-    - vllm/v1/kv_offload/
-    - vllm/v1/kv_connector/
-    - tests/v1/kv_offload/
-    - tests/v1/kv_connector/unit/test_offloading_connector.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      cd tests &&
-      pytest -v -s v1/kv_offload &&
-      pytest -v -s v1/kv_connector/unit/test_offloading_connector.py'
-
- label: Regression
-  key: regression
-  timeout_in_minutes: 30
-  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/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
-  - tests/test_regression
-  commands:
-  - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install modelscope &&
-      cd tests &&
-      pytest -v -s test_regression.py'
-
- label: Metrics, Tracing (2 GPUs)
-  key: metrics-tracing-2-gpus
-  timeout_in_minutes: 30
-  num_devices: 2
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 2+
-    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/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/tracing/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
-  - tests/v1/tracing
-  commands:
-  - >-
-    bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-    'pip install opentelemetry-sdk\>=1.26.0 opentelemetry-api\>=1.26.0 opentelemetry-exporter-otlp\>=1.26.0 opentelemetry-semantic-conventions-ai\>=0.4.1 &&
-    cd tests &&
-    pytest -v -s v1/tracing'
-
- label: Async Engine, Inputs, Utils, Worker
-  key: async-engine-inputs-utils-worker
-  timeout_in_minutes: 30
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 1+
-    mem: 24+
-  no_plugin: true
-  working_dir: "."
-  env:
-    REGISTRY: "public.ecr.aws/q9t5s3a7"
-    REPO: "vllm-ci-test-repo"
-    VLLM_TEST_DEVICE: "xpu"
-  source_file_dependencies:
-  - vllm/assets/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/tokenizers/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
-  - tests/detokenizer
-  - tests/multimodal
-  - tests/utils_
-  commands:
-  - >-
-    bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-    'cd tests &&
-    pip install av &&
-    pytest -v -s detokenizer &&
-    pytest -v -s -m "not cpu_test" ./multimodal &&
-    pytest -v -s utils_ --ignore=utils_/test_mem_utils.py'
@@ -1,62 +0,0 @@
-group: Model Runner V2 Intel
-depends_on:
-  - image-build-xpu
-steps:
- label: Model Runner V2 Core Tests (Intel)
-  timeout_in_minutes: 45
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 2+
-    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/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/
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'export VLLM_USE_V2_MODEL_RUNNER=1 &&
-      cd tests &&
-      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_min_tokens.py'
-
- label: Model Runner V2 Examples (Intel)
-  timeout_in_minutes: 45
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 1+
-    mem: 24+
-  no_plugin: true
-  working_dir: "."
-  env:
-    REGISTRY: "public.ecr.aws/q9t5s3a7"
-    REPO: "vllm-ci-test-repo"
-    VLLM_TEST_DEVICE: "xpu"
-  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/
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'export VLLM_USE_V2_MODEL_RUNNER=1 &&
-      cd examples &&
-      python3 basic/offline_inference/chat.py &&
-      python3 basic/offline_inference/generate.py --model facebook/opt-125m &&
-      python3 generate/multimodal/vision_language_offline.py --seed 0 &&
-      python3 features/automatic_prefix_caching/prefix_caching_offline.py'
@@ -1,131 +0,0 @@
-group: Models - Multimodal
-depends_on: 
-  - image-build-xpu
-steps:
- label: "Multi-Modal Models (Standard) 1: qwen2"
-  key: multi-modal-models-standard-1-qwen2
-  timeout_in_minutes: 45
-  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/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install av git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2" &&
-      pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model'
-
- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
-  key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 45
-  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/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model'
-
- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
-  key: multi-modal-models-standard-3-llava-qwen2-vl
-  timeout_in_minutes: 45
-  device: intel_gpu
-  agent_tags:
-    label: production
-    gpu: 1+
-    mem: 24+
-  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/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma" &&
-      pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model'
-
- label: "Multi-Modal Models (Standard) 4: other + whisper"
-  key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 45
-  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/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install av git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      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/processing'
-
- label: Multi-Modal Processor # 44min
-  key: multi-modal-processor
-  timeout_in_minutes: 45
-  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/multimodal
-  - tests/models/registry.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install av matplotlib ftfy git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      pip install open-clip-torch --no-deps &&
-      cd tests &&
-      pytest -v -s models/multimodal/processing/test_tensor_schema.py
-      --deselect "tests/models/multimodal/processing/test_tensor_schema.py::test_model_tensor_schema[mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4]"
-      --deselect "tests/models/multimodal/processing/test_tensor_schema.py::test_model_tensor_schema[Qwen/Qwen2.5-Omni-7B-AWQ]"
-      --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB'
-  parallelism: 4
@@ -1,123 +0,0 @@
-group: Intel
-steps:
-  - label: ":docker: Build XPU image"
-    soft_fail: true
-    depends_on: []
-    key: image-build-xpu
-    commands:
-      - bash -lc '.buildkite/image_build/image_build_xpu.sh "public.ecr.aws/q9t5s3a7" "vllm-ci-test-repo" "$BUILDKITE_COMMIT"'
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-  - label: "XPU example Test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    device: intel_gpu
-    agent_tags:
-      label: production
-      gpu: 2+
-      mem: 24+
-    no_plugin: true
-    env:
-      REGISTRY: "public.ecr.aws/q9t5s3a7"
-      REPO: "vllm-ci-test-repo"
-    source_file_dependencies:
-      - vllm/
-      - .buildkite/intel_jobs/test-intel.yaml 
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'pip install tblib==3.1.0 &&
-        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager &&
-        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE &&
-        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp &&
-        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN &&
-        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8 &&
-        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --kv-cache-dtype fp8 &&
-        python3 examples/basic/offline_inference/generate.py --model nvidia/Llama-3.1-8B-Instruct-FP8 --block-size 64 --enforce-eager --quantization modelopt --kv-cache-dtype fp8 --attention-backend TRITON_ATTN --max-model-len 4096 &&
-        python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --block-size 64 --enforce-eager --max-model-len 8192 &&
-        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 &&
-        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel &&
-        python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --max-model-len 8192 &&
-        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 V1 test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    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"
-    source_file_dependencies:
-      - vllm/
-      - .buildkite/intel_jobs/test-intel.yaml 
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'cd tests &&
-        bash v1/kv_connector/nixl_integration/run_xpu_disagg_accuracy_test.sh &&
-        pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py &&
-        pytest -v -s v1/engine --ignore=v1/engine/test_output_processor.py &&
-        pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py -k "not test_topk_only and not test_topp_only and not test_topk_and_topp" &&
-        pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py &&
-        pytest -v -s v1/structured_output &&
-        pytest -v -s v1/test_serial_utils.py &&
-        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py --ignore=v1/spec_decode/test_speculators_correctness.py &&
-        pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py --ignore=v1/kv_connector/unit/test_offloading_connector.py'
-  - label: "XPU server test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    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"
-    source_file_dependencies:
-      - vllm/
-      - .buildkite/intel_jobs/test-intel.yaml 
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'pip install av &&
-        cd tests &&
-        pytest -v -s entrypoints/multimodal/openai/chat_completion/test_audio_in_video.py &&
-        pytest -v -s benchmarks/test_serve_cli.py'
-  - label: "XPU quantization test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    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"
-    source_file_dependencies:
-      - vllm/
-      - .buildkite/intel_jobs/test-intel.yaml 
-    commands:
-      - >-
-        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-        'cd tests &&
-        pytest -v -s quantization/test_auto_round.py'
@@ -1,9 +1,6 @@
 # For hf script, without -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
-required_gpu_arch:
-  - gfx942
-  - gfx950
 tasks:
 - name: "mmlu_pro"
  metrics:
@@ -6,7 +6,9 @@ tasks:
    value: 0.7142
  - name: "exact_match,flexible-extract"
    value: 0.4579
-moe_backend: "flashinfer_cutlass"
+env_vars:
+  VLLM_USE_FLASHINFER_MOE_FP8: "1"
+  VLLM_FLASHINFER_MOE_BACKEND: "throughput"
 limit: 1319
 num_fewshot: 5
 max_model_len: 262144
@@ -1,9 +1,6 @@
 # For vllm script, with -t option (tensor parallel size)
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -l 1319 -t 1
 model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
-required_gpu_arch:
-  - gfx942
-  - gfx950
 tasks:
 - name: "gsm8k"
  metrics:
@@ -1,7 +1,4 @@
 model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
-required_gpu_arch:
-  - gfx942
-  - gfx950
 tasks:
  - name: "mmlu_pro"
    metrics:
@@ -0,0 +1,12 @@
+# For vllm script, with -t option (tensor parallel size).
+# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
+model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.6353
+  - name: "exact_match,flexible-extract"
+    value: 0.637
+limit: null
+num_fewshot: null 
@@ -1 +0,0 @@
-Qwen3-235B-A22B-Instruct-2507-FP8.yaml
@@ -1,6 +1,5 @@
 Qwen2.5-1.5B-Instruct.yaml
 Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
-Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
 Qwen1.5-MoE-W4A16-compressed-tensors.yaml
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on chartqa for vllm.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.9.2"

 usage() {
    echo``
@@ -41,4 +41,4 @@ lm_eval --model vllm-vlm \
  --tasks chartqa \
  --batch_size auto \
  --apply_chat_template \
-  --limit "$LIMIT"
+  --limit $LIMIT
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.9.2"

 usage() {
    echo``
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.9.2"

 usage() {
    echo``
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.9.2"

 usage() {
    echo``
@@ -20,11 +20,14 @@ usage() {
    echo
 }

-while getopts "m:l:f:t:" OPT; do
+while getopts "m:b:l:f:t:" OPT; do
  case ${OPT} in
    m )
        MODEL="$OPTARG"
        ;;
+    b )
+        BATCH_SIZE="$OPTARG"
+        ;;
    l )
        LIMIT="$OPTARG"
        ;;
@@ -13,11 +13,9 @@ import os
 from contextlib import contextmanager

 import lm_eval
-import pytest
+import numpy as np
 import yaml

-from vllm.platforms import current_platform
-
 DEFAULT_RTOL = 0.08


@@ -65,13 +63,6 @@ def launch_lm_eval(eval_config, tp_size):
        "allow_deprecated_quantization=True,"
    )

-    if current_platform.is_rocm() and "Nemotron-3" in eval_config["model_name"]:
-        model_args += "attention_backend=TRITON_ATTN"
-
-    moe_backend = eval_config.get("moe_backend", None)
-    if moe_backend is not None:
-        model_args += f"moe_backend={moe_backend},"
-
    env_vars = eval_config.get("env_vars", None)
    with scoped_env_vars(env_vars):
        results = lm_eval.simple_evaluate(
@@ -94,40 +85,9 @@ def launch_lm_eval(eval_config, tp_size):
    return results


-def _check_rocm_gpu_arch_requirement(eval_config):
-    """Skip the test if the model requires a ROCm GPU arch not present.
-
-    Model YAML configs can specify::
-
-        required_gpu_arch:
-          - gfx942
-          - gfx950
-
-    The check only applies on ROCm.  On other platforms (e.g. CUDA) the
-    field is ignored so that shared config files work for both NVIDIA and
-    AMD CI pipelines.
-    """
-    required_archs = eval_config.get("required_gpu_arch")
-    if not required_archs:
-        return
-
-    if not current_platform.is_rocm():
-        return
-
-    from vllm.platforms.rocm import _GCN_ARCH  # noqa: E402
-
-    if not any(arch in _GCN_ARCH for arch in required_archs):
-        pytest.skip(
-            f"Model requires GPU arch {required_archs}, "
-            f"but detected arch is '{_GCN_ARCH}'"
-        )
-
-
 def test_lm_eval_correctness_param(config_filename, tp_size):
    eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))

-    _check_rocm_gpu_arch_requirement(eval_config)
-
    results = launch_lm_eval(eval_config, tp_size)

    rtol = eval_config.get("rtol", DEFAULT_RTOL)
@@ -142,8 +102,6 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
                f"ground_truth={ground_truth:.3f} | "
                f"measured={measured_value:.3f} | rtol={rtol}"
            )
-
-            min_acceptable = ground_truth * (1 - rtol)
-            success = success and measured_value >= min_acceptable
+            success = success and np.isclose(ground_truth, measured_value, rtol=rtol)

    assert success
@@ -83,6 +83,7 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
        "server_parameters": {
            "model": "meta-llama/Meta-Llama-3-8B",
            "tensor_parallel_size": 1,
+            "swap_space": 16,
            "disable_log_stats": "",
            "load_format": "dummy"
        },
@@ -7,10 +7,8 @@ import argparse
 import html as _html
 import json
 import os
-from contextlib import nullcontext
 from dataclasses import dataclass
 from importlib import util
-from pathlib import Path

 import pandas as pd

@@ -33,45 +31,6 @@ pd.set_option("display.precision", 2)
 pd.set_option("display.float_format", lambda x: f"{x:.2f}")


-# -----------------------------
-# Concurrency normalization (NEW, small)
-# -----------------------------
-def _find_concurrency_col(df: pd.DataFrame) -> str:
-    for c in [
-        "# of max concurrency.",
-        "# of max concurrency",
-        "Max Concurrency",
-        "max_concurrency",
-        "Concurrency",
-    ]:
-        if c in df.columns:
-            return c
-
-    for c in df.columns:
-        if "concurr" in str(c).lower():
-            s = df[c]
-            if s.dtype.kind in "iu" and s.nunique() > 1 and s.min() >= 1:
-                return c
-
-    raise ValueError(
-        "Cannot infer concurrency column. "
-        "Please rename the column to one of the known names "
-        "or add an explicit override (e.g., --concurrency-col)."
-    )
-
-
-def _normalize_concurrency_in_df(
-    df: pd.DataFrame, canonical: str = "# of max concurrency."
-) -> pd.DataFrame:
-    if canonical in df.columns:
-        return df
-    detected = _find_concurrency_col(df)
-    if detected in df.columns and detected != canonical:
-        return df.rename(columns={detected: canonical})
-    df[canonical] = pd.NA
-    return df
-
-
 # -----------------------------
 # Core data compare
 # -----------------------------
@@ -91,25 +50,19 @@ def compare_data_columns(
    - Concat along axis=1 (indexes align), then reset_index so callers can
      group by columns.
    - If --debug, add a <file_label>_name column per file.
-
-    Minimal fix to support different max_concurrency lists across files:
-      - normalize concurrency column naming to "# of max concurrency."
-      - align on UNION of keys (missing points become NaN)
-      - BUGFIX: don't drop throughput rows based on P99/Median presence
    """
    print("\ncompare_data_column:", data_column)

    frames = []
    raw_data_cols: list[str] = []
+    compare_frames = []

-    # Determine key cols after normalizing concurrency
    cols_per_file: list[set] = []
    for f in files:
        try:
            df_tmp = pd.read_json(f, orient="records")
        except Exception as err:
            raise ValueError(f"Failed to read {f}") from err
-        df_tmp = _normalize_concurrency_in_df(df_tmp, canonical="# of max concurrency.")
        cols_per_file.append(set(df_tmp.columns))

    key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
@@ -120,25 +73,12 @@ def compare_data_columns(
            "No common key columns found from info_cols across the input files."
        )

-    union_index = None
-    metas: list[pd.DataFrame] = []
-    staged: list[tuple[str, pd.Series, pd.Series | None]] = []
+    meta_added = False

    for file in files:
        df = pd.read_json(file, orient="records")
-        df = _normalize_concurrency_in_df(df, canonical="# of max concurrency.")

-        # BUGFIX: only drop rows for latency-like metrics; throughput rows may have
-        # NaN in P99/Median columns even if the column exists in the JSON.
-        metric_lc = str(data_column).lower()
-        is_latency_metric = (
-            "ttft" in metric_lc
-            or "tpot" in metric_lc
-            or "p99" in metric_lc
-            or "median" in metric_lc
-            or metric_lc.strip() in {"p99", "median"}
-        )
-        if is_latency_metric and drop_column in df.columns:
+        if drop_column in df.columns:
            df = df.dropna(subset=[drop_column], ignore_index=True)

        for c in (
@@ -163,61 +103,35 @@ def compare_data_columns(
            meta = meta.groupby(level=key_cols, dropna=False).first()

        file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
-
-        if data_column in df_idx.columns:
-            s = df_idx[data_column]
-            if not s.index.is_unique:
-                s = s.groupby(level=key_cols, dropna=False).mean()
-        else:
-            # keep NA series to preserve meta keys for union_index
-            s = pd.Series(pd.NA, index=meta.index)
+        s = df_idx[data_column]
+        if not s.index.is_unique:
+            s = s.groupby(level=key_cols, dropna=False).mean()
        s.name = file_label

-        name_s = None
+        if not meta_added:
+            frames.append(meta)
+            meta_added = True
+
        if debug and name_column in df_idx.columns:
            name_s = df_idx[name_column]
            if not name_s.index.is_unique:
                name_s = name_s.groupby(level=key_cols, dropna=False).first()
            name_s.name = f"{file_label}_name"
+            frames.append(name_s)

-        if union_index is None:
-            union_index = meta.index
-        else:
-            union_index = union_index.union(meta.index)
-        metas.append(meta)
-
-        staged.append((file_label, s, name_s))
-
-    if union_index is None:
-        raise ValueError("No data found after loading inputs.")
-
-    # meta first (union-aligned): build UNION meta across all files
-    if metas:
-        meta_union = pd.concat(metas, axis=0)
-        # Collapse duplicates on the MultiIndex; keep first non-null per column
-        meta_union = meta_union.groupby(level=key_cols, dropna=False).first()
-        frames.append(meta_union.reindex(union_index))
-
-    # values + ratios (union-aligned)
-    metric_series_aligned: list[pd.Series] = []
-    for file_label, s, name_s in staged:
-        s_aligned = s.reindex(union_index)
-        frames.append(s_aligned)
+        frames.append(s)
        raw_data_cols.append(file_label)
-        metric_series_aligned.append(s_aligned)
+        compare_frames.append(s)

-        if debug and name_s is not None:
-            frames.append(name_s.reindex(union_index))
-
-        if len(metric_series_aligned) >= 2:
-            base = metric_series_aligned[0]
-            current = metric_series_aligned[-1]
-            if "P99" in str(data_column) or "Median" in str(data_column):
+        if len(compare_frames) >= 2:
+            base = compare_frames[0]
+            current = compare_frames[-1]
+            if "P99" in data_column or "Median" in data_column:
                ratio = base / current
            else:
                ratio = current / base
            ratio = ratio.mask(base == 0)
-            ratio.name = f"Ratio 1 vs {len(metric_series_aligned)}"
+            ratio.name = f"Ratio 1 vs {len(compare_frames)}"
            frames.append(ratio)

    concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
@@ -288,10 +202,24 @@ def split_json_by_tp_pp(
 # -----------------------------
 # Styling helpers
 # -----------------------------
+def _find_concurrency_col(df: pd.DataFrame) -> str:
+    for c in [
+        "# of max concurrency.",
+        "# of max concurrency",
+        "Max Concurrency",
+        "max_concurrency",
+        "Concurrency",
+    ]:
+        if c in df.columns:
+            return c
+    for c in df.columns:
+        if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
+            return c
+    return "# of max concurrency."
+
+
 def _highlight_threshold(
-    df: pd.DataFrame,
-    threshold: float,
-    slack_pct: float = 0.0,
+    df: pd.DataFrame, threshold: float
 ) -> pd.io.formats.style.Styler:
    conc_col = _find_concurrency_col(df)
    key_cols = [
@@ -304,24 +232,12 @@ def _highlight_threshold(
    ]
    conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]

-    try:
-        slack_pct = float(slack_pct or 0.0)
-    except Exception:
-        slack_pct = 0.0
-    slack_limit = threshold * (1.0 + slack_pct / 100.0)
-
-    def _cell(v):
-        if pd.isna(v):
-            return ""
-        if v <= threshold:
-            # Strict SLA
-            return "background-color:#e6ffe6;font-weight:bold;"
-        if v <= slack_limit:
-            # Within slack range
-            return "background-color:#ffe5cc;font-weight:bold;"
-        return ""
-
-    return df.style.map(_cell, subset=conf_cols)
+    return df.style.map(
+        lambda v: "background-color:#e6ffe6;font-weight:bold;"
+        if pd.notna(v) and v <= threshold
+        else "",
+        subset=conf_cols,
+    )


 def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
@@ -359,177 +275,6 @@ def _apply_two_decimals(
    return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")


-# -----------------------------
-# Export helpers (Excel + CSV)
-# -----------------------------
-def _sanitize_sheet_name(name: str) -> str:
-    """
-    Excel sheet constraints:
-      - max 31 chars
-      - cannot contain: : \ / ? * [ ]
-      - cannot be empty
-
-    NOTE: Use fast, non-regex operations here to avoid the third-party `regex`
-    module's compile overhead/edge-cases on some systems.
-    """
-    name = "sheet" if name is None else str(name)
-
-    # Replace illegal characters with underscore.
-    trans = str.maketrans(
-        {
-            ":": "_",
-            "\\": "_",
-            "/": "_",
-            "?": "_",
-            "*": "_",
-            "[": "_",
-            "]": "_",
-        }
-    )
-    name = name.translate(trans)
-
-    # Strip quotes/spaces and collapse whitespace.
-    name = name.strip().strip("'")
-    name = " ".join(name.split())
-
-    if not name:
-        name = "sheet"
-    return name[:31]
-
-
-def _group_to_sheet_base(group_cols: list[str], gkey_tuple) -> str:
-    d = dict(zip(group_cols, gkey_tuple))
-
-    # Always keep input/output lengths (these are important).
-    ilen = d.get("Input Len", "")
-    olen = d.get("Output Len", "")
-    lens = f"_{ilen}x{olen}" if ilen != "" and olen != "" else ""
-
-    # Shorten model name aggressively to make room for lens.
-    model = d.get("Model", "model")
-    leaf = str(model).split("/")[-1]
-
-    max_model_len = max(1, 31 - len(lens))
-    model_short = leaf[:max_model_len]
-
-    return _sanitize_sheet_name(f"{model_short}{lens}")
-
-
-def _write_tables_to_excel_sheet(
-    writer: pd.ExcelWriter, sheet: str, blocks: list[tuple[str, pd.DataFrame]]
-):
-    """Write all blocks to a sheet with a single to_excel() call.
-
-    Pandas+openpyxl can be extremely slow when called many times per sheet.
-    We flatten blocks into one table with a 'Section' column to keep structure
-    while making Excel generation fast and deterministic.
-    """
-    if not blocks:
-        pd.DataFrame().to_excel(writer, sheet_name=sheet, index=False)
-        return
-
-    combined_parts: list[pd.DataFrame] = []
-    for title, df in blocks:
-        df2 = df.copy()
-        # Put the section label as the first column for readability.
-        df2.insert(0, "Section", title)
-        combined_parts.append(df2)
-
-    combined = pd.concat(combined_parts, axis=0, ignore_index=True, sort=False)
-    combined.to_excel(writer, sheet_name=sheet, index=False)
-
-
-def _safe_filename(s: str) -> str:
-    # Fast path without the third-party `regex` module.
-    s = " ".join(str(s).strip().split())
-    allowed = []
-    for ch in s:
-        if ch.isalnum() or ch in "._-":
-            allowed.append(ch)
-        else:
-            allowed.append("_")
-    out = "".join(allowed)
-    return out[:180] if len(out) > 180 else out
-
-
-# -----------------------------
-# vLLM environment export helper
-# -----------------------------
-def _parse_vllm_env_txt(env_path: Path) -> pd.DataFrame:
-    """Parse vllm_env.txt into a flat table (Section, Key, Value).
-
-    Supports:
-      - section headers as standalone lines (no ':' or '=')
-      - key-value lines like 'OS: Ubuntu ...'
-      - env var lines like 'HF_HOME=/data/hf'
-    """
-    lines = env_path.read_text(encoding="utf-8", errors="replace").splitlines()
-    section = "General"
-    rows: list[dict] = []
-
-    def set_section(s: str):
-        nonlocal section
-        s = (s or "").strip()
-        if s:
-            section = s
-
-    for raw in lines:
-        stripped = raw.strip()
-        if not stripped:
-            continue
-        # divider lines like =====
-        if set(stripped) <= {"="}:
-            continue
-
-        # section header heuristic: short standalone line
-        if ":" not in stripped and "=" not in stripped and len(stripped) <= 64:
-            if stripped.lower().startswith("collecting environment information"):
-                continue
-            set_section(stripped)
-            continue
-
-        # env var style: KEY=VALUE (and not a URL with :)
-        if "=" in stripped and ":" not in stripped:
-            k, v = stripped.split("=", 1)
-            k = k.strip()
-            v = v.strip()
-            if k:
-                rows.append({"Section": section, "Key": k, "Value": v})
-            continue
-
-        # key: value
-        if ":" in stripped:
-            k, v = stripped.split(":", 1)
-            k = k.strip()
-            v = v.strip()
-            if k:
-                rows.append({"Section": section, "Key": k, "Value": v})
-            continue
-
-    return pd.DataFrame(rows, columns=["Section", "Key", "Value"])
-
-
-def _load_env_df_for_inputs(args, files: list[str]) -> pd.DataFrame | None:
-    """Load vllm_env.txt next to the *original* input JSON file.
-
-    Note: when only one -f is provided, the script may split JSON into ./splits/...,
-    but vllm_env.txt typically lives next to the original benchmark_results.json.
-    """
-    base_dir: Path | None = None
-    if getattr(args, "file", None):
-        base_dir = Path(args.file[0]).resolve().parent
-    elif files:
-        base_dir = Path(files[0]).resolve().parent
-    if base_dir is None:
-        return None
-
-    env_path = base_dir / "vllm_env.txt"
-    if not env_path.exists():
-        return None
-    df = _parse_vllm_env_txt(env_path)
-    return df
-
-
 # -----------------------------
 # Valid max concurrency summary helpers
 # -----------------------------
@@ -556,11 +301,7 @@ def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:


 def _max_concurrency_ok(
-    df: pd.DataFrame,
-    conc_col: str,
-    cfg_col: str,
-    threshold: float,
-    slack_pct: float = 0.0,
+    df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
 ):
    if df is None or conc_col not in df.columns or cfg_col not in df.columns:
        return pd.NA
@@ -573,14 +314,7 @@ def _max_concurrency_ok(
    if d.empty:
        return pd.NA

-    # Accept values up to (1 + slack_pct%) above the SLA.
-    try:
-        slack_pct = float(slack_pct or 0.0)
-    except Exception:
-        slack_pct = 0.0
-    effective_limit = float(threshold) * (1.0 + slack_pct / 100.0)
-
-    ok = d[d[cfg_col] <= effective_limit]
+    ok = d[d[cfg_col] <= threshold]
    if ok.empty:
        return pd.NA

@@ -646,25 +380,15 @@ def build_valid_max_concurrency_summary_html(
    if not cfg_cols:
        cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)

-    # Display SLA ranges in the table header (SLA .. SLA*(1+slack))
-    ttft_hi = args.ttft_max_ms * (1.0 + args.ttft_slack_pct / 100.0)
-    tpot_hi = args.tpot_max_ms * (1.0 + args.tpot_slack_pct / 100.0)
-    ttft_range = f"{args.ttft_max_ms:g}–{ttft_hi:g} ms (+{args.ttft_slack_pct:g}%)"
-    tpot_range = f"{args.tpot_max_ms:g}–{tpot_hi:g} ms (+{args.tpot_slack_pct:g}%)"
-
    rows = []
    for cfg in cfg_cols:
        ttft_max = (
-            _max_concurrency_ok(
-                ttft_group_df, conc_col, cfg, args.ttft_max_ms, args.ttft_slack_pct
-            )
+            _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
            if ttft_group_df is not None
            else pd.NA
        )
        tpot_max = (
-            _max_concurrency_ok(
-                tpot_group_df, conc_col, cfg, args.tpot_max_ms, args.tpot_slack_pct
-            )
+            _max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
            if tpot_group_df is not None
            else pd.NA
        )
@@ -693,8 +417,8 @@ def build_valid_max_concurrency_summary_html(
        rows.append(
            {
                "Configuration": cfg,
-                f"Max {conc_col} (TTFT ≤ {ttft_range})": ttft_max,
-                f"Max {conc_col} (TPOT ≤ {tpot_range})": tpot_max,
+                f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
+                f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
                f"Max {conc_col} (Both)": both,
                "Output Tput @ Both (tok/s)": tput_at_both,
                "TTFT @ Both (ms)": ttft_at_both,
@@ -704,6 +428,7 @@ def build_valid_max_concurrency_summary_html(

    summary_df = pd.DataFrame(rows)

+    # --- Coerce numeric columns so Styler doesn't miss them due to object dtype ---
    for c in summary_df.columns:
        if c == "Configuration":
            continue
@@ -711,10 +436,12 @@ def build_valid_max_concurrency_summary_html(

    both_col = f"Max {conc_col} (Both)"

+    # --- Strict 2-decimal formatting for ALL non-Configuration columns ---
    formatters = {}
    for c in summary_df.columns:
        if c == "Configuration":
            continue
+        # default argument binds per-column formatter correctly
        formatters[c] = lambda v: "" if pd.isna(v) else f"{float(v):.2f}"

    styler = summary_df.style.format(formatters)
@@ -733,104 +460,6 @@ def build_valid_max_concurrency_summary_html(
    return title + styler.to_html(table_attributes='border="1" class="dataframe"')


-def build_valid_max_concurrency_summary_df(
-    tput_group_df: pd.DataFrame | None,
-    ttft_group_df: pd.DataFrame | None,
-    tpot_group_df: pd.DataFrame | None,
-    conc_col: str,
-    args,
-) -> pd.DataFrame | None:
-    if ttft_group_df is None and tpot_group_df is None:
-        return None
-
-    ttft_cols = (
-        _config_value_columns(ttft_group_df, conc_col)
-        if ttft_group_df is not None
-        else []
-    )
-    tpot_cols = (
-        _config_value_columns(tpot_group_df, conc_col)
-        if tpot_group_df is not None
-        else []
-    )
-    tput_cols = (
-        _config_value_columns(tput_group_df, conc_col)
-        if tput_group_df is not None
-        else []
-    )
-
-    if ttft_group_df is not None and tpot_group_df is not None:
-        cfg_cols = [c for c in ttft_cols if c in tpot_cols]
-        if tput_group_df is not None:
-            cfg_cols = [c for c in cfg_cols if c in tput_cols] or cfg_cols
-    else:
-        cfg_cols = ttft_cols or tpot_cols
-
-    if not cfg_cols:
-        cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
-
-    ttft_hi = args.ttft_max_ms * (1.0 + args.ttft_slack_pct / 100.0)
-    tpot_hi = args.tpot_max_ms * (1.0 + args.tpot_slack_pct / 100.0)
-    ttft_range = f"{args.ttft_max_ms:g}–{ttft_hi:g} ms (+{args.ttft_slack_pct:g}%)"
-    tpot_range = f"{args.tpot_max_ms:g}–{tpot_hi:g} ms (+{args.tpot_slack_pct:g}%)"
-
-    rows = []
-    for cfg in cfg_cols:
-        ttft_max = (
-            _max_concurrency_ok(
-                ttft_group_df, conc_col, cfg, args.ttft_max_ms, args.ttft_slack_pct
-            )
-            if ttft_group_df is not None
-            else pd.NA
-        )
-        tpot_max = (
-            _max_concurrency_ok(
-                tpot_group_df, conc_col, cfg, args.tpot_max_ms, args.tpot_slack_pct
-            )
-            if tpot_group_df is not None
-            else pd.NA
-        )
-        both = (
-            pd.NA
-            if (pd.isna(ttft_max) or pd.isna(tpot_max))
-            else min(ttft_max, tpot_max)
-        )
-
-        tput_at_both = (
-            _value_at_concurrency(tput_group_df, conc_col, cfg, both)
-            if tput_group_df is not None
-            else pd.NA
-        )
-        ttft_at_both = (
-            _value_at_concurrency(ttft_group_df, conc_col, cfg, both)
-            if ttft_group_df is not None
-            else pd.NA
-        )
-        tpot_at_both = (
-            _value_at_concurrency(tpot_group_df, conc_col, cfg, both)
-            if tpot_group_df is not None
-            else pd.NA
-        )
-
-        rows.append(
-            {
-                "Configuration": cfg,
-                f"Max {conc_col} (TTFT ≤ {ttft_range})": ttft_max,
-                f"Max {conc_col} (TPOT ≤ {tpot_range})": tpot_max,
-                f"Max {conc_col} (Both)": both,
-                "Output Tput @ Both (tok/s)": tput_at_both,
-                "TTFT @ Both (ms)": ttft_at_both,
-                "TPOT @ Both (ms)": tpot_at_both,
-            }
-        )
-
-    df = pd.DataFrame(rows)
-    for c in df.columns:
-        if c != "Configuration":
-            df[c] = pd.to_numeric(df[c], errors="coerce")
-    return df
-
-
 # -----------------------------
 # Plot helper
 # -----------------------------
@@ -908,35 +537,6 @@ def build_parser() -> argparse.ArgumentParser:
        default=100.0,
        help="Reference limit for TPOT plots (ms)",
    )
-
-    # ---- SLA tolerance (slack) options ----
-    parser.add_argument(
-        "--ttft-slack-pct",
-        type=float,
-        default=5.0,
-        help="Allowed percentage above TTFT SLA (default: 5).",
-    )
-    parser.add_argument(
-        "--tpot-slack-pct",
-        type=float,
-        default=5.0,
-        help="Allowed percentage above TPOT SLA (default: 5).",
-    )
-
-    # ---- export options ----
-    parser.add_argument(
-        "--excel-out",
-        type=str,
-        default="perf_comparison.xlsx",
-        help="Write one sheet per (Model, Dataset, Input Len, Output Len).",
-    )
-    parser.add_argument(
-        "--csv-out-dir",
-        type=str,
-        default="",
-        help="If set, write per-group per-metric CSVs into this directory.",
-    )
-
    return parser


@@ -1015,13 +615,9 @@ def render_metric_table_html(

    metric_name = metric_label.lower()
    if "ttft" in metric_name:
-        styler = _highlight_threshold(
-            display_group, args.ttft_max_ms, args.ttft_slack_pct
-        )
+        styler = _highlight_threshold(display_group, args.ttft_max_ms)
    elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
-        styler = _highlight_threshold(
-            display_group, args.tpot_max_ms, args.tpot_slack_pct
-        )
+        styler = _highlight_threshold(display_group, args.tpot_max_ms)
    else:
        styler = display_group.style

@@ -1061,6 +657,7 @@ def maybe_write_plot(
        markers=True,
    )

+    # Ensure plot hover + y tick labels are also 2 decimals.
    fig.update_traces(hovertemplate="%{y:.2f}<extra></extra>")
    fig.update_yaxes(tickformat=".2f")

@@ -1133,186 +730,87 @@ def write_report_group_first(
        for metric_label, (df, _) in metric_cache.items()
    }

-    csv_dir = Path(args.csv_out_dir) if args.csv_out_dir else None
-    if csv_dir:
-        csv_dir.mkdir(parents=True, exist_ok=True)
+    with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
+        main_fh.write('<meta charset="utf-8">\n')
+        for gkey in group_keys:
+            gkey_tuple = normalize_group_key(gkey)
+            suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
+            sub_path = group_filename(gkey_tuple)
+            group_header = (
+                '<div style="font-size: 1.4em; font-weight: 700; '
+                'margin: 18px 0 10px 0;">'
+                f"{_html.escape(suffix)}"
+                "</div>\n"
+            )

-    excel_path = args.excel_out or "perf_comparison.xlsx"
-    disable_excel = os.getenv("VLLM_COMPARE_DISABLE_EXCEL", "0") == "1"
+            main_fh.write(group_header)
+            with open(sub_path, "w", encoding="utf-8") as sub_fh:
+                sub_fh.write('<meta charset="utf-8">\n')
+                sub_fh.write(group_header)
+                tput_group_df = None
+                ttft_group_df = None
+                tpot_group_df = None
+                conc_col = args.xaxis

-    # Prefer xlsxwriter for speed; fallback to openpyxl if unavailable.
-    excel_engine = (
-        os.getenv("VLLM_COMPARE_EXCEL_ENGINE", "xlsxwriter").strip() or "xlsxwriter"
-    )
-    if excel_engine == "xlsxwriter" and util.find_spec("xlsxwriter") is None:
-        excel_engine = "openpyxl"
+                for metric_label in plan.data_cols:
+                    gb = metric_groupbys[metric_label]
+                    df_sorted, raw_data_cols = metric_cache[metric_label]

-    excel_engine_kwargs = {}
-    if excel_engine == "xlsxwriter":
-        # Reduce memory pressure & usually faster writes.
-        excel_engine_kwargs = {"options": {"constant_memory": True}}
+                    try:
+                        group_df = gb.get_group(gkey)
+                    except KeyError:
+                        missing = (
+                            '<div style="font-size: 1.1em; font-weight: 600; '
+                            'margin: 10px 0;">'
+                            f"{_html.escape(metric_label)} — missing for this group"
+                            "</div>\n"
+                        )

-    xw_ctx = (
-        nullcontext(None)
-        if disable_excel
-        else pd.ExcelWriter(
-            excel_path, engine=excel_engine, engine_kwargs=excel_engine_kwargs
-        )
-    )
-    with xw_ctx as xw:
-        used_sheets: set[str] = set()
-        # ---- Environment sheet (first) ----
-        env_sheet = _sanitize_sheet_name("Environment")
-        env_df = _load_env_df_for_inputs(args, files)
-        if xw is not None:
-            if env_df is None or env_df.empty:
-                pd.DataFrame(
-                    [
-                        {
-                            "Section": "Environment",
-                            "Key": "vllm_env.txt",
-                            "Value": "NOT FOUND (or empty)",
-                        }
-                    ]
-                ).to_excel(xw, sheet_name=env_sheet, index=False)
-            else:
-                env_df.to_excel(xw, sheet_name=env_sheet, index=False)
-            used_sheets.add(env_sheet)
-        with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
-            main_fh.write('<meta charset="utf-8">\n')
-            for gkey in group_keys:
-                gkey_tuple = normalize_group_key(gkey)
-                suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
-                sub_path = group_filename(gkey_tuple)
-                group_header = (
-                    '<div style="font-size: 1.4em; font-weight: 700; '
-                    'margin: 18px 0 10px 0;">'
-                    f"{_html.escape(suffix)}"
-                    "</div>\n"
+                        main_fh.write(missing)
+                        sub_fh.write(missing)
+                        continue
+
+                    if conc_col not in group_df.columns:
+                        conc_col = _find_concurrency_col(group_df)
+
+                    mn = metric_label.lower().strip()
+                    if "tok/s" in mn:
+                        tput_group_df = group_df
+                    elif "ttft" in mn:
+                        ttft_group_df = group_df
+                    elif mn in ("p99", "median") or "tpot" in mn:
+                        tpot_group_df = group_df
+
+                    display_group = group_df.drop(
+                        columns=group_cols_canonical, errors="ignore"
+                    )
+
+                    html = render_metric_table_html(
+                        display_group, metric_label, suffix, args
+                    )
+                    main_fh.write(html)
+                    sub_fh.write(html)
+
+                    maybe_write_plot(
+                        main_fh,
+                        sub_fh,
+                        group_df=group_df,
+                        raw_data_cols=raw_data_cols,
+                        metric_label=metric_label,
+                        y_axis_col=y_axis_col,
+                        args=args,
+                    )
+
+                summary_html = build_valid_max_concurrency_summary_html(
+                    tput_group_df=tput_group_df,
+                    ttft_group_df=ttft_group_df,
+                    tpot_group_df=tpot_group_df,
+                    conc_col=conc_col,
+                    args=args,
                )
-
-                main_fh.write(group_header)
-
-                do_excel = xw is not None
-                sheet = _group_to_sheet_base(group_cols_canonical, gkey_tuple)
-                sheet_base = sheet
-                if do_excel:
-                    dedup_i = 1
-                    while sheet in used_sheets:
-                        dedup_i += 1
-                        suffix = f"_{dedup_i}"
-                        # Ensure uniqueness even when sheet names are truncated.
-                        base = str(sheet_base)
-                        keep = max(1, 31 - len(suffix))
-                        sheet = _sanitize_sheet_name(base[:keep] + suffix)
-                    used_sheets.add(sheet)
-
-                excel_blocks: list[tuple[str, pd.DataFrame]] = []
-
-                with open(sub_path, "w", encoding="utf-8") as sub_fh:
-                    sub_fh.write('<meta charset="utf-8">\n')
-                    sub_fh.write(group_header)
-                    tput_group_df = None
-                    ttft_group_df = None
-                    tpot_group_df = None
-                    conc_col = args.xaxis
-
-                    for metric_label in plan.data_cols:
-                        gb = metric_groupbys[metric_label]
-                        df_sorted, raw_data_cols = metric_cache[metric_label]
-
-                        try:
-                            group_df = gb.get_group(gkey)
-                        except KeyError:
-                            missing = (
-                                '<div style="font-size: 1.1em; font-weight: 600; '
-                                'margin: 10px 0;">'
-                                f"{_html.escape(metric_label)} — missing for this group"
-                                "</div>\n"
-                            )
-                            main_fh.write(missing)
-                            sub_fh.write(missing)
-                            continue
-
-                        if conc_col not in group_df.columns:
-                            conc_col = _find_concurrency_col(group_df)
-
-                        mn = metric_label.lower().strip()
-                        if "tok/s" in mn:
-                            tput_group_df = group_df
-                        elif "ttft" in mn:
-                            ttft_group_df = group_df
-                        elif mn in ("p99", "median") or "tpot" in mn:
-                            tpot_group_df = group_df
-
-                        display_group = group_df.drop(
-                            columns=group_cols_canonical, errors="ignore"
-                        )
-
-                        html = render_metric_table_html(
-                            display_group, metric_label, suffix, args
-                        )
-                        main_fh.write(html)
-                        sub_fh.write(html)
-
-                        maybe_write_plot(
-                            main_fh,
-                            sub_fh,
-                            group_df=group_df,
-                            raw_data_cols=raw_data_cols,
-                            metric_label=metric_label,
-                            y_axis_col=y_axis_col,
-                            args=args,
-                        )
-
-                        excel_blocks.append(
-                            (metric_label, group_df.reset_index(drop=True))
-                        )
-                        if csv_dir:
-                            fn = _safe_filename(
-                                f"{sheet}__{metric_label}".replace(" ", "_").replace(
-                                    "/", "_"
-                                )
-                            )
-                            group_df.to_csv(csv_dir / f"{fn}.csv", index=False)
-
-                    summary_html = build_valid_max_concurrency_summary_html(
-                        tput_group_df=tput_group_df,
-                        ttft_group_df=ttft_group_df,
-                        tpot_group_df=tpot_group_df,
-                        conc_col=conc_col,
-                        args=args,
-                    )
-                    if summary_html:
-                        main_fh.write(summary_html)
-                        sub_fh.write(summary_html)
-
-                    summary_df = build_valid_max_concurrency_summary_df(
-                        tput_group_df=tput_group_df,
-                        ttft_group_df=ttft_group_df,
-                        tpot_group_df=tpot_group_df,
-                        conc_col=conc_col,
-                        args=args,
-                    )
-                    if summary_df is not None:
-                        excel_blocks.append(
-                            ("Valid Max Concurrency Summary", summary_df)
-                        )
-                        if csv_dir:
-                            fn = _safe_filename(
-                                f"{sheet}__Valid_Max_Concurrency_Summary"
-                            )
-                            summary_df.to_csv(csv_dir / f"{fn}.csv", index=False)
-
-                if do_excel:
-                    _write_tables_to_excel_sheet(xw, sheet, excel_blocks)
-
-    if disable_excel:
-        print("Skipped Excel generation (VLLM_COMPARE_DISABLE_EXCEL=1).")
-    else:
-        print(f"Wrote Excel: {excel_path}")
-    if csv_dir:
-        print(f"Wrote CSVs under: {csv_dir}")
+                if summary_html:
+                    main_fh.write(summary_html)
+                    sub_fh.write(summary_html)


 def main():
@@ -1,4 +1,6 @@
 #!/bin/bash
+
+# This script should be run inside the CI process
 # This script assumes that we are already inside the vllm/ directory
 # Benchmarking results will be available inside vllm/benchmarks/results/

@@ -7,26 +9,14 @@
 set -x
 set -o pipefail

-# Environment-driven debug controls (like ON_CPU=1)
-DRY_RUN="${DRY_RUN:-0}"
-MODEL_FILTER="${MODEL_FILTER:-}"
-DTYPE_FILTER="${DTYPE_FILTER:-}"
-
-# Adaptive search controls
-ENABLE_ADAPTIVE_CONCURRENCY="${ENABLE_ADAPTIVE_CONCURRENCY:-0}"
-SLA_TTFT_MS="${SLA_TTFT_MS:-3000}"
-SLA_TPOT_MS="${SLA_TPOT_MS:-100}"
-ADAPTIVE_MAX_PROBES="${ADAPTIVE_MAX_PROBES:-8}"
-ADAPTIVE_MAX_CONCURRENCY="${ADAPTIVE_MAX_CONCURRENCY:-1024}"
-
 check_gpus() {
  if command -v nvidia-smi; then
    # check the number of GPUs and GPU type.
-    declare -g gpu_count=$(nvidia-smi --list-gpus | grep -c . || true)
+    declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
  elif command -v amd-smi; then
-    declare -g gpu_count=$(amd-smi list | grep -c 'GPU' || true)
+    declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
  elif command -v hl-smi; then
-    declare -g gpu_count=$(hl-smi --list | grep -ci "Module ID" || true)
+    declare -g gpu_count=$(hl-smi --list | grep -i "Module ID" | wc -l)
  fi

  if [[ $gpu_count -gt 0 ]]; then
@@ -54,7 +44,7 @@ check_cpus() {
  declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
  if [[ $numa_count -gt 0 ]]; then
    echo "NUMA found."
-    echo "$numa_count"
+    echo $numa_count
  else
    echo "Need at least 1 NUMA to run benchmarking."
    exit 1
@@ -122,12 +112,13 @@ json2envs() {
 }

 wait_for_server() {
+  # wait for vllm server to start
+  # return 1 if vllm server crashes
  local timeout_val="1200"
  timeout "$timeout_val" bash -c '
-    until curl -sf http://localhost:8000/v1/models >/dev/null; do
+    until curl -X POST localhost:8000/v1/completions; do
      sleep 1
-    done
-  '
+    done' && return 0 || return 1
 }

 kill_processes_launched_by_current_bash() {
@@ -190,304 +181,6 @@ upload_to_buildkite() {
  $BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
 }

-# -------------------------------
-# Adaptive concurrency helpers
-# -------------------------------
-result_json_path_for_serving() {
-  local test_name=$1
-  local qps=$2
-  local max_concurrency=$3
-  echo "$RESULTS_FOLDER/${test_name}_qps_${qps}_concurrency_${max_concurrency}.json"
-}
-
-extract_metric_ms() {
-  local metric_name=$1
-  local json_file=$2
-
-  [[ -f "$json_file" ]] || return 0
-
-  if [[ "$metric_name" == "ttft" ]]; then
-    jq -r '
-      [
-        .ttft_ms.p99?,
-        .metrics.ttft_ms.p99?,
-        .ttft.p99?,
-        .metrics.ttft.p99?,
-        .p99_ttft_ms?,
-        .ttft_ms.mean?,
-        .metrics.ttft_ms.mean?,
-        .ttft.mean?,
-        .metrics.ttft.mean?,
-        .mean_ttft_ms?
-      ] | map(select(. != null)) | .[0] // empty
-    ' "$json_file"
-  else
-    jq -r '
-      [
-        .tpot_ms.p99?,
-        .metrics.tpot_ms.p99?,
-        .tpot.p99?,
-        .metrics.tpot.p99?,
-        .p99_tpot_ms?,
-        .itl_ms.p99?,
-        .metrics.itl_ms.p99?,
-        .inter_token_latency_ms.p99?,
-        .tpot_ms.mean?,
-        .metrics.tpot_ms.mean?,
-        .tpot.mean?,
-        .metrics.tpot.mean?,
-        .itl_ms.mean?,
-        .metrics.itl_ms.mean?,
-        .mean_tpot_ms?,
-        .mean_itl_ms?
-      ] | map(select(. != null)) | .[0] // empty
-    ' "$json_file"
-  fi
-}
-
-evaluate_sla_from_json() {
-  local json_file=$1
-  local ttft
-  local tpot
-  local pass
-
-  [[ -f "$json_file" ]] || return 2
-
-  ttft=$(extract_metric_ms ttft "$json_file")
-  tpot=$(extract_metric_ms tpot "$json_file")
-
-  [[ -n "$ttft" && -n "$tpot" ]] || return 2
-
-  pass=$(jq -n \
-    --argjson ttft "$ttft" \
-    --argjson tpot "$tpot" \
-    --argjson sla_ttft "$SLA_TTFT_MS" \
-    --argjson sla_tpot "$SLA_TPOT_MS" \
-    '($ttft <= $sla_ttft) and ($tpot <= $sla_tpot)')
-
-  [[ "$pass" == "true" ]]
-}
-
-write_adaptive_summary_json() {
-  local summary_file=$1
-  local test_name=$2
-  local qps=$3
-  local static_last_pass=$4
-  local static_first_fail=$5
-  local final_last_pass=$6
-  local final_first_fail=$7
-
-  jq -n \
-    --arg test_name "$test_name" \
-    --arg qps "$qps" \
-    --argjson sla_ttft "$SLA_TTFT_MS" \
-    --argjson sla_tpot "$SLA_TPOT_MS" \
-    --arg static_last_pass "${static_last_pass:-}" \
-    --arg static_first_fail "${static_first_fail:-}" \
-    --arg final_last_pass "${final_last_pass:-}" \
-    --arg final_first_fail "${final_first_fail:-}" \
-    '{
-      test_name: $test_name,
-      qps: $qps,
-      sla_ttft_ms: $sla_ttft,
-      sla_tpot_ms: $sla_tpot,
-      static_last_pass: (if $static_last_pass == "" then null else ($static_last_pass | tonumber) end),
-      static_first_fail: (if $static_first_fail == "" then null else ($static_first_fail | tonumber) end),
-      final_last_pass: (if $final_last_pass == "" then null else ($final_last_pass | tonumber) end),
-      final_first_fail: (if $final_first_fail == "" then null else ($final_first_fail | tonumber) end)
-    }' > "$summary_file"
-}
-
-run_single_serving_probe() {
-  local test_name=$1
-  local qps=$2
-  local max_concurrency=$3
-  local tp=$4
-  local compilation_config_mode=$5
-  local optimization_level=$6
-  local client_args_effective=$7
-  local client_remote_args=$8
-  local server_command=$9
-
-  local new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
-  local result_json
-  local num_prompts_arg=""
-  local client_command
-
-  result_json=$(result_json_path_for_serving "$test_name" "$qps" "$max_concurrency")
-
-  if [[ -f "$result_json" ]]; then
-    evaluate_sla_from_json "$result_json"
-    return $?
-  fi
-
-  if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
-    num_prompts=$(( max_concurrency * PROMPTS_PER_CONCURRENCY ))
-    if (( num_prompts < MIN_NUM_PROMPTS )); then num_prompts=$MIN_NUM_PROMPTS; fi
-    if (( num_prompts > MAX_NUM_PROMPTS )); then num_prompts=$MAX_NUM_PROMPTS; fi
-    num_prompts_arg="--num-prompts $num_prompts"
-  fi
-
-  client_command="vllm bench serve \
-    --save-result \
-    --result-dir $RESULTS_FOLDER \
-    --result-filename ${new_test_name}.json \
-    --request-rate $qps \
-    --max-concurrency $max_concurrency \
-    $num_prompts_arg \
-    --metadata tensor_parallel_size=$tp compilation_config.mode=$compilation_config_mode optimization_level=$optimization_level adaptive_search=1 \
-    $client_args_effective $client_remote_args "
-
-  echo "Adaptive probe: $client_command"
-
-  if [[ "${DRY_RUN:-0}" != "1" ]]; then
-    bash -c "$client_command"
-  fi
-
-  jq_output=$(jq -n \
-    --arg server "$server_command" \
-    --arg client "$client_command" \
-    --arg gpu "$gpu_type" \
-    '{
-      server_command: $server,
-      client_command: $client,
-      gpu_type: $gpu,
-      adaptive_search: true
-    }')
-  echo "$jq_output" > "$RESULTS_FOLDER/${new_test_name}.commands"
-
-  evaluate_sla_from_json "$result_json"
-}
-
-adaptive_refine_from_static_results() {
-  local test_name=$1
-  local qps=$2
-  local max_concurrency_list_raw=$3
-  local tp=$4
-  local compilation_config_mode=$5
-  local optimization_level=$6
-  local client_args_effective=$7
-  local client_remote_args=$8
-  local server_command=$9
-
-  local sorted_points
-  local point
-  local rc
-  local static_last_pass=""
-  local static_first_fail=""
-  local largest_static=""
-  local step_hint=1
-  local previous_point=""
-  local low
-  local high
-  local mid
-  local probes=0
-  local summary_file="$RESULTS_FOLDER/${test_name}_qps_${qps}_sla_summary.json"
-
-  [[ "${ENABLE_ADAPTIVE_CONCURRENCY}" == "1" ]] || return 0
-  [[ "${DRY_RUN:-0}" != "1" ]] || return 0
-
-  sorted_points=$(for point in $max_concurrency_list_raw; do printf '%s\n' "$point"; done | tr -d "'" | awk '/^[0-9]+$/' | sort -n | uniq)
-  [[ -n "$sorted_points" ]] || return 0
-
-  while read -r point; do
-    [[ -z "$point" ]] && continue
-    largest_static="$point"
-    evaluate_sla_from_json "$(result_json_path_for_serving "$test_name" "$qps" "$point")"
-    rc=$?
-    if (( rc == 0 )); then
-      static_last_pass="$point"
-    elif (( rc == 1 )); then
-      if [[ -n "$static_last_pass" ]]; then
-        static_first_fail="$point"
-        break
-      fi
-    fi
-
-    if [[ -n "$previous_point" ]]; then
-      step_hint=$(( point - previous_point ))
-      if (( step_hint < 1 )); then step_hint=1; fi
-    fi
-    previous_point="$point"
-  done <<< "$sorted_points"
-
-  if [[ -z "$static_last_pass" ]]; then
-    write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "" "$static_first_fail" "" "$static_first_fail"
-    return 0
-  fi
-
-  if [[ -n "$static_first_fail" ]]; then
-    low=$static_last_pass
-    high=$static_first_fail
-    while (( low + 1 < high )) && (( probes < ADAPTIVE_MAX_PROBES )); do
-      mid=$(( (low + high) / 2 ))
-      probes=$(( probes + 1 ))
-      run_single_serving_probe \
-        "$test_name" "$qps" "$mid" "$tp" \
-        "$compilation_config_mode" "$optimization_level" \
-        "$client_args_effective" "$client_remote_args" "$server_command"
-      rc=$?
-      if (( rc == 0 )); then
-        low=$mid
-      elif (( rc == 1 )); then
-        high=$mid
-      else
-        break
-      fi
-    done
-    write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "$static_last_pass" "$static_first_fail" "$low" "$high"
-    return 0
-  fi
-
-  low=$largest_static
-  high=""
-  while (( probes < ADAPTIVE_MAX_PROBES )); do
-    point=$(( low + step_hint ))
-    if (( point > ADAPTIVE_MAX_CONCURRENCY )); then
-      point=$ADAPTIVE_MAX_CONCURRENCY
-    fi
-    (( point > low )) || break
-    probes=$(( probes + 1 ))
-    run_single_serving_probe \
-      "$test_name" "$qps" "$point" "$tp" \
-      "$compilation_config_mode" "$optimization_level" \
-      "$client_args_effective" "$client_remote_args" "$server_command"
-    rc=$?
-    if (( rc == 0 )); then
-      low=$point
-      (( point == ADAPTIVE_MAX_CONCURRENCY )) && break
-      step_hint=$(( step_hint * 2 ))
-      if (( step_hint < 1 )); then step_hint=1; fi
-    elif (( rc == 1 )); then
-      high=$point
-      break
-    else
-      break
-    fi
-  done
-
-  if [[ -n "$high" ]]; then
-    while (( low + 1 < high )) && (( probes < ADAPTIVE_MAX_PROBES )); do
-      mid=$(( (low + high) / 2 ))
-      probes=$(( probes + 1 ))
-      run_single_serving_probe \
-        "$test_name" "$qps" "$mid" "$tp" \
-        "$compilation_config_mode" "$optimization_level" \
-        "$client_args_effective" "$client_remote_args" "$server_command"
-      rc=$?
-      if (( rc == 0 )); then
-        low=$mid
-      elif (( rc == 1 )); then
-        high=$mid
-      else
-        break
-      fi
-    done
-  fi
-
-  write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "$static_last_pass" "" "$low" "$high"
-}
-
 run_benchmark_tests() {
  # run benchmark tests using `vllm bench <test_type>` command
  # $1: test type (latency or throughput)
@@ -559,16 +252,37 @@ run_benchmark_tests() {
  done
 }

-run_latency_tests() { run_benchmark_tests "latency" "$1"; }
-run_startup_tests() { run_benchmark_tests "startup" "$1"; }
-run_throughput_tests() { run_benchmark_tests "throughput" "$1"; }
+run_latency_tests() {
+  run_benchmark_tests "latency" "$1"
+}

-merge_serving_tests_stream() {
-  # Emit merged serving test objects, optionally filtered by MODEL_FILTER/DTYPE_FILTER in DRY_RUN mode.
-  # This helper does NOT modify JSON; it only filters the stream in dry-run mode.
-  local serving_test_file="$1"
-  # shellcheck disable=SC2016
-  local merged='
+run_startup_tests() {
+  run_benchmark_tests "startup" "$1"
+}
+
+run_throughput_tests() {
+  run_benchmark_tests "throughput" "$1"
+}
+
+run_serving_tests() {
+  # run serving tests using `vllm bench serve` command
+  # $1: a json file specifying serving test cases
+  #
+  # Supported JSON formats:
+  # 1) Plain format: top-level array
+  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #
+  # 2) Default parameters field + plain format tests
+  #    {
+  #      "defaults": { ... },
+  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #    }
+
+  local serving_test_file
+  serving_test_file=$1
+
+  # Iterate over serving tests
+  jq -c '
    if type == "array" then
      # Plain format: test cases array
      .[]
@@ -590,50 +304,7 @@ merge_serving_tests_stream() {
    else
      error("Unsupported serving test file format: must be array or object with .tests")
    end
-  '
-
-  jq -c "$merged" "$serving_test_file" | \
-  if [[ "${DRY_RUN:-0}" == "1" && ( "${MODEL_FILTER}${DTYPE_FILTER}" != "" ) ]]; then
-    jq -c --arg model "$MODEL_FILTER" --arg dtype "$DTYPE_FILTER" '
-      select((($model|length)==0)
-             or ((.server_parameters.model // "") == $model)
-             or ((.client_parameters.model // "") == $model))
-      | select((($dtype|length)==0) or ((.server_parameters.dtype // "") == $dtype))
-    '
-  else
-    cat
-  fi
-}
-
-run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
-  # $1: a json file specifying serving test cases
-  #
-  # Supported JSON formats:
-  # 1) Plain format: top-level array
-  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #
-  # 2) Default parameters field + plain format tests
-  #    {
-  #      "defaults": { ... },
-  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #    }
-
-  local serving_test_file
-  serving_test_file=$1
-
-  # In dry-run mode, if filters are provided but no tests match, fail fast.
-  if [[ "${DRY_RUN:-0}" == "1" && ( "${MODEL_FILTER}${DTYPE_FILTER}" != "" ) ]]; then
-    local count
-    count=$(merge_serving_tests_stream "$serving_test_file" | wc -l | tr -d ' ')
-    if [[ "$count" -eq 0 ]]; then
-      echo "No matching serving tests found in $serving_test_file for model='$MODEL_FILTER' dtype='$DTYPE_FILTER'." >&2
-      return 0
-    fi
-  fi
-
-  # Iterate over serving tests (merged + optional filtered stream)
-  merge_serving_tests_stream "$serving_test_file" | while read -r params; do
+  ' "$serving_test_file" | while read -r params; do
    # get the test name, and append the GPU type back to it.
    test_name=$(echo "$params" | jq -r '.test_name')
    if [[ ! "$test_name" =~ ^serving_ ]]; then
@@ -652,48 +323,10 @@ run_serving_tests() {
    server_envs=$(echo "$params" | jq -r '.server_environment_variables')
    client_params=$(echo "$params" | jq -r '.client_parameters')

-    # vLLM serve CLI: model must be positional (no --model). Convert server_parameters accordingly.
-    server_model=$(echo "$server_params" | jq -r '.model // empty')
-    if [[ -z "$server_model" || "$server_model" == "null" ]]; then
-      echo "Error: serving test '$test_name' is missing server_parameters.model" >&2
-      exit 1
-    fi
-    server_params_no_model=$(echo "$server_params" | jq -c 'del(.model)')
-    server_args=$(json2args "$server_params_no_model")
-
+    server_args=$(json2args "$server_params")
    server_envs=$(json2envs "$server_envs")
    client_args=$(json2args "$client_params")

-    # ------------------------------------------------------------
-    # Option 1: Dynamic num-prompts scaling based on max_concurrency
-    #
-    # If PROMPTS_PER_CONCURRENCY is set, override JSON num_prompts with:
-    #   num_prompts = max_concurrency * PROMPTS_PER_CONCURRENCY
-    #
-    # If PROMPTS_PER_CONCURRENCY is NOT set, keep JSON num_prompts behavior
-    # unchanged (i.e., whatever is in serving-tests-*.json).
-    # ------------------------------------------------------------
-    PROMPTS_PER_CONCURRENCY="${PROMPTS_PER_CONCURRENCY-}"  # no default on purpose
-    MIN_NUM_PROMPTS="${MIN_NUM_PROMPTS:-1}"
-    MAX_NUM_PROMPTS="${MAX_NUM_PROMPTS:-1000000}"
-
-    if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
-      # Remove any fixed --num-prompts from JSON-derived args (avoid duplicates)
-      # Remove any fixed --num-prompts from JSON-derived args (avoid duplicates)
-      # Handles: --num-prompts 123   and   --num-prompts=123
-      client_args_no_np="$(
-        printf ' %s ' "$client_args" \
-        | sed -E \
-          -e 's/[[:space:]]--num-prompts=([^[:space:]]+)([[:space:]]|$)/ /g' \
-          -e 's/[[:space:]]--num-prompts[[:space:]]+([^[:space:]]+)([[:space:]]|$)/ /g'
-      )"
-      # normalize whitespace
-      client_args_no_np="$(echo "$client_args_no_np" | tr -s ' ' | sed -E 's/^ //; s/ $//')"
-      client_args_no_np="$(echo "$client_args_no_np" | xargs)"
-      client_args_effective="$client_args_no_np"
-    else
-      client_args_effective="$client_args"
-    fi
    # qps_list
    qps_list=$(echo "$params" | jq -r '.qps_list')
    qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
@@ -725,13 +358,14 @@ run_serving_tests() {
    fi

    # check if server model and client model is aligned
+    server_model=$(echo "$server_params" | jq -r '.model')
    client_model=$(echo "$client_params" | jq -r '.model')
    if [[ $server_model != "$client_model" ]]; then
      echo "Server model and client model must be the same. Skip testcase $test_name."
      continue
    fi

-    server_command="$server_envs vllm serve $server_model \
+    server_command="$server_envs vllm serve \
      $server_args"

    # run the server
@@ -739,7 +373,7 @@ run_serving_tests() {
    echo "Server command: $server_command"
    # support remote vllm server
    client_remote_args=""
-    if [[ -z "${REMOTE_HOST}" && "${DRY_RUN:-0}" != "1" ]]; then
+    if [[ -z "${REMOTE_HOST}" ]]; then
      bash -c "$server_command" &
      server_pid=$!
      # wait until the server is alive
@@ -750,9 +384,6 @@ run_serving_tests() {
        echo ""
        echo "vLLM failed to start within the timeout period."
      fi
-    elif [[ "${DRY_RUN:-0}" == "1" ]]; then
-        # dry-run: don't start server
-        echo "Dry Run."
    else
      server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
      if [[ ${REMOTE_PORT} ]]; then
@@ -771,21 +402,15 @@ run_serving_tests() {
    for qps in $qps_list; do
      # remove the surrounding single quote from qps
      if [[ "$qps" == *"inf"* ]]; then
+        echo "qps was $qps"
        qps="inf"
+        echo "now qps is $qps"
      fi

      # iterate over different max_concurrency
      for max_concurrency in $max_concurrency_list; do
-        new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
+        new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
        echo " new test name $new_test_name"
-        # If PROMPTS_PER_CONCURRENCY is set, compute per-concurrency --num-prompts.
-        num_prompts_arg=""
-        if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
-          num_prompts=$(( max_concurrency * PROMPTS_PER_CONCURRENCY ))
-          if (( num_prompts < MIN_NUM_PROMPTS )); then num_prompts=$MIN_NUM_PROMPTS; fi
-          if (( num_prompts > MAX_NUM_PROMPTS )); then num_prompts=$MAX_NUM_PROMPTS; fi
-          num_prompts_arg="--num-prompts $num_prompts"
-        fi
        # pass the tensor parallel size, the compilation mode, and the optimization
        # level to the client so that they can be used on the benchmark dashboard
        client_command="vllm bench serve \
@@ -794,16 +419,13 @@ run_serving_tests() {
          --result-filename ${new_test_name}.json \
          --request-rate $qps \
          --max-concurrency $max_concurrency \
-          $num_prompts_arg \
          --metadata tensor_parallel_size=$tp compilation_config.mode=$compilation_config_mode optimization_level=$optimization_level \
-          $client_args_effective $client_remote_args "
+          $client_args $client_remote_args "

        echo "Running test case $test_name with qps $qps"
        echo "Client command: $client_command"

-        if [[ "${DRY_RUN:-0}" != "1" ]]; then
-          bash -c "$client_command"
-        fi
+        bash -c "$client_command"

        # record the benchmarking commands
        jq_output=$(jq -n \
@@ -818,23 +440,15 @@ run_serving_tests() {
        echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"

      done
-
-      adaptive_refine_from_static_results \
-        "$test_name" "$qps" "$max_concurrency_list" "$tp" \
-        "$compilation_config_mode" "$optimization_level" \
-        "$client_args_effective" "$client_remote_args" "$server_command"
    done

    # clean up
-    if [[ "${DRY_RUN:-0}" != "1" ]]; then
-      kill -9 "$server_pid"
-      kill_gpu_processes
-    fi
+    kill -9 $server_pid
+    kill_gpu_processes
  done
 }

 main() {
-
  local ARCH
  ARCH=''
  if [[ "$ON_CPU" == "1" ]]; then
@@ -844,13 +458,7 @@ main() {
     check_gpus
     ARCH="$arch_suffix"
  fi
-
-  # DRY_RUN does not execute vLLM; do not require HF_TOKEN.
-  if [[ "${DRY_RUN:-0}" != "1" ]]; then
-    check_hf_token
-  else
-    echo "DRY_RUN=1 -> skip HF_TOKEN validation"
-  fi
+  check_hf_token

  # dependencies
  (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
@@ -871,16 +479,11 @@ main() {

  # dump vllm info via vllm collect-env
  env_output=$(vllm collect-env)
+
  echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"

  # benchmarking
-  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}" || exit $?
-
-  if [[ "${DRY_RUN:-0}" == "1" ]]; then
-    echo "DRY_RUN=1 -> skip latency/startup/throughput suites"
-    exit 0
-  fi
-
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
  run_startup_tests $QUICK_BENCHMARK_ROOT/tests/"${STARTUP_JSON:-startup-tests$ARCH.json}"
  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
@@ -888,7 +491,6 @@ main() {
  # postprocess benchmarking results
  pip install tabulate pandas
  python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
-  python3 $QUICK_BENCHMARK_ROOT/scripts/compare-json-results.py -f $RESULTS_FOLDER/benchmark_results.json

  upload_to_buildkite
 }
@@ -2,6 +2,7 @@
    {
        "test_name": "latency_llama8B_tp1",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
            "VLLM_CPU_KVCACHE_SPACE": 40
@@ -2,6 +2,7 @@
    {
        "test_name": "latency_llama8B_tp2",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
 	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
 	    "VLLM_CPU_SGL_KERNEL": 1,
@@ -51,56 +51,5 @@
            "max-model-len": 256,
            "async-scheduling": ""
        }
-    },
-    {
-        "test_name": "latency_deepseek_r1",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "tensor_parallel_size": 8,
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "dtype": "bfloat16"
-        }
-    },
-    {
-        "test_name": "latency_llama4_maverick_17b128e_instruct_fp8",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "tensor_parallel_size": 8,
-            "max-model-len": 512,
-            "max-num-seqs": 128,
-            "async-scheduling": "",
-            "gpu-memory-utilization": 0.95,
-            "enable_expert_parallel": ""
-        }
-    },
-    {
-        "test_name": "latency_qwen3_8b",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "Qwen/Qwen3-8B",
-            "tensor_parallel_size": 1,
-            "max-model-len": 2048,
-            "max-num-seqs": 128,
-            "dtype": "bfloat16",
-            "async-scheduling": ""
-        }
    }
 ]
@@ -13,6 +13,7 @@
      200
    ],
    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
      "VLLM_CPU_SGL_KERNEL": 1,
@@ -35,7 +36,6 @@
      "model": "meta-llama/Llama-3.1-8B-Instruct",
      "backend": "vllm",
      "ignore-eos": "",
-      "temperature": 0,
      "num_prompts": 200
    }
  },
@@ -127,4 +127,4 @@
      }
    }
  ]
-}
+}
@@ -1,37 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-    "server_environment_variables": {
-      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120
-    },
-    "server_parameters": {
-      "dtype": "bfloat16",
-      "model": "openai/whisper-large-v3-turbo"
-    },
-    "client_parameters": {
-      "model": "openai/whisper-large-v3-turbo",
-      "backend": "openai-audio",
-      "endpoint": "/v1/audio/transcriptions",
-      "dataset_name": "hf",
-      "dataset_path": "openslr/librispeech_asr",
-      "hf_subset": "clean",
-      "hf_split": "test",
-      "no_stream": "",
-      "no_oversample": "",
-      "temperature": 0,
-      "num_prompts": 200
-    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_whisper_large_v3_turbo_librispeech_clean_tp1",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {}
-    }
-  ]
-}
@@ -1,40 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [
-      32,
-      64,
-      128
-    ],
-    "server_environment_variables": {
-      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-      "VLLM_CPU_SGL_KERNEL": 1,
-      "VLLM_CPU_KVCACHE_SPACE": 40
-    },
-    "server_parameters": {
-      "dtype": "bfloat16",
-      "model": "jinaai/jina-embeddings-v3",
-      "trust_remote_code": ""
-    },
-    "client_parameters": {
-      "model": "jinaai/jina-embeddings-v3",
-      "backend": "openai-embeddings",
-      "endpoint": "/v1/embeddings",
-      "dataset_name": "sharegpt",
-      "dataset_path": "ShareGPT_V3_unfiltered_cleaned_split.json",
-      "num_prompts": 200
-    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_jina_embed_v3_tp1_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {}
-    }
-  ]
-}
@@ -1,288 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-    "server_environment_variables": {
-      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-      "VLLM_CPU_SGL_KERNEL": 1,
-      "VLLM_CPU_KVCACHE_SPACE": 40
-    },
-    "server_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "tensor_parallel_size": 1,
-      "dtype": "bfloat16",
-      "distributed_executor_backend": "mp",
-      "block_size": 128,
-      "trust_remote_code": "",
-      "disable_log_stats": "",
-      "max_num_batched_tokens": 2048,
-      "max_num_seqs": 256
-    },
-    "client_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "backend": "vllm",
-      "ignore-eos": "",
-      "temperature": 0,
-      "num_prompts": 200
-    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_llama8B_tp1_random_128_128",
-      "server_parameters": {
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int4_tp1_random_128_128",
-      "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
-      },
-      "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int8_tp1_random_128_128",
-      "server_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
-      },
-      "client_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama1B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-1B"
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-1B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama3B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct"
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama70B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct"
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_granite2B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct"
-      },
-      "client_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen1.7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen4B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-4B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen8B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-8B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen14B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-14B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-14B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen30B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-30B-A3B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-30B-A3B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_glm9B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "zai-org/glm-4-9b-hf"
-      },
-      "client_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "google/gemma-7b"
-      },
-      "client_parameters": {
-        "model": "google/gemma-7b",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma3-4b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-3-4b-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-3-4b-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma3-12b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-3-12b-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-3-12b-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-4b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-4-E4B-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-E4B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-2b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-4-E2B-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-E2B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-26b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0,
-        "VLLM_CPU_ATTN_SPLIT_KV": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-4-26B-A4B-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-26B-A4B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_phi4_tp1_random_128_128",
-      "server_parameters": {
-        "model": "microsoft/Phi-4-reasoning"
-      },
-      "client_parameters": {
-        "model": "microsoft/Phi-4-reasoning",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    }
-  ]
-}
@@ -5,6 +5,7 @@
    ],
    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
      "VLLM_CPU_SGL_KERNEL": 1,
@@ -25,7 +26,6 @@
      "model": "meta-llama/Llama-3.1-8B-Instruct",
      "backend": "vllm",
      "ignore-eos": "",
-      "temperature": 0,
      "num_prompts": 200
    }
  },
@@ -72,6 +72,17 @@
        "random-output-len": 128
      }
    },
+    {
+      "test_name": "serving_llama8B_tp4_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
    {
      "test_name": "serving_llama8B_tp1_random_128_2048",
      "server_parameters": {
@@ -94,6 +105,17 @@
        "random-output-len": 2048
      }
    },
+    {
+      "test_name": "serving_llama8B_tp4_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
    {
      "test_name": "serving_llama8B_tp1_random_2048_128",
      "server_parameters": {
@@ -117,25 +139,144 @@
      }
    },
    {
-      "test_name": "serving_llama8B_tp1_random_2048_2048",
+      "test_name": "serving_llama8B_tp4_random_2048_128",
      "server_parameters": {
-        "tensor_parallel_size": 1
+        "tensor_parallel_size": 4
      },
      "client_parameters": {
        "dataset_name": "random",
        "random-input-len": 2048,
-        "random-output-len": 2048
+        "random-output-len": 128
      }
    },
    {
-      "test_name": "serving_llama8B_tp2_random_2048_2048",
+      "test_name": "serving_llama8B_int4_tp1_random_128_128",
      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp2_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
        "tensor_parallel_size": 2
      },
      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 2048
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp4_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama3B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_granite2B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen1.7B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-1.7B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-1.7B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen4B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-4B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-4B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen8B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-8B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-8B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_glm9B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "zai-org/glm-4-9b-hf",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "zai-org/glm-4-9b-hf",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_gemma7B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "google/gemma-7b",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "google/gemma-7b",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
      }
    }
  ]
@@ -10,6 +10,7 @@
        "server_parameters": {
            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
            "tensor_parallel_size": 1,
+            "swap_space": 16,
            "disable_log_stats": "",
            "load_format": "dummy",
            "max-model-len": 2048,
@@ -21,7 +22,6 @@
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "temperature": 0,
            "num_prompts": 200
        }
    },
@@ -37,6 +37,7 @@
        "server_parameters": {
            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
            "tensor_parallel_size": 4,
+            "swap_space": 16,
            "disable_log_stats": "",
            "load_format": "dummy",
            "max-model-len": 2048,
@@ -48,7 +49,6 @@
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "temperature": 0,
            "num_prompts": 200
        }
    },
@@ -64,6 +64,7 @@
        "server_parameters": {
            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
            "tensor_parallel_size": 2,
+            "swap_space": 16,
            "disable_log_stats": "",
            "load_format": "dummy",
            "max-model-len": 2048,
@@ -75,88 +76,6 @@
            "backend": "vllm",
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "temperature": 0,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_deepseek_r1",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "tensor_parallel_size": 8,
-            "disable_log_stats": "",
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "max-num-seqs": 200,
-            "async-scheduling": "",
-            "dtype": "bfloat16"
-        },
-        "client_parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "temperature": 0,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama4_maverick_17b128e_instruct_fp8",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "tensor_parallel_size": 8,
-            "disable_log_stats": "",
-            "max-model-len": 2048,
-            "max-num-seqs": 128,
-            "async-scheduling": "",
-            "enable_expert_parallel": "",
-            "max-num-batched-tokens": 4096
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "temperature": 0,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_qwen3_8b",
-        "qps_list": [1, 4, 10, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "Qwen/Qwen-3-8B",
-            "tensor_parallel_size": 1,
-            "dtype": "bfloat16",
-            "disable_log_stats": "",
-            "async-scheduling": ""
-        },
-        "client_parameters": {
-            "model": "Qwen/Qwen-3-8B",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "temperature": 0,
            "num_prompts": 200
        }
    }
@@ -1,112 +1,77 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-    "server_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "tensor_parallel_size": 1,
-      "dtype": "bfloat16"
+[
+    {
+        "test_name": "serving_llama8B_tp1_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
    },
-    "client_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "backend": "vllm",
-      "ignore-eos": "",
-      "temperature": 0,
-      "num_prompts": 200
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "tensor_parallel_size": 4,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_mixtral8x7B_tp2_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "tensor_parallel_size": 2,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt_specdecode",
+        "qps_list": [2],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct", 
+            "tensor_parallel_size": 4,
+            "swap_space": 16,
+            "speculative_config": {
+                "model": "turboderp/Qwama-0.5B-Instruct",
+                "num_speculative_tokens": 4,
+                "draft_tensor_parallel_size": 1
+            }
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200 
+        }
    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_llama8B_tp1_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_2048_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama70B_tp4_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct",
-        "async_scheduling": "",
-	"no_enable_prefix_caching": "",
-        "max_num_batched_tokens": 8192
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-e4b_tp1_random_128_128",
-      "server_parameters": {
-        "model": "google/gemma-4-E4B-it",
-	"enable_auto_tool_choice": "",
-	"tool_call_parser": "gemma4",
-	"chat_template": "examples/tool_chat_template_gemma4.jinja",
-	"reasoning_parser": "gemma4"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-E4B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    }
-  ]
-}
+]
@@ -2,6 +2,7 @@
    {
        "test_name": "throughput_llama8B_tp1",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
            "VLLM_CPU_KVCACHE_SPACE": 40
@@ -2,6 +2,7 @@
    {
        "test_name": "throughput_llama8B_tp2",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
 	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
 	    "VLLM_CPU_SGL_KERNEL": 1,
@@ -57,67 +57,5 @@
            "max-num-seqs": 512,
            "async-scheduling": ""
        }
-    },
-    {
-        "test_name": "throughput_deepseek_r1",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "tensor_parallel_size": 8,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "dataset_name": "sharegpt",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 384,
-            "async-scheduling": ""
-        }
-    },
-    {
-        "test_name": "throughput_llama4_maverick_17b128e_instruct_fp8",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "tensor_parallel_size": 8,
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "dataset_name": "sharegpt",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 512,
-            "async-scheduling": "",
-            "enable_expert_parallel": ""
-        }
-    },
-    {
-        "test_name": "throughput_qwen3_8b",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "Qwen/Qwen-3-8B",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "dataset_name": "sharegpt",
-            "num_prompts": 1000,
-            "max-num-seqs": 512,
-            "backend": "vllm",
-            "async-scheduling": ""
-        }
    }
 ]
@@ -1,9 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Append a build artifact line to the Buildkite annotation.
-# Usage: annotate-build-artifact.sh <label> <value>
-set -e
-echo "- **${1}**: \`${2}\`" | \
-  buildkite-agent annotate --append --style 'info' --context 'release-artifacts'
@@ -0,0 +1,117 @@
+#!/bin/bash
+
+set -ex
+
+# Get release version, default to 1.0.0.dev for nightly/per-commit builds
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version 2>/dev/null | sed 's/^v//')
+if [ -z "${RELEASE_VERSION}" ]; then
+  RELEASE_VERSION="1.0.0.dev"
+fi
+
+buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
+To download the wheel (by commit):
+\`\`\`
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_aarch64.whl .
+
+(Optional) For CUDA 13.0:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_aarch64.whl .
+
+(Optional) For CPU:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
+\`\`\`
+
+
+To download and upload the image:
+
+\`\`\`
+# Download images:
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+# Tag and push images:
+
+## CUDA
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai:latest-x86_64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130 vllm/vllm-openai:x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:latest-x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+docker push vllm/vllm-openai:latest-x86_64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker push vllm/vllm-openai:latest-aarch64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130 vllm/vllm-openai:aarch64-cu130
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker push vllm/vllm-openai:latest-aarch64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+
+## ROCm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+## CPU
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai-cpu:latest-x86_64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker push vllm/vllm-openai-cpu:latest-arm64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+# Create multi-arch manifest:
+
+docker manifest rm vllm/vllm-openai:latest
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest push vllm/vllm-openai:latest
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+docker manifest rm vllm/vllm-openai:latest-cu130
+docker manifest create vllm/vllm-openai:latest-cu130 vllm/vllm-openai:latest-x86_64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker manifest push vllm/vllm-openai:latest-cu130
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu130
+
+docker manifest rm vllm/vllm-openai-cpu:latest || true
+docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker manifest push vllm/vllm-openai-cpu:latest
+docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
+\`\`\`
+EOF
@@ -5,28 +5,27 @@
 # Generate Buildkite annotation for ROCm wheel release
 set -ex

-# Extract build configuration from Dockerfile.rocm_base (single source of truth)
+# Get build configuration from meta-data
 # Extract ROCm version dynamically from Dockerfile.rocm_base
 # BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.0-complete -> extracts "7.0"
 ROCM_VERSION=$(grep -E '^ARG BASE_IMAGE=' docker/Dockerfile.rocm_base | sed -E 's/.*:([0-9]+\.[0-9]+).*/\1/' || echo "unknown")
-PYTHON_VERSION=$(grep '^ARG PYTHON_VERSION=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTHON_VERSION=//')
-PYTORCH_ROCM_ARCH=$(grep '^ARG PYTORCH_ROCM_ARCH=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTORCH_ROCM_ARCH=//')
+PYTHON_VERSION=$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo "3.12")
+PYTORCH_ROCM_ARCH=$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")

+# TODO: Enable the nightly build for ROCm
 # Get release version, default to 1.0.0.dev for nightly/per-commit builds
 RELEASE_VERSION=$(buildkite-agent meta-data get release-version 2>/dev/null || echo "")
 if [ -z "${RELEASE_VERSION}" ]; then
  RELEASE_VERSION="1.0.0.dev"
 fi

-ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-
 # S3 URLs
 S3_BUCKET="${S3_BUCKET:-vllm-wheels}"
 S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
 S3_URL="http://${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com"

 # Format ROCm version for path (e.g., "7.1" -> "rocm710")
-ROCM_VERSION_PATH="rocm$(echo "${ROCM_VERSION}" | tr -d '.')"
+ROCM_VERSION_PATH="rocm$(echo ${ROCM_VERSION} | tr -d '.')"
 ROCM_PATH="rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}"
 buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' << EOF
 ## ROCm Wheel and Docker Image Releases
@@ -69,7 +68,7 @@ aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchvision-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchaudio-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amdsmi-*.whl .
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amd_aiter-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/aiter-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-attn-*.whl .
 \`\`\`

@@ -81,7 +80,7 @@ aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-
 - **torchvision**: TorchVision for ROCm PyTorch
 - **torchaudio**: Torchaudio for ROCm PyTorch
 - **amdsmi**: AMD SMI Python bindings
- **amd_aiter**: Aiter for ROCm
+- **aiter**: Aiter for ROCm
 - **flash-attn**: Flash Attention for ROCm

 ### :warning: Notes
@@ -97,7 +96,7 @@ To download and upload the image:
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm

-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
 docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
 docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
 docker push vllm/vllm-openai-rocm:latest-base
@@ -15,6 +15,8 @@
 #
 # Environment variables:
 #   S3_BUCKET          - S3 bucket name (default: vllm-wheels)
+#   PYTHON_VERSION     - Python version (affects cache key)
+#   PYTORCH_ROCM_ARCH  - GPU architectures (affects cache key)
 #
 # Note: ROCm version is determined by BASE_IMAGE in Dockerfile.rocm_base,
 #       so changes to ROCm version are captured by the Dockerfile hash.
@@ -34,7 +36,13 @@ generate_cache_key() {
    fi
    local dockerfile_hash=$(sha256sum "$DOCKERFILE" | cut -c1-16)

-    echo "${dockerfile_hash}"
+    # Include key build args that affect the output
+    # These should match the ARGs in Dockerfile.rocm_base that change the build output
+    # Note: ROCm version is determined by BASE_IMAGE in the Dockerfile, so it's captured by dockerfile_hash
+    local args_string="${PYTHON_VERSION:-}|${PYTORCH_ROCM_ARCH:-}"
+    local args_hash=$(echo "$args_string" | sha256sum | cut -c1-8)
+
+    echo "${dockerfile_hash}-${args_hash}"
 }

 CACHE_KEY=$(generate_cache_key)
@@ -44,6 +52,9 @@ case "${1:-}" in
    check)
        echo "Checking cache for key: ${CACHE_KEY}" >&2
        echo "Cache path: ${CACHE_PATH}" >&2
+        echo "Variables used in cache key:" >&2
+        echo "  PYTHON_VERSION: ${PYTHON_VERSION:-<not set>}" >&2
+        echo "  PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH:-<not set>}" >&2

        # Check if cache exists by listing objects
        # We look for at least one .whl file
@@ -72,7 +83,7 @@ case "${1:-}" in
            exit 1
        fi

-        WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
+        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
        if [[ "$WHEEL_COUNT" -eq 0 ]]; then
            echo "ERROR: No wheels found in artifacts/rocm-base-wheels/" >&2
            exit 1
@@ -93,17 +104,15 @@ case "${1:-}" in
        echo "Cache key: ${CACHE_KEY}"
        echo "Cache path: ${CACHE_PATH}"
        echo ""
+
        mkdir -p artifacts/rocm-base-wheels
-        
-        # Use sync with include/exclude to only download .whl files
-        aws s3 sync "${CACHE_PATH}" artifacts/rocm-base-wheels/ \
-            --exclude "*" \
-            --include "*.whl"
-        
+        aws s3 cp --recursive "${CACHE_PATH}" artifacts/rocm-base-wheels/
+
        echo ""
        echo "Downloaded wheels:"
-        find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' -exec ls -lh {} \;
-        WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
+        ls -lh artifacts/rocm-base-wheels/
+
+        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
        echo ""
        echo "Total: $WHEEL_COUNT wheels"
        echo "========================================"
@@ -1,235 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Check if Ray LLM can generate lock files that are compatible with this
-# version of vllm. Downloads Ray's requirement files and runs a full
-# dependency resolution with the installed vllm's constraints to see if
-# a valid lock file can be produced.
-#
-# See: https://github.com/vllm-project/vllm/issues/33599
-
-set -eo pipefail
-
-RAY_BASE_URL="https://raw.githubusercontent.com/ray-project/ray/master/python"
-
-WORK_DIR=$(mktemp -d)
-trap 'rm -rf "$WORK_DIR"' EXIT
-
-# ── Detect PyTorch index URL ─────────────────────────────────────────────
-
-if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
-    ROCM_VER=$(python3 -c "import torch; print(torch.version.hip.rsplit('.', 1)[0])")
-    CANDIDATE_URL="https://download.pytorch.org/whl/rocm${ROCM_VER}"
-    if curl -fsSL --head "${CANDIDATE_URL}/" >/dev/null 2>&1; then
-        TORCH_INDEX_URL="${CANDIDATE_URL}"
-    else
-        echo ">>> WARNING: ROCm ${ROCM_VER} wheel index not found at ${CANDIDATE_URL}"
-        echo ">>>          Falling back to default PyPI (resolution may be incomplete)"
-        TORCH_INDEX_URL=""
-    fi
-else
-    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
-fi
-echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
-
-# Fetch all Ray requirement files used in the LLM depset pipeline
-echo ">>> Fetching Ray requirement files"
-RAY_FILES=(
-    "requirements.txt"
-    "requirements/cloud-requirements.txt"
-    "requirements/base-test-requirements.txt"
-    "requirements/llm/llm-requirements.txt"
-    "requirements/llm/llm-test-requirements.txt"
-)
-for FILE in "${RAY_FILES[@]}"; do
-    LOCAL_PATH="${WORK_DIR}/$(basename "$FILE")"
-    echo "    ${FILE}"
-    curl -fsSL -o "$LOCAL_PATH" "${RAY_BASE_URL}/${FILE}"
-done
-
-# Extract installed vllm deps
-echo ">>> Extracting installed vllm dependency constraints"
-python3 - "${WORK_DIR}/vllm-constraints.txt" <<'PYEOF'
-"""Write out the installed vllm's dependencies as pip constraint lines.
-
-Ray uses vllm[audio], so audio-extra deps are included with their extra
-markers stripped. The resolver cannot evaluate extra markers for a
-package that is not itself being resolved from an index, so we activate
-them manually here.
-"""
-import importlib.metadata
-import re
-import sys
-
-out_path = sys.argv[1]
-raw_reqs = importlib.metadata.requires("vllm") or []
-
-# Ray uses vllm[audio] – activate that extra.
-ACTIVE_EXTRAS = {"audio"}
-EXTRA_RE = re.compile(r"""extra\s*==\s*['"]([^'"]+)['"]""")
-
-lines = []
-for r in raw_reqs:
-    if ";" not in r:
-        # Unconditional dep — always include.
-        lines.append(r.strip())
-        continue
-
-    req_part, _, marker_part = r.partition(";")
-    marker_part = marker_part.strip()
-
-    extra_matches = EXTRA_RE.findall(marker_part)
-    if not extra_matches:
-        # Non-extra marker (python_version, etc.) — keep as-is.
-        lines.append(r.strip())
-        continue
-
-    if not ACTIVE_EXTRAS.intersection(extra_matches):
-        continue  # Skip inactive extras (tensorizer, bench, …).
-
-    # Strip the extra== conditions but keep any remaining markers
-    # (e.g. python_version).
-    cleaned = EXTRA_RE.sub("", marker_part)
-    cleaned = re.sub(r"\band\b\s*\band\b", "and", cleaned)
-    cleaned = re.sub(r"^\s*and\s+|\s+and\s*$", "", cleaned).strip()
-
-    if cleaned:
-        lines.append(f"{req_part.strip()} ; {cleaned}")
-    else:
-        lines.append(req_part.strip())
-
-with open(out_path, "w") as f:
-    for line in lines:
-        f.write(line + "\n")
-
-print(f"Wrote {len(lines)} constraints to {out_path}")
-PYEOF
-
-echo ">>> Installed vllm deps (first 20 lines):"
-head -20 "${WORK_DIR}/vllm-constraints.txt"
-
-# Remove Ray's vllm pin — the installed vllm's transitive deps
-# (written above) replace it in the resolution. vllm itself cannot
-# be resolved from PyPI for in-development versions, so we test
-# whether Ray's requirements can coexist with vllm's dependency
-# constraints instead.
-sed -i '/^vllm/d' "${WORK_DIR}/llm-requirements.txt"
-
-# Install uv if needed
-if ! command -v uv &>/dev/null; then
-    echo ">>> Installing uv"
-    pip install uv -q
-fi
-
-# Resolve: given vllm's constraints, can Ray compile a lock file?
-#
-# vllm's dependency constraints are the fixed side — Ray is flexible and
-# can regenerate its lock files. We pass vllm's constraints via -c so
-# the resolver treats them as non-negotiable bounds, then check whether
-# Ray's own requirements can still be satisfied within those bounds.
-echo ""
-echo "============================================================"
-echo ">>> Resolving: Can Ray generate compatible lock files?"
-echo "============================================================"
-
-EXTRA_INDEX_ARGS=()
-if [[ -n "${TORCH_INDEX_URL}" ]]; then
-    EXTRA_INDEX_ARGS+=(--extra-index-url "${TORCH_INDEX_URL}")
-fi
-
-set +e
-uv pip compile \
-    "${WORK_DIR}/requirements.txt" \
-    "${WORK_DIR}/cloud-requirements.txt" \
-    "${WORK_DIR}/base-test-requirements.txt" \
-    "${WORK_DIR}/llm-requirements.txt" \
-    "${WORK_DIR}/llm-test-requirements.txt" \
-    -c "${WORK_DIR}/vllm-constraints.txt" \
-    --python-version 3.12 \
-    --python-platform x86_64-manylinux_2_31 \
-    "${EXTRA_INDEX_ARGS[@]}" \
-    --index-strategy unsafe-best-match \
-    --unsafe-package setuptools \
-    --unsafe-package ray \
-    --no-header \
-    -o "${WORK_DIR}/resolved.txt" \
-    2>&1
-EXIT_CODE=$?
-set -e
-
-echo ""
-echo "=========================================="
-if [ $EXIT_CODE -eq 0 ]; then
-    echo "SUCCESS: Ray can generate lock files compatible with this vllm."
-    echo ""
-    echo "Key resolved versions:"
-    grep -E '^(protobuf|torch|numpy|transformers)==' \
-        "${WORK_DIR}/resolved.txt" | sort || true
-    echo "=========================================="
-    exit 0
-fi
-
-echo "FAILURE: Ray cannot generate lock files compatible with this vllm."
-echo "This means a fundamental dependency conflict exists that Ray"
-echo "cannot resolve by regenerating its lock files."
-echo "See: https://github.com/vllm-project/vllm/issues/33599"
-echo "=========================================="
-
-# Buildkite annotation
-if [ -f /usr/bin/buildkite-agent ]; then
-    buildkite-agent annotate --style 'warning' --context 'ray-compat' << EOF
-### :warning: Ray Dependency Compatibility Warning
-This PR introduces dependencies that **cannot** be resolved with Ray's requirements.
-Ray would not be able to regenerate its lock files to accommodate this vllm version.
-
-Please check the **Ray Dependency Compatibility Check** step logs for details.
-See [issue #33599](https://github.com/vllm-project/vllm/issues/33599) for context.
-EOF
-fi
-
-# Notify Slack if webhook is configured and PR/branch are valid.
-if [ -n "$RAY_COMPAT_SLACK_WEBHOOK_URL" ]; then
-    PR="${BUILDKITE_PULL_REQUEST:-}"
-    BRANCH="${BUILDKITE_BRANCH:-}"
-
-    # Skip notification if PR is invalid or branch is empty
-    if [[ "$PR" = "false" || -z "$PR" || -z "$BRANCH" ]]; then
-        echo ">>> Skipping Slack notification (invalid PR or empty branch: PR=$PR, branch=$BRANCH)"
-    else
-        echo ">>> Sending Slack notification"
-        # Single quotes are intentional: the f-string expressions are Python, not shell.
-        # shellcheck disable=SC2016
-        PAYLOAD=$(python3 -c '
-import json, os, sys
-pr = os.getenv("BUILDKITE_PULL_REQUEST", "N/A")
-branch = os.getenv("BUILDKITE_BRANCH", "unknown")
-url = os.getenv("BUILDKITE_BUILD_URL", "#")
-data = {
-    "text": ":warning: Ray Dependency Compatibility Check Failed",
-    "blocks": [{
-        "type": "section",
-        "text": {
-            "type": "mrkdwn",
-            "text": (
-                "*:warning: Ray Dependency Compatibility Check Failed*\n"
-                f"PR #{pr} on branch `{branch}` introduces dependencies "
-                f"that cannot be resolved with Ray'\''s requirements.\n"
-                f"<{url}|View Build>"
-            ),
-        },
-    }],
-}
-print(json.dumps(data))
-')
-
-        HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -X POST "$RAY_COMPAT_SLACK_WEBHOOK_URL" \
-            -H 'Content-type: application/json' \
-            -d "$PAYLOAD")
-        echo "    Slack webhook response: $HTTP_CODE"
-    fi
-else
-    echo ">>> Skipping Slack notification (RAY_COMPAT_SLACK_WEBHOOK_URL not set)"
-fi
-
-exit 1
@@ -134,7 +134,7 @@ log_info "Fetching merged PRs from milestone '${MILESTONE}'..."

 # Store PR data in a temp file
 PR_DATA=$(mktemp)
-trap 'rm -f "$PR_DATA"' EXIT
+trap "rm -f $PR_DATA" EXIT

 if ! gh pr list --state merged --search "milestone:${MILESTONE}" \
    --limit 1000 \
@@ -13,8 +13,5 @@ INPUT_FILE="$1"
 # Strip timestamps
 sed -i 's/^\[[0-9]\{4\}-[0-9]\{2\}-[0-9]\{2\}T[0-9]\{2\}:[0-9]\{2\}:[0-9]\{2\}Z\] //' "$INPUT_FILE"

-# Strip Buildkite inline timestamp markers (ESC _bk;t=<ms> BEL)
-sed -i 's/\x1B_bk;t=[0-9]*\x07//g' "$INPUT_FILE"
-
 # Strip colorization
 sed -i -r 's/\x1B\[[0-9;]*[mK]//g' "$INPUT_FILE"
@@ -1,178 +0,0 @@
-#!/bin/bash
-# Fetch vLLM Buildkite CI logs (public; no login required).
-#
-# Usage:
-#   ci-fetch-log.sh [--soft|--all] --pr [<PR>]  failed jobs in the PR's latest
-#                                               build (current branch if omitted)
-#   ci-fetch-log.sh [--soft|--all] <build_url>  failed jobs in that build
-#   ci-fetch-log.sh <job_url> [output]          one job; both #<job_uuid> and
-#                                               ?sid=<id> URL forms work
-#   ci-fetch-log.sh <build> <job_uuid> [output]
-#
-# --soft also fetches soft-failed jobs; --all fetches every finished job.
-# Saves each log as ci-<build>-<job-name>.log (ANSI/timestamps stripped) and
-# prints "<file>\t<job name>" per job. [output] is single-job only; "-"
-# streams to stdout. Existing files are kept; CI_FETCH_LOG_FORCE=1 refetches.
-
-set -euo pipefail
-
-ORG="vllm"
-PIPELINE="ci"
-UA="vllm-ci-fetch-log"
-UUID_RE='[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}'
-
-usage() {
-    sed -n '2,15p' "$0" | sed 's/^# \{0,1\}//'
-    exit 1
-}
-
-die() {
-    echo "$1" >&2
-    exit 1
-}
-
-BUILD="" JOB="" SID="" OUT=""
-SCOPE="failed"
-
-while :; do
-    case "${1:-}" in
-    --soft) SCOPE="soft" ;;
-    --all) SCOPE="all" ;;
-    *) break ;;
-    esac
-    shift
-done
-
-case "${1:-}" in
--pr)
-    PR="${2:-}"
-    # gh pr checks exits non-zero when checks are failing; that is the
-    # expected case here.
-    URL=$(gh pr checks ${PR:+"$PR"} --repo vllm-project/vllm 2>/dev/null |
-        grep -oE "https://buildkite.com/${ORG}/${PIPELINE}/builds/[0-9]+" |
-        sort -t/ -k7 -n | tail -1 || true)
-    [ -n "$URL" ] || die "No Buildkite build found via: gh pr checks ${PR:-<current branch>}"
-    BUILD="${URL##*/}"
-    ;;
-https://*)
-    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
-    JOB=$(echo "$1" | grep -oE "#${UUID_RE}" | head -n 1 | cut -c2- || true)
-    SID=$(echo "$1" | grep -oE "[?&]sid=${UUID_RE}" | head -n 1 | sed 's/.*sid=//' || true)
-    OUT="${2:-}"
-    [ -n "$BUILD" ] || die "Could not parse build number from: $1"
-    ;;
-[0-9]*)
-    [ $# -ge 2 ] || usage
-    BUILD="$1"
-    JOB="$2"
-    OUT="${3:-}"
-    ;;
-*)
-    usage
-    ;;
-esac
-
-COOKIES=$(mktemp)
-JOBS_TSV=$(mktemp)
-trap 'rm -f "$COOKIES" "$JOBS_TSV"' EXIT
-
-# Buildkite issues a session cookie on first hit; later requests need it.
-curl -fsSL -c "$COOKIES" -A "$UA" \
-    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
-
-# The build's job list (id, step uuid, state, name) is served as JSON from
-# the user-facing /data/jobs endpoint. Flatten it to TSV for easy filtering:
-#   job_id  step_uuid  failed  soft_failed  finished  slug  name
-curl -fsSL -b "$COOKIES" -A "$UA" \
-    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}/data/jobs" |
-    python3 -c '
-import json, re, sys
-
-data = json.load(sys.stdin)
-if data.get("has_next_page"):
-    print("warning: job list is paginated; some jobs not shown", file=sys.stderr)
-for r in data["records"]:
-    if r.get("type") != "script":
-        continue
-    name = (r.get("name") or "").replace("\t", " ").replace("\n", " ")
-    slug = re.sub(r"[^a-z0-9]+", "-", name.lower()).strip("-")[:60]
-    print("\t".join([
-        r["id"],
-        r.get("step_uuid") or "",
-        str(r.get("passed") is False),
-        str(bool(r.get("soft_failed"))),
-        str(bool(r.get("finished_at"))),
-        slug,
-        name,
-    ]))
-' >"$JOBS_TSV" || die "Could not list jobs for build ${BUILD}"
-
-if [ -n "$SID" ] && [ -z "$JOB" ]; then
-    # The ?sid= in builds/<N>/list URLs is the *step* uuid, not the job uuid.
-    JOB=$(awk -F'\t' -v s="$SID" '$1 == s || $2 == s {print $1; exit}' "$JOBS_TSV")
-    [ -n "$JOB" ] || die "No job matching sid=${SID} in build ${BUILD}"
-fi
-
-fetch_job() { # <job_uuid> <output_file>
-    curl -fsSL -b "$COOKIES" -A "$UA" \
-        "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/$1/download" \
-        -o "$2"
-    bash "$(dirname "$0")/ci-clean-log.sh" "$2"
-}
-
-if [ -n "$JOB" ]; then
-    # Single-job mode.
-    NAME=$(awk -F'\t' -v j="$JOB" '$1 == j {print $7; exit}' "$JOBS_TSV")
-    SLUG=$(awk -F'\t' -v j="$JOB" '$1 == j {print $6; exit}' "$JOBS_TSV")
-    [ -n "$OUT" ] || OUT="ci-${BUILD}-${SLUG:-${JOB:0:13}}.log"
-    if [ "$OUT" = "-" ]; then
-        TMP=$(mktemp)
-        fetch_job "$JOB" "$TMP"
-        cat "$TMP"
-        rm -f "$TMP"
-        exit 0
-    fi
-    if [ -e "$OUT" ] && [ -z "${CI_FETCH_LOG_FORCE:-}" ]; then
-        die "Refusing to overwrite existing ${OUT} (set CI_FETCH_LOG_FORCE=1 or pass an output path)."
-    fi
-    fetch_job "$JOB" "$OUT"
-    printf '%s\t%s\n' "$OUT" "${NAME:-$JOB}"
-    exit 0
-fi
-
-# Build-wide mode: fetch finished jobs matching $SCOPE.
-[ -z "$OUT" ] || die "[output_file] is only valid when fetching a single job."
-
-case "$SCOPE" in
-failed) FILTER='$3 == "True" && $4 == "False" && $5 == "True"' ;;
-soft) FILTER='$3 == "True" && $5 == "True"' ;;
-all) FILTER='$5 == "True"' ;;
-esac
-
-if [ "$SCOPE" = "failed" ]; then
-    SOFT=$(awk -F'\t' '$3 == "True" && $4 == "True"' "$JOBS_TSV" | wc -l)
-    [ "$SOFT" -eq 0 ] || echo "Skipping ${SOFT} soft-failed job(s); use --soft to include them." >&2
-fi
-
-FOUND=0
-EMITTED=" "
-while IFS=$'\t' read -r job_id _ _ _ _ slug name; do
-    FOUND=$((FOUND + 1))
-    out="ci-${BUILD}-${slug:-${job_id:0:13}}.log"
-    # Retries share a name with the original job; disambiguate by uuid.
-    case "$EMITTED" in
-    *" $out "*) out="ci-${BUILD}-${slug:-job}-${job_id:0:13}.log" ;;
-    esac
-    EMITTED="${EMITTED}${out} "
-    if [ -e "$out" ] && [ -z "${CI_FETCH_LOG_FORCE:-}" ]; then
-        echo "Keeping existing ${out} (set CI_FETCH_LOG_FORCE=1 to refetch)." >&2
-    elif ! fetch_job "$job_id" "$out"; then
-        echo "Failed to download log for job ${job_id} (${name})." >&2
-        continue
-    fi
-    printf '%s\t%s\n' "$out" "$name"
-done < <(awk -F'\t' "$FILTER" "$JOBS_TSV")
-
-if [ "$FOUND" -eq 0 ]; then
-    echo "No matching jobs in build ${BUILD} (scope: ${SCOPE})." >&2
-fi
@@ -4,19 +4,16 @@ set -ex

 # Clean up old nightly builds from DockerHub, keeping only the last 14 builds
 # This script uses DockerHub API to list and delete old tags with specified prefix
-# Usage: cleanup-nightly-builds.sh [TAG_PREFIX] [REPO]
-# Example: cleanup-nightly-builds.sh "nightly-"
-# Example: cleanup-nightly-builds.sh "cu130-nightly-"
-# Example: cleanup-nightly-builds.sh "nightly-" "vllm/vllm-openai-rocm"
+# Usage: cleanup-nightly-builds.sh [TAG_PREFIX]
+# Example: cleanup-nightly-builds.sh "nightly-" or cleanup-nightly-builds.sh "cu130-nightly-"

-# Get tag prefix and repo from arguments
+# Get tag prefix from argument, default to "nightly-" if not provided
 TAG_PREFIX="${1:-nightly-}"
-REPO="${2:-vllm/vllm-openai}"

-echo "Cleaning up tags with prefix: $TAG_PREFIX in repository: $REPO"
+echo "Cleaning up tags with prefix: $TAG_PREFIX"

-# DockerHub API endpoint for the repository
-REPO_API_URL="https://hub.docker.com/v2/repositories/${REPO}/tags"
+# DockerHub API endpoint for vllm/vllm-openai repository
+REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"

 # Get DockerHub credentials from environment
 if [ -z "$DOCKERHUB_TOKEN" ]; then
@@ -73,7 +70,7 @@ delete_tag() {
    local tag_name="$1"
    echo "Deleting tag: $tag_name"
    
-    local delete_url="https://hub.docker.com/v2/repositories/${REPO}/tags/$tag_name"
+    local delete_url="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags/$tag_name"
    set +x
    local response=$(curl -s -X DELETE -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
    set -x
@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Detect the manylinux platform tag for a wheel and rename it in place.
-
-vLLM's build images produce wheels with the generic ``linux_<arch>`` platform
-tag, which installers like ``pip`` won't accept off PyPI/our index. We need to
-rewrite the platform tag to the appropriate ``manylinux_<major>_<minor>_<arch>``
-before uploading.
-
-Historically the tag was hard-coded per build (``manylinux_2_31`` for the
-Ubuntu 20.04-based image, ``manylinux_2_35`` for the Ubuntu 22.04-based
-images). That is brittle: bumping the base image silently produces wheels
-labelled with the wrong glibc requirement. This script asks ``auditwheel``
-to derive the tag from the symbol versions actually referenced by the
-binaries inside the wheel, so the label tracks reality.
-
-We can't simply call ``auditwheel repair`` -- it tries to graft external
-shared libraries into the wheel and fails on vLLM's CUDA/cuBLAS dependencies.
-Instead we use ``auditwheel.wheel_abi.analyze_wheel_abi`` directly, which is
-the same call that powers ``auditwheel show``, and read off
-``winfo.sym_policy.name``.
-
-Usage:
-    detect-manylinux-tag.py <wheel_path>
-
-The wheel is renamed in place; the new path is printed on stdout. All
-diagnostics go to stderr so callers can capture stdout safely.
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-from auditwheel.error import (
-    AuditwheelError,
-    NonPlatformWheelError,
-    WheelToolsError,
-)
-from auditwheel.wheel_abi import analyze_wheel_abi
-from auditwheel.wheeltools import get_wheel_architecture, get_wheel_libc
-
-
-def detect_platform_tag(wheel_path: Path) -> str:
-    """Return the most precise platform tag the wheel is consistent with.
-
-    Mirrors ``auditwheel show`` but returns ``sym_policy`` rather than
-    ``overall_policy``: we only care about the glibc symbol versions used,
-    not about other policy axes (ISA extensions, blacklist, etc.) that
-    ``overall_policy`` folds in.
-    """
-    fn = wheel_path.name
-
-    try:
-        arch = get_wheel_architecture(fn)
-    except (WheelToolsError, NonPlatformWheelError):
-        # Architecture isn't deducible from the filename; let auditwheel
-        # infer it from the ELF binaries inside the wheel.
-        arch = None
-
-    try:
-        libc = get_wheel_libc(fn)
-    except WheelToolsError:
-        # An unrepaired wheel uses ``linux_<arch>``, which doesn't encode
-        # libc. Let auditwheel infer it from the ELF binaries.
-        libc = None
-
-    winfo = analyze_wheel_abi(
-        libc,
-        arch,
-        wheel_path,
-        frozenset(),
-        disable_isa_ext_check=False,
-        allow_graft=False,
-    )
-    return winfo.sym_policy.name
-
-
-def rename_wheel(wheel_path: Path, new_platform_tag: str) -> Path:
-    """Rename the wheel in place, replacing only its platform tag."""
-    # Wheel filename per PEP 427:
-    #   {distribution}-{version}(-{build})?-{python}-{abi}-{platform}.whl
-    # The platform tag is always the last ``-``-separated token before
-    # ``.whl``. Compound tags like ``manylinux_2_31_x86_64`` use ``_`` as the
-    # internal separator, so ``-``-splitting is unambiguous.
-    parts = wheel_path.stem.split("-")
-    if len(parts) < 5:
-        raise ValueError(f"Unrecognised wheel filename: {wheel_path.name}")
-    parts[-1] = new_platform_tag
-    new_path = wheel_path.with_name("-".join(parts) + ".whl")
-    if new_path != wheel_path:
-        wheel_path.rename(new_path)
-    return new_path
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Detect a wheel's manylinux platform tag with "
-        "auditwheel and rename the wheel in place."
-    )
-    parser.add_argument(
-        "wheel",
-        type=Path,
-        help="Path to the wheel to inspect and rename.",
-    )
-    args = parser.parse_args()
-
-    wheel_path: Path = args.wheel
-    if not wheel_path.is_file():
-        print(f"error: {wheel_path} is not a file", file=sys.stderr)
-        return 1
-
-    # Catch the things that ``analyze_wheel_abi`` and ``rename_wheel`` can
-    # raise: any subclass of ``AuditwheelError`` (pure-Python wheels,
-    # invalid libc, malformed wheels), filesystem errors, or our own
-    # ``ValueError`` for an unrecognised wheel filename. Print a single
-    # ``ERROR_TYPE: message`` line to stderr instead of a Python
-    # traceback, which is much friendlier in CI logs.
-    try:
-        new_tag = detect_platform_tag(wheel_path)
-        print(f"detected platform tag: {new_tag}", file=sys.stderr)
-        new_path = rename_wheel(wheel_path, new_tag)
-    except (AuditwheelError, ValueError, OSError) as e:
-        print(
-            f"error: failed to retag {wheel_path.name}: {type(e).__name__}: {e}",
-            file=sys.stderr,
-        )
-        return 2
-
-    if new_path != wheel_path:
-        print(f"renamed {wheel_path.name} -> {new_path.name}", file=sys.stderr)
-    else:
-        print(f"wheel already tagged {new_tag}", file=sys.stderr)
-
-    print(new_path)
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -1,54 +0,0 @@
-#!/bin/bash
-# Emit docker build flags for release image provenance metadata.
-# Keep this helper best-effort: missing Buildkite metadata should fall back to
-# local/default values instead of blocking the Docker build.
-
-# Variant examples: "", "cu129", "ubuntu2404", "cu129-ubuntu2404".
-variant="${1:-}"
-variant_suffix="${variant:+-${variant}}"
-
-image_name="${VLLM_DOCKER_IMAGE_NAME:-vllm/vllm-openai}"
-staging_repo="${VLLM_STAGING_IMAGE_REPO:-public.ecr.aws/q9t5s3a7/vllm-release-repo}"
-build_commit="${VLLM_BUILD_COMMIT:-${BUILDKITE_COMMIT:-unknown}}"
-build_pipeline="${VLLM_BUILD_PIPELINE:-${BUILDKITE_PIPELINE_ID:-${BUILDKITE_PIPELINE_SLUG:-local}}}"
-build_url="${VLLM_BUILD_URL:-${BUILDKITE_BUILD_URL:-}}"
-tag_commit="${BUILDKITE_COMMIT:-${build_commit}}"
-
-if [[ -n "${BUILDKITE:-}" || -n "${BUILDKITE_COMMIT:-}" ]]; then
-  release_version="${RELEASE_VERSION:-}"
-  if command -v buildkite-agent >/dev/null 2>&1; then
-    release_version="${release_version:-$(buildkite-agent meta-data get release-version 2>/dev/null)}"
-  fi
-  release_version="${release_version#v}"
-  release_version="${release_version:-${tag_commit}}"
-
-  staging_image_ref="${staging_repo}:${tag_commit}-$(uname -m)${variant_suffix}"
-
-  if [[ "${NIGHTLY:-}" == "1" ]]; then
-    if [[ -z "${variant}" ]]; then
-      image_tag="${image_name}:nightly-${tag_commit}"
-    elif [[ "${variant}" == cu* ]]; then
-      cuda_variant="${variant%%-*}"
-      remaining_variant="${variant#${cuda_variant}}"
-      image_tag="${image_name}:${cuda_variant}-nightly-${tag_commit}${remaining_variant}"
-    else
-      image_tag="${image_name}:nightly-${tag_commit}${variant_suffix}"
-    fi
-  else
-    image_tag="${image_name}:v${release_version}${variant_suffix}"
-  fi
-else
-  image_tag="${VLLM_IMAGE_TAG:-local/vllm-openai:dev}"
-  staging_image_ref="${image_tag}"
-fi
-
-emit_arg() {
-  printf -- "--build-arg %s=%s " "$1" "$2"
-}
-
-emit_arg VLLM_BUILD_COMMIT "${build_commit}"
-emit_arg VLLM_BUILD_PIPELINE "${build_pipeline}"
-emit_arg VLLM_BUILD_URL "${build_url}"
-# This is the intended public tag. The final digest is only known after push.
-emit_arg VLLM_IMAGE_TAG "${image_tag}"
-printf -- "--tag %s " "${staging_image_ref}"
@@ -1,77 +0,0 @@
-#!/usr/bin/env bash
-
-set -ex
-
-# Generate and upload wheel indices for all wheels in the commit directory.
-# This script should run once after all wheels have been built and uploaded.
-
-# ======== setup ========
-
-BUCKET="vllm-wheels"
-INDICES_OUTPUT_DIR="indices"
-DEFAULT_VARIANT_ALIAS="cu130" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
-SUBPATH=$BUILDKITE_COMMIT
-S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
-
-# Select python3 (>= 3.12) -- local if available, else a docker fallback.
-# shellcheck source=lib/select-python.sh
-source .buildkite/scripts/lib/select-python.sh
-select_python
-
-# ======== generate and upload indices ========
-
-# list all wheels in the commit directory
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-obj_json="objects.json"
-aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
-mkdir -p "$INDICES_OUTPUT_DIR"
-
-# call script to generate indices for all existing wheels
-# these indices have relative paths that work as long as they are next to the wheel directory in s3
-# i.e., the wheels are always in s3://vllm-wheels/<commit>/
-# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
-alias_args=()
-if [[ -n "$DEFAULT_VARIANT_ALIAS" ]]; then
-    alias_args=(--alias-to-default "$DEFAULT_VARIANT_ALIAS")
-fi
-
-# HACK: we do not need regex module here, but it is required by pre-commit hook
-# To avoid any external dependency, we simply replace it back to the stdlib re module
-sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
-$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" "${alias_args[@]}"
-
-# copy indices to /<commit>/ unconditionally
-echo "Uploading indices to $S3_COMMIT_PREFIX"
-aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
-
-# copy to /nightly/ only if it is on the main branch and not a PR
-if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
-    echo "Uploading indices to overwrite /nightly/"
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
-fi
-
-# detect version from any wheel in the commit directory
-# download the first wheel we find to extract version metadata
-first_wheel_key=$($PYTHON -c "import json; obj=json.load(open('$obj_json')); print(next((c['Key'] for c in obj.get('Contents', []) if c['Key'].endswith('.whl')), ''))")
-if [[ -z "$first_wheel_key" ]]; then
-    echo "Error: No wheels found in $S3_COMMIT_PREFIX"
-    exit 1
-fi
-first_wheel=$(basename "$first_wheel_key")
-aws s3 cp "s3://$BUCKET/${first_wheel_key}" "/tmp/${first_wheel}"
-version=$(unzip -p "/tmp/${first_wheel}" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
-rm -f "/tmp/${first_wheel}"
-echo "Version in wheel: $version"
-pure_version="${version%%+*}"
-echo "Pure version (without variant): $pure_version"
-
-# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
-if [[ "$version" != *"dev"* ]]; then
-    echo "Re-generating indices for /$pure_version/"
-    rm -rf "${INDICES_OUTPUT_DIR:?}"
-    mkdir -p "$INDICES_OUTPUT_DIR"
-    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
-    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" "${alias_args[@]}"
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
-fi
@@ -1,534 +1,237 @@
 #!/bin/bash

-# This script runs tests inside the corresponding ROCm docker container.
-# It handles both single-node and multi-node test configurations.
-#
-# Multi-node detection: Instead of matching on fragile group names, we detect
-# multi-node jobs structurally by looking for the bracket command syntax
-# "[node0_cmds] && [node1_cmds]" or via the NUM_NODES environment variable.
-#
-###############################################################################
-# QUOTING / COMMAND PASSING
-#
-# Passing commands as positional arguments ($*) is fragile when the command
-# string itself contains double quotes, e.g.:
-#
-#   bash run-amd-test.sh "export FLAGS="value" && pytest -m "not slow""
-#
-# The outer shell resolves the nested quotes *before* this script runs, so
-# the script receives mangled input it cannot fully recover.
-#
-# Preferred: pass commands via the VLLM_TEST_COMMANDS environment variable:
-#
-#   export VLLM_TEST_COMMANDS='export FLAGS="value" && pytest -m "not slow"'
-#   bash run-amd-test.sh
-#
-# Single-quoted assignment preserves all inner double quotes verbatim.
-# The $* path is kept for backward compatibility but callers should migrate.
-###############################################################################
+# This script runs test inside the corresponding ROCm docker container.
 set -o pipefail

-# 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.
-export PYTHONPATH="${PYTHONPATH:-..}"
+# Export Python path
+export PYTHONPATH=".."

-###############################################################################
-# Helper Functions
-###############################################################################
-
-report_docker_usage() {
-  echo "--- Docker usage"
-  docker system df || true
-}
-
-cleanup_network() {
-  local max_nodes=${NUM_NODES:-2}
-  for node in $(seq 0 $((max_nodes - 1))); do
-    if docker ps -a -q -f name="node${node}" | grep -q .; then
-      docker stop "node${node}" || true
-    fi
-  done
-  if docker network ls | grep -q docker-net; then
-    docker network rm docker-net || true
-  fi
-}
-
-prepare_artifact_image() {
-  if [[ "${VLLM_CI_USE_ARTIFACTS:-0}" != "1" ]]; then
-    return 1
-  fi
-  if ! command -v buildkite-agent >/dev/null 2>&1; then
-    echo "buildkite-agent not found; cannot download ROCm wheel artifact"
-    return 1
-  fi
-
-  local artifact_glob="${VLLM_CI_ARTIFACT_GLOB:-artifacts/vllm-rocm-install/vllm-rocm-install.tar.gz}"
-  local archive=""
-  local metadata_file=""
-  local base_image="${VLLM_CI_BASE_IMAGE:-rocm/vllm-dev:ci_base}"
-  local artifact_image=""
-  local artifact_key=""
-  local base_digest=""
-  local wheel_dir=""
-  local context_dir=""
-  local workspace_dir=""
-
-  artifact_work_dir=$(mktemp -d -t vllm-rocm-artifact.XXXXXX)
-  wheel_dir="${artifact_work_dir}/wheels"
-  context_dir="${artifact_work_dir}/context"
-  workspace_dir="${context_dir}/workspace"
-  mkdir -p "${wheel_dir}" "${context_dir}/wheels" "${workspace_dir}"
-
-  echo "--- Downloading ROCm wheel artifact"
-  if ! buildkite-agent artifact download "${artifact_glob}" "${artifact_work_dir}"; then
-    echo "Failed to download ${artifact_glob}"
-    return 1
-  fi
-  buildkite-agent artifact download \
-    "artifacts/vllm-rocm-install/ci-base-image.txt" \
-    "${artifact_work_dir}" >/dev/null 2>&1 || true
-
-  archive=$(find "${artifact_work_dir}" -name "vllm-rocm-install.tar.gz" -type f | head -1)
-  if [[ -z "${archive}" || ! -f "${archive}" ]]; then
-    echo "ROCm wheel artifact archive was not found"
-    return 1
-  fi
-
-  metadata_file=$(find "${artifact_work_dir}" -name "ci-base-image.txt" -type f | head -1)
-  if [[ -n "${metadata_file}" && -s "${metadata_file}" ]]; then
-    base_image=$(tr -d '[:space:]' < "${metadata_file}")
-  fi
-
-  echo "--- Preparing local ROCm test image"
-  echo "Base image: ${base_image}"
-  docker pull "${base_image}" || return 1
-  base_digest=$(
-    docker image inspect \
-      --format='{{if .RepoDigests}}{{index .RepoDigests 0}}{{else}}{{.Id}}{{end}}' \
-      "${base_image}" 2>/dev/null || printf '%s' "${base_image}"
-  )
-
-  artifact_key=$(
-    {
-      printf 'base-image:%s\n' "${base_digest}"
-      sha256sum "${archive}"
-    } | sha256sum | cut -c1-24
-  )
-  artifact_image="rocm/vllm-ci-artifact:${artifact_key}"
-
-  if docker image inspect "${artifact_image}" >/dev/null 2>&1; then
-    echo "Using existing local ROCm artifact image: ${artifact_image}"
-    image_name="${artifact_image}"
-    return 0
-  fi
-
-  tar -xzf "${archive}" -C "${wheel_dir}" || return 1
-  if ! ls "${wheel_dir}"/*.whl >/dev/null 2>&1; then
-    echo "ROCm wheel artifact did not contain a wheel"
-    return 1
-  fi
-  if [[ ! -d "${wheel_dir}/tests" ]]; then
-    echo "ROCm wheel artifact did not contain the test workspace"
-    return 1
-  fi
-
-  cp "${wheel_dir}"/*.whl "${context_dir}/wheels/" || return 1
-  tar -C "${wheel_dir}" --exclude='*.whl' -cf - . \
-    | tar -C "${workspace_dir}" -xf - || return 1
-  cat > "${context_dir}/Dockerfile" <<'EOF'
-ARG BASE_IMAGE
-FROM ${BASE_IMAGE}
-COPY wheels/ /tmp/vllm-wheels/
-COPY workspace/ /vllm-workspace/
-RUN python3 -m pip install --no-deps --force-reinstall /tmp/vllm-wheels/*.whl \
-    && rm -rf /tmp/vllm-wheels
-WORKDIR /vllm-workspace
-EOF
-
-  echo "--- Building local ROCm test image"
-  docker build \
-    --pull=false \
-    --build-arg "BASE_IMAGE=${base_image}" \
-    -t "${artifact_image}" \
-    "${context_dir}" || return 1
-  image_name="${artifact_image}"
-  return 0
-}
-
-is_multi_node() {
-  local cmds="$1"
-  # Primary signal: NUM_NODES environment variable set by the pipeline
-  if [[ "${NUM_NODES:-1}" -gt 1 ]]; then
-    return 0
-  fi
-  # Fallback: detect the bracket syntax structurally
-  # Pattern: [...] && [...] (per-node command arrays)
-  if [[ "$cmds" =~ \[.*\].*\&\&.*\[.*\] ]]; then
-    return 0
-  fi
-  return 1
-}
-
-handle_pytest_exit() {
-  local exit_code=$1
-  if [ "$exit_code" -eq 5 ]; then
-    echo "Pytest exit code 5 (no tests collected) - treating as success."
-    exit 0
-  fi
-  exit "$exit_code"
-}
-
-###############################################################################
-# Pytest marker/keyword re-quoting
-#
-# When commands are passed through Buildkite -> shell -> $* -> bash -c,
-# quotes around multi-word pytest -m/-k expressions get stripped:
-#   pytest -v -s -m 'not cpu_test' v1/core
-# becomes:
-#   pytest -v -s -m not cpu_test v1/core
-#
-# pytest then interprets "cpu_test" as a file path, not part of the marker.
-#
-# This function detects unquoted expressions after -m/-k and re-quotes them
-# by collecting tokens until a recognizable boundary is reached:
-#   - test path (contains '/')
-#   - test file (ends with '.py')
-#   - another pytest flag (--xxx or -x single-char flags)
-#   - command separator (&& || ; |)
-#   - environment variable assignment (FOO=bar)
-#
-# Single-word markers (e.g. -m cpu_test, -m hybrid_model) pass through
-# unquoted since they have no spaces and work fine.
-#
-# Already-quoted expressions (containing literal single quotes) are passed
-# through untouched to avoid double-quoting well-formed shell fragments.
-#
-# NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
-# double-quotes stripped by the calling shell (see header comment).
-# Use VLLM_TEST_COMMANDS to avoid the problem entirely.
-###############################################################################
-re_quote_pytest_markers() {
-  local input="$1"
-  local output=""
-  local collecting=false
-  local marker_buf=""
-
-  # Strip backslash-newline continuations, then flatten remaining newlines
-  local flat="${input//$'\\\n'/ }"
-  flat="${flat//$'\n'/ }"
-
-  # Disable globbing to prevent *.py etc. from expanding during read -ra
-  local restore_glob
-  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
-  set -o noglob
-  local -a words
-  read -ra words <<< "$flat"
-  eval "$restore_glob"
-
-  for word in "${words[@]}"; do
-    if $collecting; then
-      # If the token we're about to collect already contains a literal
-      # single quote, the expression was already quoted upstream.
-      # Flush and stop collecting.
-      if [[ "$word" == *"'"* ]]; then
-        if [[ -n "$marker_buf" ]]; then
-          # Should not normally happen (partial buf + quote), flush raw
-          output+="${marker_buf} "
-          marker_buf=""
+# Print ROCm version
+echo "--- Confirming Clean Initial State"
+while true; do
+        sleep 3
+        if grep -q clean /opt/amdgpu/etc/gpu_state; then
+                echo "GPUs state is \"clean\""
+                break
        fi
-        output+="${word} "
-        collecting=false
-        continue
-      fi
+done

-      local is_boundary=false
-      case "$word" in
-        # Line-continuation artifact
-        "\\")
-          is_boundary=true ;;
-        # Command separators
-        "&&"|"||"|";"|"|")
-          is_boundary=true ;;
-        # Long flags (--ignore, --shard-id, etc.)
-        --*)
-          is_boundary=true ;;
-        # Short flags (-v, -s, -x, etc.) but NOT negative marker tokens
-        # like "not" which don't start with "-". Also skip -k/-m which
-        # would start a new marker (handled below).
-        -[a-zA-Z])
-          is_boundary=true ;;
-        # Test path (contains /)
-        */*)
-          is_boundary=true ;;
-        # Test file (ends with .py, possibly with ::method)
-        *.py|*.py::*)
-          is_boundary=true ;;
-        # Environment variable assignment preceding a command (FOO=bar)
-        *=*)
-          # Only treat as boundary if it looks like VAR=value, not
-          # pytest filter expressions like num_gpus=2 inside markers
-          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
-            is_boundary=true
-          fi
-          ;;
-      esac
-
-      if $is_boundary; then
-        # Strip surrounding double quotes if present (from upstream
-        # single-to-double conversion); without this, wrapping below
-        # would produce '"expr"' with literal double-quote characters.
-        if [[ "$marker_buf" == '"'*'"' ]]; then
-          marker_buf="${marker_buf#\"}"
-          marker_buf="${marker_buf%\"}"
-        fi
-        # Flush the collected marker expression
-        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
-          output+="'${marker_buf}' "
-        else
-          output+="${marker_buf} "
-        fi
-        collecting=false
-        marker_buf=""
-        # Check if this boundary word itself starts a new -m/-k
-        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
-          output+="${word} "
-          collecting=true
-        # Drop stray backslash tokens silently
-        elif [[ "$word" == "\\" ]]; then
-          :
-        else
-          output+="${word} "
-        fi
-      else
-        # Accumulate into marker buffer
-        if [[ -n "$marker_buf" ]]; then
-          marker_buf+=" ${word}"
-        else
-          marker_buf="${word}"
-        fi
-      fi
-    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
-      output+="${word} "
-      collecting=true
-      marker_buf=""
-    else
-      output+="${word} "
-    fi
-  done
-
-  # Flush any trailing marker expression (marker at end of command)
-  if $collecting && [[ -n "$marker_buf" ]]; then
-    # Strip surrounding double quotes (see mid-stream flush comment)
-    if [[ "$marker_buf" == '"'*'"' ]]; then
-      marker_buf="${marker_buf#\"}"
-      marker_buf="${marker_buf%\"}"
-    fi
-    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
-      output+="'${marker_buf}'"
-    else
-      output+="${marker_buf}"
-    fi
-  fi
-
-  echo "${output% }"
-}
-
-###############################################################################
-# Main
-###############################################################################
-
-# --- GPU initialization ---
 echo "--- ROCm info"
 rocminfo

-# --- Docker status ---
-report_docker_usage
+# cleanup older docker images
+cleanup_docker() {
+  # Get Docker's root directory
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory."
+    exit 1
+  fi
+  echo "Docker root directory: $docker_root"
+  # Check disk usage of the filesystem where Docker's root directory is located
+  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  # Define the threshold
+  threshold=70
+  if [ "$disk_usage" -gt "$threshold" ]; then
+    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    # Remove dangling images (those that are not tagged and not used by any container)
+    docker image prune -f
+    # Remove unused volumes / force the system prune for old images as well.
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
+    echo "Docker images and volumes cleanup completed."
+  else
+    echo "Disk usage is below $threshold%. No cleanup needed."
+  fi
+}
+
+cleanup_network() {
+  for node in $(seq 0 $((NUM_NODES-1))); do
+    if docker pr -a -q -f name="node${node}" | grep -q .; then
+      docker stop "node${node}"
+    fi
+  done
+  if docker network ls | grep docker-net; then
+    docker network rm docker-net
+  fi
+}
+
+# Call the cleanup docker function
+cleanup_docker
+
+echo "--- Resetting GPUs"
+
+echo "reset" > /opt/amdgpu/etc/gpu_state
+
+while true; do
+        sleep 3
+        if grep -q clean /opt/amdgpu/etc/gpu_state; then
+                echo "GPUs state is \"clean\""
+                break
+        fi
+done

-# --- Pull test image ---
 echo "--- Pulling container"
-image_name="${VLLM_CI_FALLBACK_IMAGE:-rocm/vllm-ci:${BUILDKITE_COMMIT:-local}}"
-artifact_work_dir=""
+image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+docker pull "${image_name}"

 remove_docker_container() {
-  if docker container inspect "${container_name}" >/dev/null 2>&1; then
-    docker rm -f "${container_name}" || true
-  fi
-  if [[ "${VLLM_CI_REMOVE_TEST_IMAGE:-0}" == "1" ]]; then
-    docker image rm -f "${image_name}" || true
-  else
-    # Keep images by default so later jobs on the same AMD node can reuse layers.
-    echo "Keeping ROCm test image locally: ${image_name}"
-  fi
-  if [[ -n "${artifact_work_dir}" ]]; then
-    rm -rf "${artifact_work_dir}"
-  fi
+   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT

-if ! prepare_artifact_image; then
-  echo "Using full ROCm CI image: ${image_name}"
-  docker pull "${image_name}" || exit 1
-fi
-
-# --- Prepare commands ---
 echo "--- Running container"

 HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"

-# Hugging Face Hub defaults to 10s request/download timeouts, while the ROCm
-# CI image currently raises downloads to 60s. AMD model-test jobs routinely
-# start from a cold or partially-populated shared cache, and the 60s read cap
-# has still timed out before pytest reached the vLLM behavior under test.
-# Keep the CI default explicit and overridable from the Buildkite environment.
-: "${HF_HUB_DOWNLOAD_TIMEOUT:=300}"
-: "${HF_HUB_ETAG_TIMEOUT:=60}"
-
-# ---- Command source selection ----
-# Prefer VLLM_TEST_COMMANDS (preserves all inner quoting intact).
-# Fall back to $* for backward compatibility, but warn that inner
-# double-quotes will have been stripped by the calling shell.
-if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
-  commands="${VLLM_TEST_COMMANDS}"
-  commands_source="env"
-  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
-else
-  commands="$*"
-  commands_source="argv"
-  if [[ -z "$commands" ]]; then
-    echo "Error: No test commands provided." >&2
-    echo "Usage:" >&2
-    echo "  Preferred:  VLLM_TEST_COMMANDS='...' bash $0" >&2
-    echo "  Legacy:     bash $0 \"commands here\"" >&2
-    exit 1
-  fi
-  echo "Commands sourced from positional args (legacy mode)"
-  echo "WARNING: Inner double-quotes in the command string may have been"
-  echo "  stripped by the calling shell. If you see syntax errors, switch to:"
-  echo "  export VLLM_TEST_COMMANDS='your commands here'"
-  echo "  bash $0"
-fi
-
+commands=$@
 echo "Raw commands: $commands"

-# Only try to repair stripped pytest -m/-k quoting in legacy argv mode.
-# VLLM_TEST_COMMANDS preserves inner quoting already, and re-quoting that path
-# can corrupt embedded echo strings or otherwise well-formed shell fragments.
-if [[ "$commands_source" == "argv" ]]; then
-  commands=$(re_quote_pytest_markers "$commands")
-  echo "After re-quoting: $commands"
-else
-  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
+commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"pytest -v -s basic_correctness/test_basic_correctness.py"}
+
+if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
+  commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
 fi

+commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"pytest -v -s compile/test_basic_correctness.py"}
+
+if [[ $commands == *"pytest -v -s lora"* ]]; then
+  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
+fi
+
+#ignore certain kernels tests
+if [[ $commands == *" kernels/core"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/core/test_fused_quant_layernorm.py \
+  --ignore=kernels/core/test_permute_cols.py"
+fi
+
+if [[ $commands == *" kernels/attention"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/attention/test_attention_selector.py \
+  --ignore=kernels/attention/test_encoder_decoder_attn.py \
+  --ignore=kernels/attention/test_flash_attn.py \
+  --ignore=kernels/attention/test_flashinfer.py \
+  --ignore=kernels/attention/test_prefix_prefill.py \
+  --ignore=kernels/attention/test_cascade_flash_attn.py \
+  --ignore=kernels/attention/test_mha_attn.py \
+  --ignore=kernels/attention/test_lightning_attn.py \
+  --ignore=kernels/attention/test_attention.py"
+fi
+
+if [[ $commands == *" kernels/quantization"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/quantization/test_int8_quant.py \
+  --ignore=kernels/quantization/test_machete_mm.py \
+  --ignore=kernels/quantization/test_block_fp8.py \
+  --ignore=kernels/quantization/test_block_int8.py \
+  --ignore=kernels/quantization/test_marlin_gemm.py \
+  --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
+  --ignore=kernels/quantization/test_int8_kernel.py"
+fi
+
+if [[ $commands == *" kernels/mamba"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/mamba/test_mamba_mixer2.py \
+  --ignore=kernels/mamba/test_causal_conv1d.py \
+  --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
+fi
+
+if [[ $commands == *" kernels/moe"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/moe/test_moe.py \
+  --ignore=kernels/moe/test_cutlass_moe.py \
+  --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
+fi
+
+#ignore certain Entrypoints/openai tests
+if [[ $commands == *" entrypoints/openai "* ]]; then
+  commands=${commands//" entrypoints/openai "/" entrypoints/openai \
+  --ignore=entrypoints/openai/test_audio.py \
+  --ignore=entrypoints/openai/test_shutdown.py \
+  --ignore=entrypoints/openai/test_completion.py \
+  --ignore=entrypoints/openai/test_models.py \
+  --ignore=entrypoints/openai/test_lora_adapters.py \
+  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
+  --ignore=entrypoints/openai/test_root_path.py \
+  --ignore=entrypoints/openai/test_tokenization.py \
+  --ignore=entrypoints/openai/test_prompt_validation.py "}
+fi
+
+#ignore certain Entrypoints/llm tests
+if [[ $commands == *" entrypoints/llm "* ]]; then
+  commands=${commands//" entrypoints/llm "/" entrypoints/llm \
+  --ignore=entrypoints/llm/test_chat.py \
+  --ignore=entrypoints/llm/test_accuracy.py \
+  --ignore=entrypoints/llm/test_init.py \
+  --ignore=entrypoints/llm/test_prompt_validation.py "}
+fi
+
+commands=$(echo "$commands" | sed 's/ \\ / /g')
 echo "Final commands: $commands"

-MYPYTHONPATH="/vllm-workspace"
+# --ignore=entrypoints/openai/test_encoder_decoder.py \
+# --ignore=entrypoints/openai/test_embedding.py \
+# --ignore=entrypoints/openai/test_oot_registration.py
+# --ignore=entrypoints/openai/test_accuracy.py \
+# --ignore=entrypoints/openai/test_models.py <= Fails on MI250 but passes on MI300 as of 2025-03-13

-container_job_id="${BUILDKITE_JOB_ID:-${BUILDKITE_PARALLEL_JOB:-0}}"
-container_job_id="${container_job_id//[^A-Za-z0-9_.-]/_}"
-container_job_id_short="${container_job_id:0:8}"
-CONTAINER_TMPDIR="/tmp/vllm-${container_job_id_short}"
-CONTAINER_CACHE_ROOT="/tmp/vllm-buildkite-${container_job_id}/cache"
-CONTAINER_PREFLIGHT="mkdir -p \"\$TMPDIR\" \"\$TORCHINDUCTOR_CACHE_DIR\" \"\$TRITON_CACHE_DIR\" \"\$VLLM_CACHE_ROOT\" \"\$XDG_CACHE_HOME\" && python -c \"import encodings, importlib.metadata as im, importlib.util as iu; [im.version(d) for d in ('transformers', 'torch', 'ray', 'sympy', 'markupsafe', 'vllm')]; missing=[m for m in ('torch.utils.model_zoo', 'transformers.models.nomic_bert', 'ray.dag', 'sympy.physics', 'markupsafe._speedups') if iu.find_spec(m) is None]; assert not missing, missing\""

-# Verify GPU access
+MYPYTHONPATH=".."
+
+# Test that we're launching on the machine that has
+# proper access to GPUs
 render_gid=$(getent group render | cut -d: -f3)
 if [[ -z "$render_gid" ]]; then
  echo "Error: 'render' group not found. This is required for GPU access." >&2
  exit 1
 fi

-# --- RDMA device passthrough (conditional) ---
-# If the host has RDMA devices, pass them through so tests like
-# test_moriio_connector can access ibverbs. On hosts without RDMA
-# hardware the tests will gracefully skip via _rdma_available().
-RDMA_FLAGS=""
-if [ -d /dev/infiniband ]; then
-  echo "RDMA devices detected on host, enabling passthrough"
-  RDMA_FLAGS="--device /dev/infiniband --cap-add=IPC_LOCK"
-else
-  echo "No RDMA devices found on host, RDMA tests will be skipped"
-fi
+if [[ $commands == *"VLLM_TEST_GROUP_NAME=mi325_4-2-node-tests-4-gpus-in-total"* ]]; then

-# --- Route: multi-node vs single-node ---
-if is_multi_node "$commands"; then
-  echo "--- Multi-node job detected"
  export DCKR_VER=$(docker --version | sed 's/Docker version \(.*\), build .*/\1/')

-  # Parse the bracket syntax:  prefix ; [node0_cmds] && [node1_cmds]
-  #   BASH_REMATCH[1] = prefix (everything before first bracket)
-  #   BASH_REMATCH[2] = comma-separated node0 commands
-  #   BASH_REMATCH[3] = comma-separated node1 commands
-  if [[ "$commands" =~ ^(.*)\[(.*)"] && ["(.*)\]$ ]]; then
-    prefix=$(echo "${BASH_REMATCH[1]}" | sed 's/;//g')
-    echo "PREFIX: ${prefix}"
-
-    export composite_command="(command rocm-smi || true)"
-    saved_IFS=$IFS
-    IFS=','
-    read -ra node0 <<< "${BASH_REMATCH[2]}"
-    read -ra node1 <<< "${BASH_REMATCH[3]}"
-    IFS=$saved_IFS
-
-    if [[ ${#node0[@]} -ne ${#node1[@]} ]]; then
-      echo "Warning: node0 has ${#node0[@]} commands, node1 has ${#node1[@]}. They will be paired by index."
-    fi
-
-    for i in "${!node0[@]}"; do
-      command_node_0=$(echo "${node0[i]}" | sed 's/\"//g')
-      command_node_1=$(echo "${node1[i]}" | sed 's/\"//g')
-
-      step_cmd="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
-      echo "COMMANDS: ${step_cmd}"
-      composite_command="${composite_command} && ${step_cmd}"
-    done
-
-    /bin/bash -c "${composite_command}"
-    exit_code=$?
-    cleanup_network
-    handle_pytest_exit "$exit_code"
+  if [[ "$commands" =~ ^(.*)"["(.*)"] && ["(.*)"]"$ ]]; then
+      prefix=$( echo "${BASH_REMATCH[1]}" | sed 's/;//g')
+      echo "PREFIX: ${prefix}"
+      export composite_command="(command rocm-smi || true)"
+      myIFS=$IFS
+      IFS=','
+      read -ra node0 <<< ${BASH_REMATCH[2]}
+      read -ra node1 <<< ${BASH_REMATCH[3]}
+      IFS=$myIFS
+      for i in "${!node0[@]}";do 
+        command_node_0=$(echo ${node0[i]} | sed 's/\"//g')
+        command_node_1=$(echo ${node1[i]} | sed 's/\"//g')
+        
+        export commands="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
+        echo "COMMANDS: ${commands}"
+        composite_command=$(echo "${composite_command} && ${commands}")
+      done
+      /bin/bash -c "${composite_command}"
+      cleanup_network
  else
-    echo "Multi-node job detected but failed to parse bracket command syntax."
-    echo "Expected format: prefix ; [node0_cmd1, node0_cmd2] && [node1_cmd1, node1_cmd2]"
-    echo "Got: $commands"
-    cleanup_network
-    exit 111
+      echo "Failed to parse node commands! Exiting."
+      cleanup_network
+      exit 111
  fi
 else
-  echo "--- Single-node job"
  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
-
  docker run \
-    --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-    $RDMA_FLAGS \
-    --network=host \
-    --shm-size=16gb \
-    --group-add "$render_gid" \
-    --rm \
-    -e HF_TOKEN \
-    -e "HF_HUB_DOWNLOAD_TIMEOUT=${HF_HUB_DOWNLOAD_TIMEOUT}" \
-    -e "HF_HUB_ETAG_TIMEOUT=${HF_HUB_ETAG_TIMEOUT}" \
-    -e AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY \
-    -e BUILDKITE_PARALLEL_JOB \
-    -e BUILDKITE_PARALLEL_JOB_COUNT \
-    -v "${HF_CACHE}:${HF_MOUNT}" \
-    -e "HF_HOME=${HF_MOUNT}" \
-    -e "PYTHONPATH=${MYPYTHONPATH}" \
-    -e "TMPDIR=${CONTAINER_TMPDIR}/tmp" \
-    -e "TORCHINDUCTOR_CACHE_DIR=${CONTAINER_CACHE_ROOT}/torchinductor" \
-    -e "TRITON_CACHE_DIR=${CONTAINER_CACHE_ROOT}/triton" \
-    -e "VLLM_CACHE_ROOT=${CONTAINER_CACHE_ROOT}/vllm" \
-    -e "XDG_CACHE_HOME=${CONTAINER_CACHE_ROOT}/xdg" \
-    -e "PYTORCH_ROCM_ARCH=" \
-    --name "${container_name}" \
-    "${image_name}" \
-    /bin/bash -c "${CONTAINER_PREFLIGHT} && ${commands}"
-
-  exit_code=$?
-  handle_pytest_exit "$exit_code"
+          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+          --network=host \
+          --shm-size=16gb \
+          --group-add "$render_gid" \
+          --rm \
+          -e HF_TOKEN \
+          -e AWS_ACCESS_KEY_ID \
+          -e AWS_SECRET_ACCESS_KEY \
+          -v "${HF_CACHE}:${HF_MOUNT}" \
+          -e "HF_HOME=${HF_MOUNT}" \
+          -e "PYTHONPATH=${MYPYTHONPATH}" \
+          --name "${container_name}" \
+          "${image_name}" \
+          /bin/bash -c "${commands}"
 fi
@@ -1,65 +0,0 @@
-#!/bin/bash
-set -euox pipefail
-
-export VLLM_CPU_KVCACHE_SPACE=1 
-export VLLM_CPU_CI_ENV=1
-# Reduce sub-processes for acceleration
-export TORCH_COMPILE_DISABLE=1 
-export VLLM_ENABLE_V1_MULTIPROCESSING=0
-
-SDE_ARCHIVE="sde-external-10.7.0-2026-02-18-lin.tar.xz"
-SDE_CHECKSUM="CA3D4086DE4ACB3FAEDF9F57B541C6936B7D5E19AE2BF763B6EA933573A0A217"
-wget "https://downloadmirror.intel.com/913594/${SDE_ARCHIVE}"
-echo "${SDE_CHECKSUM}  ${SDE_ARCHIVE}" | sha256sum --check
-mkdir -p sde
-tar -xvf "./${SDE_ARCHIVE}" --strip-components=1 -C ./sde/
-
-wait_for_pid_and_check_log() {
-    local pid="$1"
-    local log_file="$2"
-    local exit_status
-
-    if [ -z "$pid" ] || [ -z "$log_file" ]; then
-        echo "Usage: wait_for_pid_and_check_log <PID> <LOG_FILE>"
-        return 1
-    fi
-
-    echo "Waiting for process $pid to finish..."
-    
-    # Use the 'wait' command to pause the script until the specific PID exits.
-    # The 'wait' command's own exit status will be that of the waited-for process.
-    if wait "$pid"; then
-        exit_status=$?
-        echo "Process $pid finished with exit status $exit_status (Success)."
-    else
-        exit_status=$?
-        echo "Process $pid finished with exit status $exit_status (Failure)."
-    fi
-
-    if [ "$exit_status" -ne 0 ]; then
-        echo "Process exited with a non-zero status."
-        echo "--- Last few lines of log file: $log_file ---"
-        tail -n 50 "$log_file"
-        echo "---------------------------------------------"
-        return 1 # Indicate failure based on exit status
-    fi
-
-    echo "No errors detected in log file and process exited successfully."
-    return 0
-}
-
-# Test Sky Lake (AVX512F)
-./sde/sde64 -skl -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_0.log 2>&1 &
-PID_TEST_0=$!
-
-# Test Cascade Lake (AVX512F + VNNI)
-./sde/sde64 -clx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_1.log 2>&1 &
-PID_TEST_1=$!
-
-# Test Cooper Lake (AVX512F + VNNI + BF16)
-./sde/sde64 -cpx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_2.log 2>&1 &
-PID_TEST_2=$!
-
-wait_for_pid_and_check_log $PID_TEST_0 test_0.log
-wait_for_pid_and_check_log $PID_TEST_1 test_1.log
-wait_for_pid_and_check_log $PID_TEST_2 test_2.log
@@ -1,39 +1,26 @@
 #!/bin/bash
 set -euox pipefail
-export VLLM_CPU_CI_ENV=0
-export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM

-MODE=${1:-all}
+echo "--- PP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
+vllm bench serve \
+    --backend vllm \
+    --dataset-name random \
+    --model meta-llama/Llama-3.2-3B-Instruct \
+    --num-prompts 20 \
+    --endpoint /v1/completions
+kill -s SIGTERM $server_pid &

-run_scenario() {
-    local label="$1" result_file="$2"
-    shift 2
-    echo "--- $label"
-    vllm serve meta-llama/Llama-3.2-3B-Instruct "$@" --max-model-len=4096 &
-    local server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
-    vllm bench serve \
-        --backend vllm \
-        --dataset-name random \
-        --model meta-llama/Llama-3.2-3B-Instruct \
-        --num-prompts 20 \
-        --result-dir ./test_results \
-        --result-filename "$result_file" \
-        --save-result \
-        --endpoint /v1/completions
-    kill -s SIGTERM "$server_pid"; wait "$server_pid" || true
-    if [ "$(jq '.failed' "./test_results/$result_file")" -ne 0 ]; then
-        echo "Some requests were failed in $label!"
-        exit 1
-    fi
-}
-
-case "$MODE" in
-    tp_pp) run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2 ;;
-    dp_tp) run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2 ;;
-    all)
-        run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2
-        run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2
-        ;;
-    *) echo "ERROR: unknown mode '$MODE' (expected: tp_pp | dp_tp | all)" >&2; exit 1 ;;
-esac
+echo "--- DP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
+vllm bench serve \
+    --backend vllm \
+    --dataset-name random \
+    --model meta-llama/Llama-3.2-3B-Instruct \
+    --num-prompts 20 \
+    --endpoint /v1/completions
+kill -s SIGTERM $server_pid &
@@ -5,8 +5,8 @@
 set -ex

 # allow to bind to different cores
-CORE_RANGE=${CORE_RANGE:-0-31}
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-31}
+CORE_RANGE=${CORE_RANGE:-0-16}
+OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-16}

 export CMAKE_BUILD_PARALLEL_LEVEL=16

@@ -31,37 +31,22 @@ function cpu_tests() {
    set -e
    pip list"

-  # Run kernel tests
-  docker exec cpu-test bash -c "
-    set -e
-    pytest -x -v -s tests/kernels/test_onednn.py
-    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/core/test_cpu_activation.py
-    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic
-    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
-  if [ -z "$HF_TOKEN" ]; then
-    echo "Warning: HF_TOKEN is not set. Skipping tests that require model downloads."
-    return
-  fi
-
  # offline inference
  docker exec cpu-test bash -c "
    set -e
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m"
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"

  # Run model tests
  docker exec cpu-test bash -c "
    set -e
    pytest -x -v -s tests/models/multimodal/generation/test_whisper.py -m cpu_model"

-  # Run quantized model tests
+  # Run kernel tests
  docker exec cpu-test bash -c "
    set -e
-    pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
-
+    pytest -x -v -s tests/kernels/test_onednn.py
+    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"

  # basic online serving
  docker exec cpu-test bash -c '
@@ -76,21 +61,6 @@ function cpu_tests() {
      --num-prompts 20 \
      --endpoint /v1/completions
    kill -s SIGTERM $server_pid &'
-
-  # smoke test for Gated DeltaNet
-  docker exec cpu-test bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS vllm serve Qwen/Qwen3.5-0.8B --max-model-len 2048 &
-    server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model Qwen/Qwen3.5-0.8B \
-      --num-prompts 20 \
-      --endpoint /v1/completions
-    kill -s SIGTERM $server_pid &'
-
 }

 # All of CPU tests are expected to be finished less than 40 mins.
@@ -27,7 +27,7 @@ function cpu_tests() {
  podman exec -it "$container_id" bash -c "
    export TORCH_COMPILE_DISABLE=1
    set -xve
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m" >> "$HOME"/test_basic.log
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> $HOME/test_basic.log

  # Run basic model test
  podman exec -it "$container_id" bash -c "
@@ -43,7 +43,7 @@ function cpu_tests() {
    pytest -v -s tests/models/language/generation/test_common.py::test_models[False-False-5-32-google/gemma-1.1-2b-it]
    pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
    # TODO: Below test case tests/models/language/pooling/test_embedding.py::test_models[True-ssmits/Qwen2-7B-Instruct-embed-base] fails on ppc64le. Disabling it for time being.
-    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> "$HOME"/test_rest.log
+    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> $HOME/test_rest.log
 }

 # All of CPU tests are expected to be finished less than 40 mins.
@@ -16,5 +16,5 @@ echo "--- :docker: Building Docker image"
 docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .

 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 -e VLLM_CPU_ATTN_SPLIT_KV=0 --shm-size=4g "$IMAGE_NAME" \
-        timeout "$TIMEOUT_VAL" bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"
+docker run --rm --cpuset-cpus=$CORE_RANGE --cpuset-mems=$NUMA_NODE -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g $IMAGE_NAME \
+        timeout $TIMEOUT_VAL bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"
@@ -25,5 +25,5 @@ remove_docker_container

 # Run the image and test offline inference
 docker run -e HF_TOKEN -e VLLM_WORKER_MULTIPROC_METHOD=spawn -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
-    python3 examples/basic/offline_inference/generate.py --model meta-llama/Llama-3.2-1B
+    python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
 '
@@ -1,48 +1,23 @@
 #!/bin/bash

-# This script builds the HPU docker image and runs the offline inference inside the container.
+# This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
-#
-# vllm-gaudi compatibility pinning:
-#   The vllm-gaudi plugin is installed on top of the vllm upstream checkout used by this CI job.
-#   When upstream vllm changes its API, the plugin may break before it has been updated.
-#   To handle this, the vllm-gaudi repository maintains a file:
-#     vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT
-#   The first line of that file controls what version of vllm is used inside the Docker image:
-#     - "latest"        : no checkout override; the current Buildkite CI commit is used as-is.
-#     - "<commit SHA>"  : vllm is checked out to that specific commit before building, pinning
-#                         the test to a known-compatible baseline.
-#   To unpin (resume testing against the live vllm tip), set the file content back to "latest".
 set -exuo pipefail

-# Fetch the vllm community commit reference from vllm-gaudi (first line only).
-VLLM_COMMUNITY_COMMIT=$(curl -s \
-  https://raw.githubusercontent.com/vllm-project/vllm-gaudi/vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT \
-  | head -1 | tr -d '\n')
-
-echo "Using vllm community commit: ${VLLM_COMMUNITY_COMMIT}"
-
 # Try building the docker image
 image_name="hpu/upstream-vllm-ci:${BUILDKITE_COMMIT}"
 container_name="hpu-upstream-vllm-ci-${BUILDKITE_COMMIT}-container"
-cat <<EOF | docker build -t "${image_name}" -f - .
+cat <<EOF | docker build -t ${image_name} -f - .
 FROM gaudi-base-image:latest

 COPY ./ /workspace/vllm

-# If VLLM_COMMUNITY_COMMIT is a specific commit (not "latest"), check it out to pin vllm
-# to the version known to be compatible with vllm-gaudi. When the value is "latest",
-# the current checkout (the Buildkite CI commit) is used unchanged.
-RUN if [ "${VLLM_COMMUNITY_COMMIT}" != "latest" ]; then \
-      cd /workspace/vllm && git fetch --unshallow 2>/dev/null || true && git checkout ${VLLM_COMMUNITY_COMMIT}; \
-    fi
-
 WORKDIR /workspace/vllm

 ENV no_proxy=localhost,127.0.0.1
 ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true

-RUN bash -c 'pip install -r <(sed "/^torch/d" requirements/build/cuda.txt)'
+RUN bash -c 'pip install -r <(sed "/^torch/d" requirements/build.txt)'
 RUN VLLM_TARGET_DEVICE=empty pip install --no-build-isolation -e .
 RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git

@@ -64,19 +39,19 @@ EOF
 # functions, while other platforms only need one remove_docker_container
 # function.
 EXITCODE=1
-remove_docker_containers() { docker rm -f "${container_name}" || true; }
+remove_docker_containers() { docker rm -f ${container_name} || true; }
 trap 'remove_docker_containers; exit $EXITCODE;' EXIT
 remove_docker_containers

 echo "Running HPU plugin v1 test"
-docker run --rm --runtime=habana --name="${container_name}" --network=host \
+docker run --rm --runtime=habana --name=${container_name} --network=host \
  -e HABANA_VISIBLE_DEVICES=all \
  -e VLLM_SKIP_WARMUP=true \
  -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
  -e PT_HPU_LAZY_MODE=1 \
  "${image_name}" \
  /bin/bash -c '
-  cd vllm; timeout 120s python -u examples/basic/offline_inference/generate.py --model facebook/opt-125m
+  cd vllm; timeout 120s python -u examples/offline_inference/basic/generate.py --model facebook/opt-125m
 '

 EXITCODE=$?
@@ -1,51 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-test_suite="${1:-}"
-
-if [[ -z "${test_suite}" ]]; then
-  echo "Usage: $0 <example|v1|server>" >&2
-  exit 1
-fi
-
-case "${test_suite}" in
-  example)
-    pip install tblib==3.1.0
-
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
-    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --kv-cache-dtype fp8
-    python3 examples/basic/offline_inference/generate.py --model nvidia/Llama-3.1-8B-Instruct-FP8 --block-size 64 --enforce-eager --quantization modelopt --kv-cache-dtype fp8 --attention-backend TRITON_ATTN --max-model-len 4096
-    python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --block-size 64 --enforce-eager --max-model-len 8192
-    python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2
-    python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
-    python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --max-model-len 8192
-    ;;
-  v1)
-    cd tests
-
-    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py
-    pytest -v -s v1/engine --ignore=v1/engine/test_output_processor.py
-    pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py -k "not test_topk_only and not test_topp_only and not test_topk_and_topp"
-    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py
-    pytest -v -s v1/structured_output
-    pytest -v -s v1/test_serial_utils.py
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py --ignore=v1/spec_decode/test_speculators_correctness.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py --ignore=v1/kv_connector/unit/test_offloading_connector.py
-    ;;
-  server)
-    pip install av
-    cd tests
-
-    pytest -v -s entrypoints/multimodal/openai/chat_completion/test_audio_in_video.py
-    pytest -v -s benchmarks/test_serve_cli.py
-    ;;
-  *)
-    echo "Unknown Intel test suite: ${test_suite}" >&2
-    exit 1
-    ;;
-esac
@@ -1,376 +0,0 @@
-#!/bin/bash
-
-# This script runs tests inside the Intel XPU docker container.
-# It mirrors the structure of run-amd-test.sh while keeping Intel-specific
-# container setup and allowing commands to be sourced from YAML or env.
-#
-# Command sources (in priority order):
-#   1) VLLM_TEST_COMMANDS env var (preferred, preserves quoting)
-#   2) Positional args (legacy)
-#   3) One or more YAML files with a commands list (test-area style)
-###############################################################################
-set -o pipefail
-
-DRY_RUN=${DRY_RUN:-0}
-if [[ "${1:-}" == "--dry-run" ]]; then
-  DRY_RUN=1
-  shift
-fi
-
-# Export Python path
-export PYTHONPATH=".."
-
-###############################################################################
-# Helper Functions
-###############################################################################
-
-cleanup_docker() {
-  # Share the same lock with image pull to avoid cleanup/pull races on one node.
-  local docker_lock="/tmp/docker-pull.lock"
-  exec 9>"$docker_lock"
-  flock 9
-
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
-  if [ -z "$docker_root" ]; then
-    echo "Failed to determine Docker root directory." >&2
-    flock -u 9
-    return 1
-  fi
-  echo "Docker root directory: $docker_root"
-
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Running aggressive CI image cleanup..."
-    cleanup_old_ci_images "${REGISTRY}/${REPO}" "${image_name}" "${DOCKER_IMAGE_CLEANUP_HOURS:-72}" 1
-  else
-    echo "Disk usage is below $threshold%. Checking old CI images anyway."
-    cleanup_old_ci_images "${REGISTRY}/${REPO}" "${image_name}" "${DOCKER_IMAGE_CLEANUP_HOURS:-72}" 0
-  fi
-  echo "Old CI image cleanup completed."
-
-  flock -u 9
-}
-
-cleanup_old_ci_images() {
-  local repo_prefix="$1"
-  local current_image_ref="$2"
-  local ttl_hours="$3"
-  local aggressive_cleanup="$4"
-
-  if [[ -z "$repo_prefix" || "$repo_prefix" == "/" ]]; then
-    echo "Skip old-image cleanup: invalid repo prefix '${repo_prefix}'"
-    return 0
-  fi
-
-  if ! [[ "$ttl_hours" =~ ^[0-9]+$ ]]; then
-    echo "Invalid DOCKER_IMAGE_CLEANUP_HOURS='${ttl_hours}', fallback to 72"
-    ttl_hours=72
-  fi
-
-  local now_epoch cutoff_epoch
-  now_epoch=$(date +%s)
-  cutoff_epoch=$((now_epoch - ttl_hours * 3600))
-
-  local -a used_image_ids
-  mapfile -t used_image_ids < <(docker ps -aq | xargs -r docker inspect --format '{{.Image}}' | sort -u)
-
-  local removed_count=0
-  local examined_count=0
-  declare -A seen_ids=()
-
-  while read -r image_ref image_id; do
-    [[ -z "$image_ref" || -z "$image_id" ]] && continue
-    ((examined_count++))
-
-    # Keep the image this job is going to use.
-    if [[ "$image_ref" == "$current_image_ref" ]]; then
-      continue
-    fi
-
-    # Avoid duplicate deletes when multiple tags point to same image id.
-    if [[ -n "${seen_ids[$image_id]:-}" ]]; then
-      continue
-    fi
-    seen_ids[$image_id]=1
-
-    # Never delete images that are used by any container on this node.
-    if printf '%s\n' "${used_image_ids[@]}" | grep -qx "$image_id"; then
-      continue
-    fi
-
-    local created created_epoch
-    created=$(docker image inspect -f '{{.Created}}' "$image_id" 2>/dev/null || true)
-    [[ -z "$created" ]] && continue
-    created_epoch=$(date -d "$created" +%s 2>/dev/null || true)
-    [[ -z "$created_epoch" ]] && continue
-
-    if (( created_epoch < cutoff_epoch )) || [[ "$aggressive_cleanup" == "1" ]]; then
-      if docker image rm -f "$image_id" >/dev/null 2>&1; then
-        ((removed_count++))
-      fi
-    fi
-  done < <(docker image ls --no-trunc "$repo_prefix" --format '{{.Repository}}:{{.Tag}} {{.ID}}')
-
-  # Also trim old dangling layers; this is safe and does not remove referenced images.
-  docker image prune -f --filter "until=${ttl_hours}h" >/dev/null 2>&1 || true
-
-  if [[ "$aggressive_cleanup" == "1" ]]; then
-    echo "Examined ${examined_count} images under ${repo_prefix}, removed ${removed_count} unused images under disk pressure."
-  else
-    echo "Examined ${examined_count} images under ${repo_prefix}, removed ${removed_count} old images (>${ttl_hours}h)."
-  fi
-}
-
-re_quote_pytest_markers() {
-  local input="$1"
-  local output=""
-  local collecting=false
-  local marker_buf=""
-
-  local flat="${input//$'\n'/ }"
-  local restore_glob
-  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
-  set -o noglob
-  local -a words
-  read -ra words <<< "$flat"
-  eval "$restore_glob"
-
-  for word in "${words[@]}"; do
-    if $collecting; then
-      if [[ "$word" == *"'"* ]]; then
-        if [[ -n "$marker_buf" ]]; then
-          output+="${marker_buf} "
-          marker_buf=""
-        fi
-        output+="${word} "
-        collecting=false
-        continue
-      fi
-
-      local is_boundary=false
-      case "$word" in
-        "&&"|"||"|";"|"|")
-          is_boundary=true ;;
-        --*)
-          is_boundary=true ;;
-        -[a-zA-Z])
-          is_boundary=true ;;
-        */*)
-          is_boundary=true ;;
-        *.py|*.py::*)
-          is_boundary=true ;;
-        *=*)
-          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
-            is_boundary=true
-          fi
-          ;;
-      esac
-
-      if $is_boundary; then
-        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
-          output+="'${marker_buf}' "
-        else
-          output+="${marker_buf} "
-        fi
-        collecting=false
-        marker_buf=""
-        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
-          output+="${word} "
-          collecting=true
-        else
-          output+="${word} "
-        fi
-      else
-        if [[ -n "$marker_buf" ]]; then
-          marker_buf+=" ${word}"
-        else
-          marker_buf="${word}"
-        fi
-      fi
-    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
-      output+="${word} "
-      collecting=true
-      marker_buf=""
-    else
-      output+="${word} "
-    fi
-  done
-
-  if $collecting && [[ -n "$marker_buf" ]]; then
-    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
-      output+="'${marker_buf}'"
-    else
-      output+="${marker_buf}"
-    fi
-  fi
-
-  echo "${output% }"
-}
-
-apply_intel_test_overrides() {
-  local cmds="$1"
-  # Placeholder for Intel-specific exclusions/overrides.
-  echo "$cmds"
-}
-
-is_yaml_file() {
-  local p="$1"
-  [[ -f "$p" && "$p" == *.yaml ]]
-}
-
-extract_yaml_commands() {
-  local yaml_path="$1"
-  awk '
-    $1 == "commands:" { in_cmds=1; next }
-    in_cmds && $0 ~ /^[[:space:]]*-[[:space:]]/ {
-      sub(/^[[:space:]]*-[[:space:]]/, "");
-      print;
-      next
-    }
-    in_cmds && $0 ~ /^[^[:space:]]/ { exit }
-  ' "$yaml_path"
-}
-
-###############################################################################
-# Main
-###############################################################################
-
-default_image_name="${REGISTRY}/${REPO}:${BUILDKITE_COMMIT}-xpu"
-#default_image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-xpu"
-image_name="${IMAGE_TAG_XPU:-${default_image_name}}"
-container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-
-# ---- Command source selection ----
-commands=""
-commands_source=""
-if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
-  commands="${VLLM_TEST_COMMANDS}"
-  commands_source="env"
-  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
-elif [[ $# -gt 0 ]]; then
-  all_yaml=true
-  for arg in "$@"; do
-    if ! is_yaml_file "$arg"; then
-      all_yaml=false
-      break
-    fi
-  done
-
-  if $all_yaml; then
-    for yaml in "$@"; do
-      mapfile -t COMMANDS < <(extract_yaml_commands "$yaml")
-      if [[ ${#COMMANDS[@]} -eq 0 ]]; then
-        echo "Error: No commands found in ${yaml}" >&2
-        exit 1
-      fi
-      for cmd in "${COMMANDS[@]}"; do
-        if [[ -z "$commands" ]]; then
-          commands="${cmd}"
-        else
-          commands+=" && ${cmd}"
-        fi
-      done
-    done
-    echo "Commands sourced from YAML files: $*"
-  else
-    commands="$*"
-    echo "Commands sourced from positional args (legacy mode)"
-  fi
-else
-  SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-  DEFAULT_YAML="${SCRIPT_DIR}/intel-test.yaml"
-  if [[ ! -f "${DEFAULT_YAML}" ]]; then
-    echo "Error: YAML file not found: ${DEFAULT_YAML}" >&2
-    exit 1
-  fi
-  mapfile -t COMMANDS < <(extract_yaml_commands "${DEFAULT_YAML}")
-  if [[ ${#COMMANDS[@]} -eq 0 ]]; then
-    echo "Error: No commands found in ${DEFAULT_YAML}" >&2
-    exit 1
-  fi
-  for cmd in "${COMMANDS[@]}"; do
-    if [[ -z "$commands" ]]; then
-      commands="${cmd}"
-    else
-      commands+=" && ${cmd}"
-    fi
-  done
-  echo "Commands sourced from default YAML: ${DEFAULT_YAML}"
-fi
-
-if [[ -z "$commands" ]]; then
-  echo "Error: No test commands provided." >&2
-  exit 1
-fi
-
-echo "Raw commands: $commands"
-if [[ "$commands_source" != "env" ]]; then
-  commands=$(re_quote_pytest_markers "$commands")
-  echo "After re-quoting: $commands"
-else
-  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
-fi
-commands=$(apply_intel_test_overrides "$commands")
-echo "Final commands: $commands"
-
-# Dry-run mode prints final commands and exits before Docker.
-if [[ "$DRY_RUN" == "1" ]]; then
-  echo "DRY_RUN=1 set, skipping Docker execution."
-  exit 0
-fi
-
-# --- Docker housekeeping ---
-cleanup_docker
-
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
-
-# --- Build or pull test image ---
-IMAGE="${IMAGE_TAG_XPU:-${image_name}}"
-
-echo "Using image: ${IMAGE}"
-
-remove_docker_container() {
-  docker rm -f "${container_name}" || true
-}
-trap remove_docker_container EXIT
-
-# --- Single-node job ---
-
-if [[ -z "${ZE_AFFINITY_MASK:-}" ]]; then
-  echo "Warning: ZE_AFFINITY_MASK is not set. Proceeding without device affinity." >&2
-fi
-
-export CMDS="${commands}"
-export HF_TOKEN ZE_AFFINITY_MASK
-
-{
-  flock 9
-  if docker image inspect "${IMAGE}" >/dev/null 2>&1; then
-    echo "Image already exists locally, skipping pull"
-  else
-    echo "Image not found locally, pulling image..."
-    timeout 900 docker pull "${IMAGE}"
-    echo "Pull step completed"
-  fi
-
-  docker create \
-    --device /dev/dri:/dev/dri \
-    --net=host \
-    --ipc=host \
-    --privileged \
-    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v "${HOME}/.cache/huggingface:/root/.cache/huggingface" \
-    --entrypoint='' \
-    -e HF_TOKEN \
-    -e ZE_AFFINITY_MASK \
-    -e BUILDKITE_PARALLEL_JOB \
-    -e BUILDKITE_PARALLEL_JOB_COUNT \
-    -e CMDS \
-    --name "${container_name}" \
-    "${IMAGE}" \
-    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"' \
-    >/dev/null
-} 9>/tmp/docker-pull.lock
-
-docker start -a "${container_name}"
@@ -41,7 +41,6 @@ get_config() {
        echo "Error: file '${TEST_RUN_CONFIG_FILE}' does not exist in the warehouse" >&2
        exit 1
    fi
-    # shellcheck source=/dev/null
    source "${TEST_RUN_CONFIG_FILE}"
    echo "Base docker image name that get from configuration: ${BASE_IMAGE_NAME}"
    return 0
@@ -49,8 +48,9 @@ get_config() {

 # get test running configuration.
 fetch_vllm_test_cfg
+get_config
 # Check if the function call was successful. If not, exit the script.
-if ! get_config; then
+if [ $? -ne 0 ]; then
  exit 1
 fi

@@ -62,14 +62,14 @@ agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
 echo "agent_idx: ${agent_idx}"
 builder_name="cachebuilder${agent_idx}"
 builder_cache_dir="/mnt/docker-cache${agent_idx}"
-mkdir -p "${builder_cache_dir}"
+mkdir -p ${builder_cache_dir}

 # Try building the docker image
 cat <<EOF | DOCKER_BUILDKIT=1 docker build \
-    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:"${PYPI_CACHE_HOST}" \
-    --builder "${builder_name}" --cache-from type=local,src="${builder_cache_dir}" \
-                           --cache-to type=local,dest="${builder_cache_dir}",mode=max \
-    --progress=plain --load -t "${image_name}" -f - .
+    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_HOST} \
+    --builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
+                           --cache-to type=local,dest=${builder_cache_dir},mode=max \
+    --progress=plain --load -t ${image_name} -f - .
 FROM ${BASE_IMAGE_NAME}

 # Define environments
@@ -116,7 +116,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
    export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
    source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
    source /usr/local/Ascend/nnal/atb/set_env.sh && \
-    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -i)-linux/devlib && \
+    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
    python3 -m pip install -v -e /workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/

 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
@@ -139,7 +139,7 @@ trap remove_docker_container EXIT
 # Generate corresponding --device args based on BUILDKITE_AGENT_NAME
 # Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
 #   e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
-#   returns one argument per line: --device, /dev/davinciX, ...
+#   returns --device /dev/davinci0 --device /dev/davinci1
 parse_and_gen_devices() {
    local input="$1"
    local index cards_num
@@ -151,24 +151,29 @@ parse_and_gen_devices() {
        return 1
    fi

+    local devices=""
    local i=0
    while (( i < cards_num )); do
        local dev_idx=$(((index - 1)*cards_num + i ))
-        printf '%s\n' "--device"
-        printf '%s\n' "/dev/davinci${dev_idx}"
+        devices="$devices --device /dev/davinci${dev_idx}"
        ((i++))
    done
+
+    # trim leading space
+    devices="${devices#"${devices%%[![:space:]]*}"}"
+    # Output devices: assigned to the caller variable
+    printf '%s' "$devices"
 }

-mapfile -t device_args < <(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
+devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1

 # Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
 # This test checks whether the OOT platform interface is functioning properly in conjunction with
 # the hardware plugin vllm-ascend.
 model_cache_dir=/mnt/modelscope${agent_idx}
-mkdir -p "${model_cache_dir}"
+mkdir -p ${model_cache_dir}
 docker run \
-    "${device_args[@]}" \
+    ${devices} \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
@@ -177,7 +182,7 @@ docker run \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v "${model_cache_dir}":/root/.cache/modelscope \
+    -v ${model_cache_dir}:/root/.cache/modelscope \
    --entrypoint="" \
    --name "${container_name}" \
    "${image_name}" \
@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.12" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"

@@ -127,7 +127,7 @@ run_and_track_test() {

 # --- Actual Test Execution ---
 run_and_track_test 1 "test_struct_output_generate.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
 run_and_track_test 2 "test_moe_pallas.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
 run_and_track_test 3 "test_lora.py" \
@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.12" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"

@@ -136,6 +136,8 @@ run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
+run_and_track_test 5 "examples/offline_inference/tpu.py" \
+    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
 run_and_track_test 6 "test_tpu_model_runner.py" \
    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
 run_and_track_test 7 "test_sampler.py" \
@@ -0,0 +1,55 @@
+#!/bin/bash
+
+# This script build the CPU docker image and run the offline inference inside the container.
+# It serves a sanity check for compilation and basic model usage.
+set -ex
+
+image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+
+# Try building the docker image
+docker build -t ${image_name} -f docker/Dockerfile.xpu .
+
+# Setup cleanup
+remove_docker_container() {
+  docker rm -f "${container_name}" || true;
+  docker image rm -f "${image_name}" || true;
+  docker system prune -f || true;
+}
+trap remove_docker_container EXIT
+
+# Run the image and test offline inference/tensor parallel
+docker run \
+    --device /dev/dri:/dev/dri \
+    --net=host \
+    --ipc=host \
+    --privileged \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK}" \
+    --name "${container_name}" \
+    "${image_name}" \
+    bash -c '
+    set -e
+    echo $ZE_AFFINITY_MASK
+    pip install tblib==3.1.0
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
+    python3 examples/offline_inference/basic/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4  --block-size 64 --enforce-eager
+    python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
+    python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
+    cd tests
+    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py
+    pytest -v -s v1/engine
+    pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
+    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
+    pytest -v -s v1/structured_output
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py
+    pytest -v -s v1/test_serial_utils.py
+'
@@ -1,44 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-set -euo pipefail
-
-if python3 -c "import torch; raise SystemExit(0 if torch.version.hip is not None else 1)"; then
-    uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-    exit 0
-fi
-
-REQUIREMENTS_FILE="${KV_CONNECTORS_REQUIREMENTS:-/vllm-workspace/requirements/kv_connectors.txt}"
-
-uv pip install --system -r "${REQUIREMENTS_FILE}"
-
-NIXL_METADATA=$(python3 - <<'PY'
-import importlib.metadata as metadata
-
-import torch
-
-cuda_version = torch.version.cuda
-if cuda_version is None:
-    raise SystemExit("torch.version.cuda is not set")
-
-print(cuda_version.split(".", 1)[0], metadata.version("nixl"))
-PY
-)
-read -r CUDA_MAJOR NIXL_VERSION <<<"${NIXL_METADATA}"
-
-# nixl>=1.1.0 can install multiple CUDA wheel variants. Keep only the variant
-# matching this CI image so nixl_ep_cpp links against the available libcudart.
-uv pip uninstall --system nixl-cu12 nixl-cu13 2>/dev/null || true
-uv pip install --system --no-deps "nixl-cu${CUDA_MAJOR}==${NIXL_VERSION}"
-
-python3 - <<'PY'
-import importlib.metadata as metadata
-
-for package_name in ("nixl", "nixl-cu12", "nixl-cu13"):
-    try:
-        version = metadata.version(package_name)
-    except metadata.PackageNotFoundError:
-        version = "not installed"
-    print(f"{package_name}: {version}")
-PY
@@ -1,127 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Shared helper for rewriting a wheel's platform tag from the generic
-# ``linux_<arch>`` to the correct ``manylinux_<major>_<minor>_<arch>``.
-# After sourcing, call ``apply_manylinux_tag <wheel>`` on each wheel
-# that still carries the generic tag; the renamed path is printed on
-# stdout (logs go to stderr).
-#
-# Why a pinned Docker container instead of using whatever Python
-# happens to be on the agent:
-#   - vLLM's release agents are heterogeneous -- they don't agree on
-#     a Python minor version, and we can't rely on a particular
-#     ``auditwheel`` being installed.
-#   - ``detect-manylinux-tag.py`` reads ``auditwheel.wheel_abi`` and
-#     ``Policy.sym_policy``, which are *internal* APIs without a
-#     stability promise. Pinning both Python and auditwheel makes the
-#     detected tag a function of the inputs alone, and shifts version
-#     bumps from "implicit drift" to "deliberate, retested change".
-#   - Other release scripts (``generate-and-upload-nightly-index.sh``,
-#     ``upload-rocm-wheels.sh``) already use the python:3-slim image
-#     when the agent's interpreter is too old; this is the same idea
-#     made stricter.
-#
-# To keep the per-wheel cost down (the ROCm upload retags ~10 wheels
-# each run), we install auditwheel into a long-lived helper container
-# once on source, then ``docker exec`` into it for each call.
-#
-# Trap behaviour:
-# - Sourcing installs an EXIT trap that calls ``manylinux_cleanup`` to
-#   tear down the helper container. Any EXIT trap that was already in
-#   place when this file was sourced is captured and run AFTER our
-#   cleanup, so we don't silently clobber it.
-# - If a caller sets a new EXIT trap *after* sourcing, that trap will
-#   replace ours; in that case the caller should call
-#   ``manylinux_cleanup`` from their own handler.
-
-if [[ -n "${_MANYLINUX_LIB_SOURCED:-}" ]]; then
-    return 0
-fi
-_MANYLINUX_LIB_SOURCED=1
-
-# Pin both sides. Bump these deliberately and re-run a representative
-# wheel from each build target through the detection.
-_MANYLINUX_PYTHON_IMAGE="python:3.12-slim"
-_MANYLINUX_AUDITWHEEL_VERSION="6.6.0"
-
-# Resolve our own directory (and the sibling detect script) using the
-# canonical, symlink-resolved path. The container mounts cwd at the
-# same absolute path on both sides, so all paths we hand to it -- the
-# script, the wheel -- must canonicalise to a location under cwd.
-_MANYLINUX_LIB_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
-_MANYLINUX_DETECT_SCRIPT="$(cd "${_MANYLINUX_LIB_DIR}/.." && pwd -P)/detect-manylinux-tag.py"
-_MANYLINUX_CWD="$(pwd -P)"
-
-docker pull --quiet "$_MANYLINUX_PYTHON_IMAGE" >/dev/null
-
-# Spin up a long-lived helper container so we install auditwheel once
-# and then ``docker exec`` into it for each wheel.
-#
-# The container runs as root so ``pip install`` can write into the
-# system site-packages; individual ``docker exec`` calls below pin
-# themselves to the host UID so any file rename happens with host
-# ownership, not root.
-_MANYLINUX_CONTAINER="$(docker run -d --rm \
-    -v "$_MANYLINUX_CWD:$_MANYLINUX_CWD" \
-    -w "$_MANYLINUX_CWD" \
-    "$_MANYLINUX_PYTHON_IMAGE" \
-    sleep infinity)"
-docker exec "$_MANYLINUX_CONTAINER" \
-    pip install --quiet --disable-pip-version-check \
-    --root-user-action=ignore \
-    "auditwheel==${_MANYLINUX_AUDITWHEEL_VERSION}"
-
-# Public cleanup -- safe to call multiple times.
-manylinux_cleanup() {
-    if [[ -n "${_MANYLINUX_CONTAINER:-}" ]]; then
-        docker rm -f "$_MANYLINUX_CONTAINER" >/dev/null 2>&1 || true
-        _MANYLINUX_CONTAINER=""
-    fi
-}
-
-# Capture any EXIT trap that was already in place so we can chain to
-# it rather than overwrite it. ``trap -p EXIT`` prints the handler in
-# eval-able form (``trap -- 'CMD' EXIT``) or nothing if unset; we
-# strip the wrapper to recover ``CMD``. Handles the common case --
-# CMDs without embedded single quotes -- and degrades gracefully (we
-# still run our own cleanup) for the pathological case.
-_manylinux_prev_exit_trap_cmd=""
-_manylinux_existing_exit_trap="$(trap -p EXIT)"
-if [[ -n "$_manylinux_existing_exit_trap" ]]; then
-    _tmp="${_manylinux_existing_exit_trap#trap -- \'}"
-    _manylinux_prev_exit_trap_cmd="${_tmp%\' EXIT}"
-    unset _tmp
-fi
-unset _manylinux_existing_exit_trap
-
-_manylinux_run_exit_chain() {
-    manylinux_cleanup
-    if [[ -n "$_manylinux_prev_exit_trap_cmd" ]]; then
-        eval "$_manylinux_prev_exit_trap_cmd"
-    fi
-}
-trap _manylinux_run_exit_chain EXIT
-
-# Detect the manylinux platform tag for a single wheel and rename it
-# in place, printing the renamed wheel path on stdout. Returns
-# non-zero on failure (which under ``set -e`` propagates to caller).
-#
-# The wheel must be reachable via a path under the host cwd so it's
-# visible inside the helper container; in CI the wheels always live
-# under ``artifacts/`` so this is fine.
-apply_manylinux_tag() {
-    local wheel="$1"
-    local abs_wheel
-    abs_wheel="$(realpath "$wheel")"
-    local new_wheel
-    new_wheel="$(docker exec -u "$(id -u):$(id -g)" \
-        "$_MANYLINUX_CONTAINER" \
-        python "$_MANYLINUX_DETECT_SCRIPT" "$abs_wheel")"
-    if [[ -z "$new_wheel" || ! -f "$new_wheel" ]]; then
-        echo "apply_manylinux_tag: detect-manylinux-tag.py did not produce a valid wheel path for $wheel" >&2
-        return 1
-    fi
-    printf '%s\n' "$new_wheel"
-}
@@ -1,41 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Pick a Python interpreter for buildkite scripts: prefer a local
-# ``python3`` if it is recent enough (>= 3.12), otherwise fall back to
-# a one-shot Docker container running ``python:3-slim``. After
-# ``select_python`` returns, ``$PYTHON`` is set in the caller's shell
-# and is safe to use as a command (e.g. ``$PYTHON some_script.py``).
-#
-# The 3.12 threshold matches what the existing nightly-index work
-# expects -- typing features used by ``generate-nightly-index.py``.
-# This helper does not pin the *minor* version; if you need stricter
-# reproducibility (e.g. relying on auditwheel internals), invoke
-# Docker yourself with a pinned tag rather than calling this.
-
-if [[ -n "${_SELECT_PYTHON_LIB_SOURCED:-}" ]]; then
-    return 0
-fi
-_SELECT_PYTHON_LIB_SOURCED=1
-
-# Sets ``PYTHON`` in the caller's shell and exports it. Idempotent --
-# calling twice is safe and the second call simply re-runs the probe.
-select_python() {
-    local py="${PYTHON_PROG:-python3}"
-    local has_new_python
-    has_new_python=$("$py" -c \
-        "print(1 if __import__('sys').version_info >= (3,12) else 0)" \
-        2>/dev/null || echo 0)
-    if [[ "$has_new_python" -eq 0 ]]; then
-        # ``-u $(id -u):$(id -g)`` so files created via the container
-        # end up owned by the host user, not root.
-        docker pull python:3-slim
-        PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-    else
-        PYTHON="$py"
-    fi
-    export PYTHON
-    echo "Using python interpreter: $PYTHON"
-    echo "Python version: $($PYTHON --version)"
-}
@@ -1,180 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Publish release Docker images from ECR to DockerHub.
-# Pulls per-arch images, tags with latest and versioned tags, pushes them,
-# then creates and pushes multi-arch manifests.
-
-set -euo pipefail
-
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
-if [ -z "${RELEASE_VERSION}" ]; then
-  echo "ERROR: release-version metadata not set"
-  exit 1
-fi
-
-COMMIT="$BUILDKITE_COMMIT"
-ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-
-echo "========================================"
-echo "Publishing release images v${RELEASE_VERSION}"
-echo "  Commit: ${COMMIT}"
-echo "  ROCm base cache key: ${ROCM_BASE_CACHE_KEY}"
-echo "========================================"
-
-# Login to ECR to pull staging images
-aws ecr-public get-login-password --region us-east-1 | \
-  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-
-# ---- CUDA (default: 13.0) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:latest-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai:latest-x86_64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:latest-aarch64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker push vllm/vllm-openai:latest-aarch64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-
-docker manifest rm vllm/vllm-openai:latest || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION} || true
-docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker manifest push vllm/vllm-openai:latest
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
-
-# ---- CUDA 12.9 ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
-docker push vllm/vllm-openai:latest-x86_64-cu129
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-docker push vllm/vllm-openai:latest-aarch64-cu129
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-
-docker manifest rm vllm/vllm-openai:latest-cu129 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129 || true
-docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-docker manifest push vllm/vllm-openai:latest-cu129
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
-
-# ---- Ubuntu 24.04 (CUDA 13.0) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
-docker push vllm/vllm-openai:latest-x86_64-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-docker push vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-
-docker manifest rm vllm/vllm-openai:latest-ubuntu2404 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 || true
-docker manifest create vllm/vllm-openai:latest-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-docker manifest push vllm/vllm-openai:latest-ubuntu2404
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404
-
-# ---- Ubuntu 24.04 (CUDA 12.9) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
-docker push vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-docker push vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-
-docker manifest rm vllm/vllm-openai:latest-cu129-ubuntu2404 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 || true
-docker manifest create vllm/vllm-openai:latest-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-docker manifest push vllm/vllm-openai:latest-cu129-ubuntu2404
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404
-
-# ---- ROCm ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:latest
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-docker push vllm/vllm-openai-rocm:latest
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:latest-base
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-docker push vllm/vllm-openai-rocm:latest-base
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-
-# ---- CPU ----
-# CPU images are behind separate block steps and may not have been built.
-# All-or-nothing: inspect both arches first, then either publish everything
-# (per-arch + multi-arch manifest) or skip everything. Publishing only one
-# arch would leave `:latest-x86_64` pointing at the new release while the
-# `:latest` multi-arch manifest still resolves to the previous release.
-
-CPU_X86_TAG=public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
-CPU_ARM_TAG=public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
-
-CPU_X86_AVAILABLE=false
-CPU_ARM_AVAILABLE=false
-docker manifest inspect "${CPU_X86_TAG}" >/dev/null 2>&1 && CPU_X86_AVAILABLE=true
-docker manifest inspect "${CPU_ARM_TAG}" >/dev/null 2>&1 && CPU_ARM_AVAILABLE=true
-
-if [ "$CPU_X86_AVAILABLE" = "true" ] && [ "$CPU_ARM_AVAILABLE" = "true" ]; then
-  docker pull "${CPU_X86_TAG}"
-  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:latest-x86_64
-  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-  docker push vllm/vllm-openai-cpu:latest-x86_64
-  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-
-  docker pull "${CPU_ARM_TAG}"
-  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:latest-arm64
-  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-  docker push vllm/vllm-openai-cpu:latest-arm64
-  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-
-  docker manifest rm vllm/vllm-openai-cpu:latest || true
-  docker manifest rm vllm/vllm-openai-cpu:v${RELEASE_VERSION} || true
-  docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
-  docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-  docker manifest push vllm/vllm-openai-cpu:latest
-  docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
-elif [ "$CPU_X86_AVAILABLE" = "false" ] && [ "$CPU_ARM_AVAILABLE" = "false" ]; then
-  echo "WARNING: Neither CPU image found in ECR, skipping CPU publish (ensure block-cpu-release-image-build and block-arm64-cpu-release-image-build were unblocked and the builds finished pushing)"
-else
-  # Partial state: one arch built, the other did not. Fail loudly rather than
-  # ship a Docker Hub state where `:latest-${arch}` and `:latest` (multi-arch)
-  # disagree on which release they point at.
-  echo "ERROR: Partial CPU build detected (x86_64=${CPU_X86_AVAILABLE}, arm64=${CPU_ARM_AVAILABLE})."
-  echo "       Refusing to publish to avoid split-tag drift between per-arch and multi-arch tags."
-  echo "       Re-run the missing CPU build and retry, or manually publish if a single-arch release is intended."
-  exit 1
-fi
-
-echo ""
-echo "Successfully published release images for v${RELEASE_VERSION}"
@@ -1,62 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Push ROCm nightly base image and nightly image from ECR 
-# to Docker Hub as vllm/vllm-openai-rocm:base-nightly and vllm/vllm-openai-rocm:nightly
-# and vllm/vllm-openai-rocm:base-nightly-<commit> and vllm/vllm-openai-rocm:nightly-<commit>.
-# Run when NIGHTLY=1 after build-rocm-release-image has pushed to ECR.
-#
-# Local testing (no push to Docker Hub):
-#   BUILDKITE_COMMIT=<commit-with-rocm-image-in-ecr> DRY_RUN=1 bash .buildkite/scripts/push-nightly-builds-rocm.sh
-# Requires: AWS CLI configured (for ECR public login), Docker. For full run: Docker Hub login.
-
-set -ex
-
-# Use BUILDKITE_COMMIT from env (required; set to a commit that has ROCm image in ECR for local test)
-BUILDKITE_COMMIT="${BUILDKITE_COMMIT:?Set BUILDKITE_COMMIT to the commit SHA that has the ROCm image in ECR (e.g. from a previous release pipeline run)}"
-DRY_RUN="${DRY_RUN:-0}"
-
-# Get the base image ECR tag (set by build-rocm-release-image pipeline step)
-BASE_ORIG_TAG="$(buildkite-agent meta-data get rocm-base-ecr-tag 2>/dev/null || echo "")"
-if [ -z "$BASE_ORIG_TAG" ]; then
-  echo "WARNING: rocm-base-ecr-tag metadata not found, falling back to commit-based tag"
-  BASE_ORIG_TAG="public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base"
-fi
-
-ORIG_TAG="${BUILDKITE_COMMIT}-rocm"
-BASE_TAG_NAME="base-nightly"
-TAG_NAME="nightly"
-BASE_TAG_NAME_COMMIT="base-nightly-${BUILDKITE_COMMIT}"
-TAG_NAME_COMMIT="nightly-${BUILDKITE_COMMIT}"
-
-echo "Pushing ROCm base image from ECR: $BASE_ORIG_TAG"
-echo "Pushing ROCm release image from ECR tag: $ORIG_TAG to Docker Hub as $TAG_NAME and $TAG_NAME_COMMIT"
-[[ "$DRY_RUN" == "1" ]] && echo "[DRY_RUN] Skipping push to Docker Hub"
-
-# Login to ECR and pull the image built by build-rocm-release-image
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-docker pull "$BASE_ORIG_TAG"
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG"
-
-# Tag for Docker Hub (base-nightly and base-nightly-<commit>, nightly and nightly-<commit>)
-docker tag "$BASE_ORIG_TAG" vllm/vllm-openai-rocm:"$BASE_TAG_NAME"
-docker tag "$BASE_ORIG_TAG" vllm/vllm-openai-rocm:"$BASE_TAG_NAME_COMMIT"
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG" vllm/vllm-openai-rocm:"$TAG_NAME"
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG" vllm/vllm-openai-rocm:"$TAG_NAME_COMMIT"
-
-if [[ "$DRY_RUN" == "1" ]]; then
-  echo "[DRY_RUN] Would push vllm/vllm-openai-rocm:$BASE_TAG_NAME and vllm/vllm-openai-rocm:$BASE_TAG_NAME_COMMIT"
-  echo "[DRY_RUN] Would push vllm/vllm-openai-rocm:$TAG_NAME and vllm/vllm-openai-rocm:$TAG_NAME_COMMIT"
-  echo "[DRY_RUN] Local tags created. Exiting without push."
-  exit 0
-fi
-
-# Push to Docker Hub (docker-login plugin runs before this step in CI)
-docker push vllm/vllm-openai-rocm:"$BASE_TAG_NAME"
-docker push vllm/vllm-openai-rocm:"$BASE_TAG_NAME_COMMIT"
-docker push vllm/vllm-openai-rocm:"$TAG_NAME"
-docker push vllm/vllm-openai-rocm:"$TAG_NAME_COMMIT"
-
-echo "Pushed vllm/vllm-openai-rocm:$BASE_TAG_NAME and vllm/vllm-openai-rocm:$BASE_TAG_NAME_COMMIT"
-echo "Pushed vllm/vllm-openai-rocm:$TAG_NAME and vllm/vllm-openai-rocm:$TAG_NAME_COMMIT"
@@ -21,16 +21,16 @@ echo "Pushing original tag $ORIG_TAG_NAME$ORIG_TAG_SUFFIX to new nightly tag nam

 # pull original arch-dependent images from AWS ECR Public
 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:"$ORIG_TAG_NAME"-x86_64"$ORIG_TAG_SUFFIX"
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-aarch64"$ORIG_TAG_SUFFIX"
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX
 # tag arch-dependent images
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64"$ORIG_TAG_SUFFIX" vllm/vllm-openai:"$TAG_NAME"-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-aarch64"$ORIG_TAG_SUFFIX" vllm/vllm-openai:"$TAG_NAME"-aarch64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-x86_64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-aarch64
 # push arch-dependent images to DockerHub
-docker push vllm/vllm-openai:"$TAG_NAME"-x86_64
-docker push vllm/vllm-openai:"$TAG_NAME"-aarch64
+docker push vllm/vllm-openai:$TAG_NAME-x86_64
+docker push vllm/vllm-openai:$TAG_NAME-aarch64
 # push arch-independent manifest to DockerHub
-docker manifest create vllm/vllm-openai:"$TAG_NAME" vllm/vllm-openai:"$TAG_NAME"-x86_64 vllm/vllm-openai:"$TAG_NAME"-aarch64 --amend
-docker manifest create vllm/vllm-openai:"$TAG_NAME"-"$BUILDKITE_COMMIT" vllm/vllm-openai:"$TAG_NAME"-x86_64 vllm/vllm-openai:"$TAG_NAME"-aarch64 --amend
-docker manifest push vllm/vllm-openai:"$TAG_NAME"
-docker manifest push vllm/vllm-openai:"$TAG_NAME"-"$BUILDKITE_COMMIT"
+docker manifest create vllm/vllm-openai:$TAG_NAME vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
+docker manifest create vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
+docker manifest push vllm/vllm-openai:$TAG_NAME
+docker manifest push vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT
@@ -0,0 +1,64 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Setup script for Prime-RL integration tests
+# This script prepares the environment for running Prime-RL tests with nightly vLLM
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
+PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
+
+if command -v rocm-smi &> /dev/null || command -v rocminfo &> /dev/null; then
+    echo "AMD GPU detected. Prime-RL currently only supports NVIDIA. Skipping..."
+    exit 0
+fi
+
+echo "Setting up Prime-RL integration test environment..."
+
+# Clean up any existing Prime-RL directory
+if [ -d "${PRIME_RL_DIR}" ]; then
+    echo "Removing existing Prime-RL directory..."
+    rm -rf "${PRIME_RL_DIR}"
+fi
+
+# Install UV if not available
+if ! command -v uv &> /dev/null; then
+    echo "Installing UV package manager..."
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    source $HOME/.local/bin/env
+fi
+
+# Clone Prime-RL repository at specific branch for reproducible tests
+PRIME_RL_BRANCH="integ-vllm-main"
+echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
+git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
+cd "${PRIME_RL_DIR}"
+
+echo "Setting up UV project environment..."
+export UV_PROJECT_ENVIRONMENT=/usr/local
+ln -s /usr/bin/python3 /usr/local/bin/python
+
+# Remove vllm pin from pyproject.toml
+echo "Removing vllm pin from pyproject.toml..."
+sed -i '/vllm==/d' pyproject.toml
+
+# Sync Prime-RL dependencies
+echo "Installing Prime-RL dependencies..."
+uv sync --inexact && uv sync --inexact --all-extras
+
+# Verify installation
+echo "Verifying installations..."
+uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
+uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
+
+echo "Prime-RL integration test environment setup complete!"
+
+echo "Running Prime-RL integration tests..."
+export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
+uv run pytest -vs tests/integration/test_rl.py -m gpu
+
+echo "Prime-RL integration tests completed!"
@@ -1,187 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-MODE="${1:-}"
-
-if [[ "$MODE" != "style-clippy" && "$MODE" != "test" ]]; then
-  echo "Usage: $0 {style-clippy|test}" >&2
-  exit 2
-fi
-
-ROOT_DIR="$(git rev-parse --show-toplevel)"
-cd "$ROOT_DIR"
-
-export CARGO_TERM_COLOR="${CARGO_TERM_COLOR:-always}"
-export CARGO_HOME="${CARGO_HOME:-$HOME/.cargo}"
-export RUSTUP_HOME="${RUSTUP_HOME:-$HOME/.rustup}"
-export PATH="$CARGO_HOME/bin:$PATH"
-
-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)
-      arch="x86_64"
-      ;;
-    aarch64|arm64)
-      arch="aarch_64"
-      ;;
-    *)
-      echo "Unsupported protoc architecture: $(uname -m)" >&2
-      return 1
-      ;;
-  esac
-
-  local url="https://github.com/protocolbuffers/protobuf/releases/download/v${version}/protoc-${version}-linux-${arch}.zip"
-  local tmp_dir
-  tmp_dir="$(mktemp -d)"
-
-  log_section "Installing protoc ${version}"
-  curl -L --proto '=https' --tlsv1.2 -sSf "$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"
-}
-
-rust_toolchain() {
-  awk -F '"' '/channel[[:space:]]*=/ { print $2; exit }' rust-toolchain.toml
-}
-
-install_rust_toolchain() {
-  log_section "Installing Rust toolchain"
-  if ! command -v rustup >/dev/null 2>&1; then
-    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs \
-      | sh -s -- -y --profile minimal --default-toolchain none
-  fi
-
-  local toolchain
-  toolchain="$(rust_toolchain)"
-  rustup toolchain install "$toolchain" --profile minimal --component rustfmt,clippy
-  rustup component add --toolchain "$toolchain" rustfmt clippy
-}
-
-install_cargo_binstall() {
-  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/main/install-from-binstall-release.sh \
-    | bash
-}
-
-install_cargo_sort() {
-  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_nextest() {
-  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() {
-  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="$CARGO_HOME/bin" sh
-}
-
-setup_pyo3_python() {
-  local python_version="${PYO3_PYTHON_VERSION:-3.12}"
-
-  log_section "Installing Python ${python_version} for PyO3 tests"
-  uv python install "$python_version"
-  PYO3_PYTHON="$(uv python find \
-    --managed-python \
-    --no-project \
-    --resolve-links \
-    "$python_version")"
-  export PYO3_PYTHON
-
-  local python_libdir
-  python_libdir="$("$PYO3_PYTHON" - <<'PY'
-import pathlib
-import sysconfig
-
-libdir = pathlib.Path(sysconfig.get_config_var("LIBDIR"))
-ldlibrary = sysconfig.get_config_var("LDLIBRARY")
-assert sysconfig.get_config_var("Py_ENABLE_SHARED") == 1
-assert ldlibrary
-assert (libdir / ldlibrary).exists(), libdir / ldlibrary
-print(libdir)
-PY
-)"
-
-  export LD_LIBRARY_PATH="${python_libdir}:${LD_LIBRARY_PATH:-}"
-  export LIBRARY_PATH="${python_libdir}:${LIBRARY_PATH:-}"
-}
-
-run_style_clippy() {
-  install_cargo_sort
-
-  log_section "Checking Rust formatting"
-  cargo fmt --manifest-path rust/Cargo.toml --all -- --check
-
-  log_section "Checking Cargo.toml ordering"
-  cargo sort --workspace --check rust
-
-  log_section "Running clippy"
-  cargo clippy \
-    --manifest-path rust/Cargo.toml \
-    --workspace \
-    --all-targets \
-    --all-features \
-    --locked \
-    -- \
-    -D warnings
-}
-
-run_tests() {
-  install_uv
-  setup_pyo3_python
-  install_cargo_nextest
-
-  log_section "Running cargo nextest"
-  cargo nextest run \
-    --manifest-path rust/Cargo.toml \
-    --workspace \
-    --all-features \
-    --locked \
-    --no-fail-fast
-}
-
-install_protoc
-install_rust_toolchain
-
-case "$MODE" in
-  style-clippy)
-    run_style_clippy
-    ;;
-  test)
-    run_tests
-    ;;
-esac
@@ -49,17 +49,16 @@ for BACK in "${BACKENDS[@]}"; do
    --data-parallel-size 2 \
    --enable-expert-parallel \
    --enable-eplb \
-    --eplb-config '{"use_async": false}' \
    --trust-remote-code \
    --max-model-len 2048 \
-    --all2all-backend "$BACK" \
-    --port "$PORT" &
+    --all2all-backend $BACK \
+    --port $PORT &
  SERVER_PID=$!
-  wait_for_server "$PORT"
+  wait_for_server $PORT

  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
  python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
@@ -1,70 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-# Nightly e2e test for prefetch offloading with a MoE model.
-# Runs DeepSeek-V2-Lite with prefetch offloading of MoE expert weights
-# and validates GSM8K accuracy matches baseline (no offloading).
-#
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-#
-# Environment variables:
-#   ATTENTION_BACKEND   - attention backend to use (e.g., FLASH_ATTN,
-#                         ROCM_ATTN, FLASHINFER). If unset, uses vllm default.
-THRESHOLD=${1:-0.25}
-NUM_Q=${2:-1319}
-PORT=${3:-8030}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="deepseek-ai/DeepSeek-V2-Lite"
-
-# ── Build optional vllm serve flags ─────────────────────────────────────
-
-EXTRA_ARGS=()
-if [[ -n "${ATTENTION_BACKEND:-}" ]]; then
-  echo "Using attention backend: ${ATTENTION_BACKEND}"
-  EXTRA_ARGS+=(--attention-backend "${ATTENTION_BACKEND}")
-fi
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-vllm serve "$MODEL" \
-  --max-model-len 2048 \
-  --offload-group-size 8 \
-  --offload-num-in-group 2 \
-  --offload-prefetch-step 1 \
-  --offload-params w13_weight w2_weight \
-  --generation-config vllm \
-  --port "$PORT" \
-  ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
-SERVER_PID=$!
-wait_for_server "$PORT"
-
-TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-OUT="${OUT_DIR}/${TAG}_prefetch_offload.json"
-python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
-python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} prefetch_offload: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} prefetch_offload accuracy {acc}"
-PY
-
-cleanup
-SERVER_PID=
@@ -47,20 +47,20 @@ for BACK in "${BACKENDS[@]}"; do
  vllm serve "$MODEL" \
    --enforce-eager \
    --enable-eplb \
-    --all2all-backend "$BACK" \
-    --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true, "use_async":false}' \
-    --tensor-parallel-size "${TENSOR_PARALLEL_SIZE}" \
-    --data-parallel-size "${DATA_PARALLEL_SIZE}" \
+    --all2all-backend $BACK \
+    --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true}' \
+    --tensor-parallel-size ${TENSOR_PARALLEL_SIZE} \
+    --data-parallel-size ${DATA_PARALLEL_SIZE} \
    --enable-expert-parallel \
    --trust-remote-code \
    --max-model-len 2048 \
-    --port "$PORT" &
+    --port $PORT &
  SERVER_PID=$!
-  wait_for_server "$PORT"
+  wait_for_server $PORT

  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
  python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
@@ -1,55 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.8}
-NUM_Q=${2:-1319}
-PORT=${3:-8050}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="Qwen/Qwen3-30B-A3B-FP8"
-BACK="allgather_reducescatter"
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-VLLM_DEEP_GEMM_WARMUP=skip \
-vllm serve "$MODEL" \
--enforce-eager \
--data-parallel-size 4 \
--enable-expert-parallel \
--enable-eplb \
--all2all-backend "$BACK" \
--eplb-config '{"window_size":20, "step_interval":100, "use_async":true}' \
--trust-remote-code \
--max-model-len 2048 \
--port "$PORT" &
-SERVER_PID=$!
-wait_for_server "$PORT"
-
-TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
-python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
-PY
@@ -24,7 +24,7 @@ if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:
  BACKENDS=("allgather_reducescatter")
  # Disable MOE padding for ROCm since it is causing eplb to fail
  export VLLM_ROCM_MOE_PADDING=0
-  PLATFORM_ARGS=("--no-async-scheduling" "--attention-backend=TRITON_ATTN")
+  PLATFORM_ARGS=("--no-async-scheduling")
  echo "Disabled async scheduling for ROCm platform due to issues with spec decode."
 else
  # Non-ROCm platform (CUDA/other)
@@ -51,20 +51,20 @@ for BACK in "${BACKENDS[@]}"; do
    --tensor-parallel-size 4 \
    --enable-expert-parallel \
    --enable-eplb \
-    --all2all-backend "$BACK" \
+    --all2all-backend $BACK \
    --eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
    --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
    --trust-remote-code \
    --max-model-len 2048 \
    --gpu-memory-utilization 0.9 \
    "${PLATFORM_ARGS[@]}" \
-    --port "$PORT" &
+    --port $PORT &
  SERVER_PID=$!
-  wait_for_server "$PORT"
+  wait_for_server $PORT

  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
  python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
@@ -1,252 +0,0 @@
-#!/bin/bash
-# Run BFCL (Berkeley Function Call Leaderboard) tool-calling correctness
-# evaluation against a local vLLM server.
-#
-# Usage:
-#   # Run with defaults (gpt-oss-20b, multi_turn)
-#   bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
-#
-#   # Run with gpt-oss-120b and multiple test categories
-#   BFCL_MODEL="openai/gpt-oss-120b" BFCL_TP_SIZE=4 \
-#     BFCL_TEST_CATEGORY="live_simple, multiple, parallel_multiple" \
-#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
-#
-#   # Chain both API types (use BFCL_OUTPUT_DIR to avoid overwriting results)
-#   BFCL_OUTPUT_DIR=./bfcl-chat-completions BFCL_API_TYPE=chat_completions \
-#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh && \
-#   BFCL_OUTPUT_DIR=./bfcl-responses BFCL_API_TYPE=responses \
-#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
-#
-# Environment variables (all optional, with defaults):
-#   BFCL_MODEL          - HF model name (default: openai/gpt-oss-20b)
-#   BFCL_API_TYPE       - API type: "chat_completions" or "responses" (default: chat_completions)
-#   BFCL_OUTPUT_DIR     - Directory for BFCL results (default: current working directory)
-#   BFCL_TEST_CATEGORY  - BFCL test categories (default: multi_turn)
-#   BFCL_TOOL_CALL_PARSER - Tool call parser name (default: openai)
-#   BFCL_NUM_THREADS    - Threads for BFCL generate (default: 8)
-#   BFCL_TP_SIZE        - Tensor parallel size (default: 1)
-#   BFCL_MAX_MODEL_LEN  - Max model length (default: 4096)
-#   BFCL_PORT           - Server port (default: 8000)
-#   BFCL_REASONING_PARSER - Reasoning parser name (default: disabled)
-#   BFCL_TEMPERATURE    - Temperature (default: 0.0)
-#   BFCL_EXTRA_ARGS     - Additional vLLM server args
-
-set -euo pipefail
-
-# ---- Configuration ----
-MODEL="${BFCL_MODEL:-openai/gpt-oss-20b}"
-API_TYPE="${BFCL_API_TYPE:-chat_completions}"
-OUTPUT_DIR="${BFCL_OUTPUT_DIR:-}"
-TEST_CATEGORY="${BFCL_TEST_CATEGORY:-multi_turn}"
-TOOL_CALL_PARSER="${BFCL_TOOL_CALL_PARSER:-openai}"
-NUM_THREADS="${BFCL_NUM_THREADS:-8}"
-TP_SIZE="${BFCL_TP_SIZE:-1}"
-MAX_MODEL_LEN="${BFCL_MAX_MODEL_LEN:-4096}"
-PORT="${BFCL_PORT:-8000}"
-REASONING_PARSER="${BFCL_REASONING_PARSER:-}"
-TEMPERATURE="${BFCL_TEMPERATURE:-0.0}"
-EXTRA_ARGS="${BFCL_EXTRA_ARGS:-}"
-
-# Set up output directory
-if [ -n "$OUTPUT_DIR" ]; then
-    mkdir -p "$OUTPUT_DIR"
-    OUTPUT_DIR="$(cd "$OUTPUT_DIR" && pwd)"
-fi
-
-echo "============================================"
-echo "BFCL Tool Call Correctness Evaluation"
-echo "============================================"
-echo "Model:          $MODEL"
-echo "Tool parser:    $TOOL_CALL_PARSER"
-echo "API type:       $API_TYPE"
-echo "Output dir:     ${OUTPUT_DIR:-<cwd>}"
-echo "Test category:  $TEST_CATEGORY"
-echo "TP size:        $TP_SIZE"
-echo "Max model len:  $MAX_MODEL_LEN"
-echo "Port:           $PORT"
-echo "Num threads:    $NUM_THREADS"
-echo "============================================"
-
-# ---- Install bfcl-eval if missing ----
-if ! python3 -c "import bfcl_eval" 2>/dev/null; then
-    echo "Installing bfcl-eval..."
-    uv pip install "bfcl-eval>=2025.10.20.1,<2026"
-fi
-
-# ---- Cleanup handler ----
-SERVER_PID=""
-cleanup() {
-    if [ -n "$SERVER_PID" ]; then
-        echo "Stopping vLLM server (pid=$SERVER_PID)..."
-        kill "$SERVER_PID" 2>/dev/null || true
-        wait "$SERVER_PID" 2>/dev/null || true
-    fi
-    # Remove BFCL lock files (created by filelock for thread-safe writes)
-    rm -rf .file_locks/
-    if [ -n "${OUTPUT_DIR:-}" ]; then
-        rm -rf "$OUTPUT_DIR/.file_locks/"
-    fi
-}
-trap cleanup EXIT
-
-# ---- Start vLLM server ----
-echo "Starting vLLM server..."
-
-SERVE_ARGS=(
-    "$MODEL"
-    --port "$PORT"
-    --enable-auto-tool-choice
-    --tool-call-parser "$TOOL_CALL_PARSER"
-    --tensor-parallel-size "$TP_SIZE"
-    --max-model-len "$MAX_MODEL_LEN"
-    --enforce-eager
-    --enable-prefix-caching
-)
-
-# Append reasoning parser if specified
-if [ -n "$REASONING_PARSER" ]; then
-    SERVE_ARGS+=(--reasoning-parser "$REASONING_PARSER")
-fi
-
-# Append any extra args
-if [ -n "$EXTRA_ARGS" ]; then
-    read -ra EXTRA_ARGS_ARRAY <<< "$EXTRA_ARGS"
-    SERVE_ARGS+=("${EXTRA_ARGS_ARRAY[@]}")
-fi
-
-echo "Command: vllm serve ${SERVE_ARGS[*]}"
-vllm serve "${SERVE_ARGS[@]}" &
-SERVER_PID=$!
-
-# ---- Wait for server to be ready ----
-echo "Waiting for vLLM server to start (timeout: 600s)..."
-SECONDS_WAITED=0
-until curl -sf "http://localhost:${PORT}/health" > /dev/null 2>&1; do
-    if [ $SECONDS_WAITED -ge 600 ]; then
-        echo ""
-        echo "ERROR: vLLM server failed to start within 600s"
-        exit 1
-    fi
-    if (( SECONDS_WAITED % 30 == 0 && SECONDS_WAITED > 0 )); then
-        echo "  Still waiting... (${SECONDS_WAITED}s elapsed)"
-    fi
-    sleep 2
-    SECONDS_WAITED=$((SECONDS_WAITED + 2))
-done
-echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
-
-# ---- Run BFCL evaluation ----
-# bfcl-eval has no CLI entry point; generate() and evaluate() are Typer
-# functions that must be called from Python. The MODEL_CONFIG_MAPPING must
-# be patched in-process so BFCL knows to use the OpenAI-compatible handler
-# against our local vLLM server.
-bfcl_exit_code=0
-python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$TEMPERATURE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
-import os
-import sys
-
-model = sys.argv[1]
-test_category = sys.argv[2]
-num_threads = int(sys.argv[3])
-port = sys.argv[4]
-api_type = sys.argv[5]
-temperature = float(sys.argv[6])
-output_dir = sys.argv[7] if len(sys.argv) > 7 and sys.argv[7] else os.getcwd()
-
-os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
-os.environ["OPENAI_API_KEY"] = "dummy"
-os.environ["BFCL_PROJECT_ROOT"] = output_dir
-
-import bfcl_eval.constants.model_config as bfcl_model_config
-from bfcl_eval.constants.model_config import ModelConfig
-from bfcl_eval.model_handler.api_inference.openai_completion import (
-    OpenAICompletionsHandler,
-)
-from bfcl_eval.model_handler.api_inference.openai_response import (
-    OpenAIResponsesHandler,
-)
-
-if api_type == "responses":
-    handler = OpenAIResponsesHandler
-else:
-    handler = OpenAICompletionsHandler
-
-bfcl_model_config.MODEL_CONFIG_MAPPING[model] = ModelConfig(
-    model_name=model,
-    display_name=f"{model} (FC) (vLLM)",
-    url=f"https://huggingface.co/{model}",
-    org="",
-    license="apache-2.0",
-    model_handler=handler,
-    input_price=None,
-    output_price=None,
-    is_fc_model=True,
-    underscore_to_dot=True,
-)
-
-from bfcl_eval.__main__ import evaluate, generate
-import inspect
-import typer
-
-
-def _get_default_kwargs(function):
-    kwargs = {}
-    for k, v in inspect.signature(function).parameters.items():
-        if v.default is not inspect.Parameter.empty:
-            default = v.default
-            if isinstance(default, typer.models.OptionInfo):
-                default = default.default
-            kwargs[k] = default
-    return kwargs
-
-
-# ---- generate ----
-print(f"=== BFCL generate: model={model} test_category={test_category} ===")
-gen_kwargs = _get_default_kwargs(generate)
-gen_kwargs["model"] = [model]
-gen_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
-gen_kwargs["skip_server_setup"] = True
-gen_kwargs["num_threads"] = num_threads
-gen_kwargs["temperature"] = temperature
-generate(**gen_kwargs)
-
-# ---- evaluate ----
-print(f"=== BFCL evaluate: model={model} test_category={test_category} ===")
-eval_kwargs = _get_default_kwargs(evaluate)
-eval_kwargs["model"] = [model]
-eval_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
-evaluate(**eval_kwargs)
-
-print("=== BFCL evaluation completed successfully ===")
-PYEOF
-
-# ---- Upload results to buildkite ----
-if command -v buildkite-agent &>/dev/null; then
-    if [ $bfcl_exit_code -eq 0 ]; then
-        STYLE="success"
-        STATUS="PASSED"
-    else
-        STYLE="error"
-        STATUS="FAILED"
-    fi
-
-    buildkite-agent annotate --style "$STYLE" --context "bfcl-results" <<EOF
-### BFCL Tool Call Correctness - ${STATUS}
- **Model:** \`${MODEL}\`
- **Parser:** \`${TOOL_CALL_PARSER}\`
- **API type:** \`${API_TYPE}\`
- **Test category:** \`${TEST_CATEGORY}\`
-EOF
-
-    # BFCL writes results to $BFCL_PROJECT_ROOT/result/ and scores to
-    # $BFCL_PROJECT_ROOT/score/
-    RESULTS_ROOT="${OUTPUT_DIR:-.}"
-    if [ -d "$RESULTS_ROOT/result" ]; then
-        buildkite-agent artifact upload "$RESULTS_ROOT/result/**/*"
-    fi
-    if [ -d "$RESULTS_ROOT/score" ]; then
-        buildkite-agent artifact upload "$RESULTS_ROOT/score/**/*"
-    fi
-fi
-
-exit $bfcl_exit_code
@@ -9,11 +9,10 @@ ENV_FILE=$1

 # For testing on local vm, use `set -a` to export all variables
 source /etc/environment
-# shellcheck source=/dev/null
-source "$ENV_FILE"
+source $ENV_FILE

 remove_docker_container() { 
-    docker rm -f "$CONTAINER_NAME" || true;
+    docker rm -f $CONTAINER_NAME || true;
 }

 trap remove_docker_container EXIT
@@ -42,13 +41,13 @@ echo
 echo "starting docker...$CONTAINER_NAME"
 echo    
 docker run \
- -v "$DOWNLOAD_DIR":"$DOWNLOAD_DIR" \
- --env-file "$ENV_FILE" \
+ -v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
+ --env-file $ENV_FILE \
 -e HF_TOKEN="$HF_TOKEN" \
- -e TARGET_COMMIT="$BUILDKITE_COMMIT" \
- -e MODEL="$MODEL" \
+ -e TARGET_COMMIT=$BUILDKITE_COMMIT \
+ -e MODEL=$MODEL \
 -e WORKSPACE=/workspace \
- --name "$CONTAINER_NAME" \
+ --name $CONTAINER_NAME \
 -d \
 --privileged \
 --network host \
@@ -42,21 +42,21 @@ echo "lanching vllm..."
 echo "logging to $VLLM_LOG"
 echo

-vllm serve "$MODEL" \
+vllm serve $MODEL \
 --seed 42 \
- --max-num-seqs "$MAX_NUM_SEQS" \
- --max-num-batched-tokens "$MAX_NUM_BATCHED_TOKENS" \
- --tensor-parallel-size "$TENSOR_PARALLEL_SIZE" \
+ --max-num-seqs $MAX_NUM_SEQS \
+ --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
+ --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
 --no-enable-prefix-caching \
- --download_dir "$DOWNLOAD_DIR" \
- --max-model-len "$MAX_MODEL_LEN" > "$VLLM_LOG" 2>&1 &
+ --download_dir $DOWNLOAD_DIR \
+ --max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &


 echo "wait for 20 minutes.."
 echo
 # sleep 1200
 # wait for 10 minutes...
-for _ in {1..120}; do
+for i in {1..120}; do
    # TODO: detect other type of errors.
    if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
        echo "Detected RuntimeError, exiting."
@@ -78,11 +78,11 @@ echo "logging to $BM_LOG"
 echo
 vllm bench serve \
    --backend vllm \
-    --model "$MODEL"  \
+    --model $MODEL  \
    --dataset-name sonnet \
    --dataset-path benchmarks/sonnet_4x.txt \
-    --sonnet-input-len "$INPUT_LEN" \
-    --sonnet-output-len "$OUTPUT_LEN" \
+    --sonnet-input-len $INPUT_LEN \
+    --sonnet-output-len $OUTPUT_LEN \
    --ignore-eos > "$BM_LOG"

 echo "completed..."
@@ -2,18 +2,27 @@

 set -ex

-# Upload a single wheel to S3, after detecting and applying the appropriate
-# manylinux platform tag with auditwheel.
-# Index generation is handled separately by generate-and-upload-nightly-index.sh.
-
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
+# ======== part 0: setup ========

 BUCKET="vllm-wheels"
+INDICES_OUTPUT_DIR="indices"
+DEFAULT_VARIANT_ALIAS="cu129" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
+PYTHON=${PYTHON_PROG:=python3} # try to read from env var, otherwise use python3
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"

-# ========= locate the wheel ==========
+# detect if python3.10+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
+if [[ "$has_new_python" -eq 0 ]]; then
+    # use new python from docker
+    docker pull python:3-slim
+    PYTHON="docker run --rm -v $(pwd):/app -w /app python:3-slim python3"
+fi
+
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
+
+# ========= part 1: collect, rename & upload the wheel ==========

 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -25,16 +34,75 @@ if [[ ${#wheel_files[@]} -ne 1 ]]; then
 fi
 wheel="${wheel_files[0]}"

-# ========= detect manylinux tag and rename ==========
+# default build image uses ubuntu 20.04, which corresponds to manylinux_2_31
+# we also accept params as manylinux tag
+# refer to https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-distros-to-build-wheels
+manylinux_version="${1:-manylinux_2_31}"

-wheel="$(apply_manylinux_tag "$wheel")"
+# Rename 'linux' to the appropriate manylinux version in the wheel filename
+if [[ "$wheel" != *"linux"* ]]; then
+  echo "Error: Wheel filename does not contain 'linux': $wheel"
+  exit 1
+fi
+new_wheel="${wheel/linux/$manylinux_version}"
+mv -- "$wheel" "$new_wheel"
+wheel="$new_wheel"
 echo "Renamed wheel to: $wheel"

 # Extract the version from the wheel
 version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
 echo "Version in wheel: $version"
+pure_version="${version%%+*}"
+echo "Pure version (without variant): $pure_version"

 # copy wheel to its own bucket
 aws s3 cp "$wheel" "$S3_COMMIT_PREFIX"

-echo "Wheel uploaded. Index generation is handled by a separate step."
+# ========= part 2: generate and upload indices ==========
+# generate indices for all existing wheels in the commit directory
+# this script might be run multiple times if there are multiple variants being built
+# so we need to guarantee there is little chance for "TOCTOU" issues
+# i.e., one process is generating indices while another is uploading a new wheel
+# so we need to ensure no time-consuming operations happen below
+
+# list all wheels in the commit directory
+echo "Existing wheels on S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+obj_json="objects.json"
+aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
+mkdir -p "$INDICES_OUTPUT_DIR"
+
+# call script to generate indicies for all existing wheels
+# this indices have relative paths that could work as long as it is next to the wheel directory in s3
+# i.e., the wheels are always in s3://vllm-wheels/<commit>/
+# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
+if [[ ! -z "$DEFAULT_VARIANT_ALIAS" ]]; then
+    alias_arg="--alias-to-default $DEFAULT_VARIANT_ALIAS"
+else
+    alias_arg=""
+fi
+
+# HACK: we do not need regex module here, but it is required by pre-commit hook
+# To avoid any external dependency, we simply replace it back to the stdlib re module
+sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
+$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" $alias_arg
+
+# copy indices to /<commit>/ unconditionally
+echo "Uploading indices to $S3_COMMIT_PREFIX"
+aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
+
+# copy to /nightly/ only if it is on the main branch and not a PR 
+if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
+    echo "Uploading indices to overwrite /nightly/"
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
+fi
+
+# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
+if [[ "$version" != *"dev"* ]]; then
+    echo "Re-generating indices for /$pure_version/"
+    rm -rf "$INDICES_OUTPUT_DIR/*"
+    mkdir -p "$INDICES_OUTPUT_DIR"
+    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
+    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" $alias_arg
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
+fi
@@ -7,7 +7,7 @@ SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"

 RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
-GIT_VERSION=$(git describe --exact-match --tags "$BUILDKITE_COMMIT" 2>/dev/null)
+GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)

 echo "Release version from Buildkite: $RELEASE_VERSION"

@@ -39,17 +39,10 @@ fi

 set -x # avoid printing secrets above

-# install uv if not already available
-if ! command -v uv &> /dev/null; then
-  curl -LsSf https://astral.sh/uv/install.sh | UV_VERSION=0.11.14 sh
-  export PATH="$HOME/.local/bin:$PATH"
-fi
-
-# install twine and sdist build prerequisites using uv with Python 3.12
-uv venv --python 3.12 /tmp/vllm-release-env
+# install twine from pypi
+python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
-uv pip install twine
-uv pip install -r requirements/build/cuda.txt
+pip install twine
 python3 -m twine --version

 # copy release wheels to local directory
@@ -61,13 +54,10 @@ mkdir -p $DIST_DIR
 # include only wheels for the release version, ignore all files with "dev" or "rc" in the name (without excluding 'aarch64')
 aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
 echo "Wheels copied to local directory"
-# generate source distribution using setup.py
-python setup.py sdist --dist-dir=$DIST_DIR
+# generate source tarball
+git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" $BUILDKITE_COMMIT
 ls -la $DIST_DIR

-SDIST_FILE=$(find $DIST_DIR -name "vllm*.tar.gz")
-echo "Found sdist: $SDIST_FILE"
-
 # upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
 PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
 if [[ -z "$PYPI_WHEEL_FILES" ]]; then
@@ -75,6 +65,6 @@ if [[ -z "$PYPI_WHEEL_FILES" ]]; then
  exit 1
 fi

-python3 -m twine check "$PYPI_WHEEL_FILES" "$SDIST_FILE"
-python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES" "$SDIST_FILE"
-echo "Wheels and source distribution uploaded to PyPI"
+python3 -m twine check $PYPI_WHEEL_FILES
+python3 -m twine upload --non-interactive --verbose $PYPI_WHEEL_FILES
+echo "Wheels uploaded to PyPI"
@@ -20,6 +20,10 @@ BUCKET="${S3_BUCKET:-vllm-wheels}"
 ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
 S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
 INDICES_OUTPUT_DIR="rocm-indices"
+PYTHON="${PYTHON_PROG:-python3}"
+
+# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
+MANYLINUX_VERSION="manylinux_2_35"

 echo "========================================"
 echo "ROCm Wheel Upload Configuration"
@@ -30,21 +34,19 @@ echo "Commit: $BUILDKITE_COMMIT"
 echo "Branch: $BUILDKITE_BRANCH"
 echo "========================================"

-# ======== Part 0: Setup Python and helpers ========
+# ======== Part 0: Setup Python ========

-# Pick a Python interpreter for index generation -- local if recent
-# enough, else a one-shot docker fallback.
-# shellcheck source=lib/select-python.sh
-source .buildkite/scripts/lib/select-python.sh
-select_python
+# Detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
+if [[ "$has_new_python" -eq 0 ]]; then
+    # Use new python from docker
+    # Use --user to ensure files are created with correct ownership (not root)
+    docker pull python:3-slim
+    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi

-# Set up auditwheel-in-a-container for the manylinux retagging step.
-# Distinct from select_python: ``manylinux.sh`` deliberately pins both
-# the Python and auditwheel versions (the script reads auditwheel
-# internals) and so always runs in a known-good container regardless
-# of what's on the agent.
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"

 # ======== Part 1: Collect and prepare wheels ========

@@ -53,7 +55,7 @@ mkdir -p all-rocm-wheels
 cp artifacts/rocm-base-wheels/*.whl all-rocm-wheels/ 2>/dev/null || true
 cp artifacts/rocm-vllm-wheel/*.whl all-rocm-wheels/ 2>/dev/null || true

-WHEEL_COUNT=$(find all-rocm-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
+WHEEL_COUNT=$(ls all-rocm-wheels/*.whl 2>/dev/null | wc -l)
 echo "Total wheels to upload: $WHEEL_COUNT"

 if [ "$WHEEL_COUNT" -eq 0 ]; then
@@ -61,18 +63,11 @@ if [ "$WHEEL_COUNT" -eq 0 ]; then
    exit 1
 fi

-# Detect the appropriate manylinux platform tag for any wheel that still
-# carries the generic ``linux_<arch>`` tag, and rename it in place. We use
-# auditwheel via ``apply_manylinux_tag`` (see lib/manylinux.sh) rather than
-# a hard-coded ``manylinux_2_35`` string so that the label tracks the actual
-# glibc symbol versions used by the binaries (and stays correct if the
-# rocm_base image is rebased).
-#
-# The ``linux``/``manylinux`` filter below skips both pre-tagged wheels
-# (e.g. upstream torch) and pure-Python ``-any.whl`` wheels.
+# Rename linux to manylinux in wheel filenames
 for wheel in all-rocm-wheels/*.whl; do
    if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
-        new_wheel="$(apply_manylinux_tag "$wheel")"
+        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
+        mv -- "$wheel" "$new_wheel"
        echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
    fi
 done
@@ -120,7 +115,7 @@ if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]] |
 fi

 # Extract version from vLLM wheel and update version-specific index
-VLLM_WHEEL=$(find all-rocm-wheels -maxdepth 1 -name 'vllm*.whl' 2>/dev/null | head -1)
+VLLM_WHEEL=$(ls all-rocm-wheels/vllm*.whl 2>/dev/null | head -1)
 if [ -n "$VLLM_WHEEL" ]; then
    VERSION=$(unzip -p "$VLLM_WHEEL" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
    echo "Version in wheel: $VERSION"
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Seiji Eicherandkhluu	c44d0c6d66	Patch protobuf for CVE-2026-0994 (#34253 ) Signed-off-by: Seiji Eicher <seiji@anyscale.com> Co-authored-by: Kevin H. Luu <khluu000@gmail.com> (cherry picked from commit `5045d5c983`)	2026-02-11 02:33:40 -08:00
Kunshang Jiandkhluu	83db96d8cd	[XPU][9/N] clean up existing ipex code/doc (#34111 ) Signed-off-by: Kunshang Ji <kunshang.ji@intel.com> (cherry picked from commit `cb9574eb85`)	2026-02-11 02:33:27 -08:00
zofiaandkhluu	dbfb79fe45	[XPU][7/N] enable xpu fp8 moe (#34202 ) Signed-off-by: Zhu, Zufang <zufang.zhu@intel.com> (cherry picked from commit `b482f71e9f`)	2026-02-11 02:33:15 -08:00
Roger Wangandkhluu	b2e1fc3589	[Bugfix][Core] Fix CPU memory leak from Request reference cycle in prefix caching (#34183 ) Signed-off-by: Roger Wang <hey@rogerw.io> (cherry picked from commit `8a5e0e2b2b`)	2026-02-11 02:33:04 -08:00
Gregory Shtrasbergandkhluu	55a1baebc5	[Bugfix][ROCm][GPT-OSS] Use old triton_kernels implementation on ROCm if the new API is not available (#34153 ) Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com> (cherry picked from commit `c60f8e3b49`)	2026-02-11 02:32:52 -08:00
Charlie Fuandkhluu	e1e9841631	[torch.compile][Fusion] Fix attention fusion pass removing kv_udpate op. (#33945 ) Signed-off-by: charlifu <charlifu@amd.com> (cherry picked from commit `bb9f97308d`)	2026-02-11 02:32:41 -08:00
zofiaandkhluu	5bd63387c3	[XPU][6/N] add xpu scaled_mm kernel (#34117 ) Signed-off-by: Zhu, Zufang <zufang.zhu@intel.com> (cherry picked from commit `9bdb06b436`)	2026-02-11 02:32:27 -08:00