pre commit, typo fix

Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>

.buildkite/ci_config.yaml

@@ -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/"

.buildkite/ci_config_intel.yaml

@@ -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"

.buildkite/hardware_tests/amd.yaml

@@ -1,8 +1,6 @@
-group: Hardware - AMD Build 
+group: Hardware
 steps:
   - label: "AMD: :docker: build image"
-    key: image-build-amd
-    depends_on: []
     device: amd_cpu
     no_plugin: true
     commands:
@@ -10,33 +8,21 @@ steps:
       docker build
       --build-arg max_jobs=16
       --build-arg REMOTE_VLLM=1
-      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942;gfx950'
+      --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 run --rm --network=none --entrypoint /bin/bash "rocm/vllm-ci:${BUILDKITE_COMMIT}" -ec '
-        if [ ! -d /vllm-workspace ]; then echo Missing directory: /vllm-workspace >&2; exit 1; fi
-        if [ ! -d /vllm-workspace/tests ]; then echo Missing directory: /vllm-workspace/tests >&2; exit 1; fi
-        if [ ! -d /vllm-workspace/src/vllm ]; then echo Missing directory: /vllm-workspace/src/vllm >&2; exit 1; fi
-        if [ ! -x /vllm-workspace/src/vllm/vllm-rs ]; then echo Missing executable: /vllm-workspace/src/vllm/vllm-rs >&2; exit 1; fi
-        command -v python3
-        command -v uv
-        command -v pytest
-        if ! command -v amd-smi >/dev/null 2>&1 && ! command -v rocminfo >/dev/null 2>&1; then
-          echo No ROCm CLI found in image >&2
-          exit 1
-        fi
-        python3 - <<PY
-      import torch, vllm
-      print(torch.__version__)
-      print(vllm.__version__)
-      PY
-        echo AMD image smoke OK
-      '
     - docker push "rocm/vllm-ci:${BUILDKITE_COMMIT}"
     env:
       DOCKER_BUILDKIT: "1"
+    retry:
+      automatic:
+        - exit_status: -1  # Agent was lost
+          limit: 1
+        - exit_status: -10  # Agent was lost
+          limit: 1
+        - exit_status: 1  # Machine occasionally fail
+          limit: 1

.buildkite/hardware_tests/arm.yaml

@@ -0,0 +1,8 @@
+group: Hardware
+steps:
+  - label: "Arm CPU Test"
+    soft_fail: true
+    device: arm_cpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-cpu-test-arm.sh

.buildkite/hardware_tests/cpu.yaml

@@ -1,146 +0,0 @@
-group: CPU
-depends_on: []
-steps:
-- label: CPU-Kernel Tests
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies:
-  - csrc/cpu/
-  - cmake/cpu_extension.cmake
-  - CMakeLists.txt
-  - 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 "
-      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"
-
-- 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"
-
-- label: CPU-Language Generation and Pooling Model Tests
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies:
-  - csrc/cpu/
-  - vllm/
-  - tests/models/language/generation/
-  - tests/models/language/pooling/
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
-      pytest -x -v -s tests/models/language/generation -m cpu_model
-      pytest -x -v -s tests/models/language/pooling -m cpu_model"
-
-- 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: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies:
-  - csrc/cpu/
-  - vllm/model_executor/layers/quantization/auto_gptq.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
-  - tests/quantization/test_compressed_tensors.py
-  - tests/quantization/test_cpu_wna16.py
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
-      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)
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies: &cpu_distributed_deps
-  - csrc/cpu/shm.cpp
-  - vllm/v1/worker/cpu_worker.py
-  - vllm/v1/worker/gpu_worker.py
-  - vllm/v1/worker/cpu_model_runner.py
-  - vllm/v1/worker/gpu_model_runner.py
-  - 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"
-
-- label: CPU-Multi-Modal Model Tests %N
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies:
-  # - vllm/
-  - vllm/model_executor/layers/rotary_embedding
-  - tests/models/multimodal/generation/
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 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
-
-- label: "Arm CPU Test"
-  depends_on: []
-  soft_fail: false
-  device: arm_cpu
-  no_plugin: true
-  commands: 
-  - bash .buildkite/scripts/hardware_ci/run-cpu-test-arm.sh

.buildkite/hardware_tests/intel.yaml

@@ -1,6 +1,13 @@
 group: Hardware
 depends_on: ~
 steps:
+  - label: "Intel CPU Test"
+    soft_fail: true
+    device: intel_cpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-cpu-test.sh
+  
   - label: "Intel HPU Test"
     soft_fail: true
     device: intel_hpu
@@ -8,3 +15,9 @@ steps:
     commands: 
     - bash .buildkite/scripts/hardware_ci/run-hpu-test.sh
 
+  - label: "Intel GPU Test"
+    soft_fail: true
+    device: intel_gpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-xpu-test.sh

.buildkite/image_build/image_build.sh

@@ -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-${BUILDKITE_BUILD_NUMBER:-local}.json"
-    docker buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --print "${TARGET}" | tee "${BAKE_CONFIG_FILE}" || true
+    BAKE_CONFIG_FILE="bake-config-build-${BUILDKITE_BUILD_NUMBER:-local}.json"
+    docker buildx bake -f "${VLLM_BAKE_FILE}" -f "${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
 
@@ -168,14 +163,16 @@ 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"
-VLLM_BAKE_FILE_PATH="${VLLM_BAKE_FILE_PATH:-docker/docker-bake.hcl}"
-BUILDER_NAME="${BUILDER_NAME:-vllm-builder}"
 CI_HCL_URL="${CI_HCL_URL:-https://raw.githubusercontent.com/vllm-project/ci-infra/main/docker/ci.hcl}"
+VLLM_BAKE_FILE="${VLLM_BAKE_FILE:-docker/docker-bake.hcl}"
+BUILDER_NAME="${BUILDER_NAME:-vllm-builder}"
 CI_HCL_PATH="/tmp/ci.hcl"
 BUILDKIT_SOCKET="/run/buildkit/buildkitd.sock"
 
@@ -183,8 +180,9 @@ prepare_cache_tags
 ecr_login
 
 # Environment info (for docs and human readers)
+#   CI_HCL_URL          - URL to ci.hcl (default: from ci-infra main branch)
 #   VLLM_CI_BRANCH      - ci-infra branch to use (default: main)
-#   VLLM_BAKE_FILE_PATH      - Path to vLLM's bake file (default: docker/docker-bake.hcl)
+#   VLLM_BAKE_FILE      - Path to vLLM's bake file (default: docker/docker-bake.hcl)
 #   BUILDER_NAME        - Name for buildx builder (default: vllm-builder)
 #
 # Build configuration (exported as environment variables for bake):
@@ -192,11 +190,12 @@ 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"
@@ -204,15 +203,18 @@ 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}"
 
 # print build configuration
 echo "--- :mag: Build configuration"
 echo "TARGET: ${TARGET}"
-echo "vLLM bake file: ${VLLM_BAKE_FILE_PATH}"
+echo "CI HCL URL: ${CI_HCL_URL}"
+echo "vLLM bake file: ${VLLM_BAKE_FILE}"
 echo "BUILDER_NAME: ${BUILDER_NAME}"
-echo "CI_HCL_URL: ${CI_HCL_URL}"
+echo "CI_HCL_PATH: ${CI_HCL_PATH}"
 echo "BUILDKIT_SOCKET: ${BUILDKIT_SOCKET}"
 
 echo "--- :mag: Cache tags"
@@ -225,11 +227,11 @@ check_and_skip_if_image_exists
 
 echo "--- :docker: Setting up Docker buildx bake"
 echo "Target: ${TARGET}"
-echo "vLLM bake file: ${VLLM_BAKE_FILE_PATH}"
-echo "CI HCL path: ${CI_HCL_PATH}"
+echo "CI HCL URL: ${CI_HCL_URL}"
+echo "vLLM bake file: ${VLLM_BAKE_FILE}"
 
-if [[ ! -f "${VLLM_BAKE_FILE_PATH}" ]]; then
-    echo "Error: vLLM bake file not found at ${VLLM_BAKE_FILE_PATH}"
+if [[ ! -f "${VLLM_BAKE_FILE}" ]]; then
+    echo "Error: vLLM bake file not found at ${VLLM_BAKE_FILE}"
     echo "Make sure you're running from the vLLM repository root"
     exit 1
 fi
@@ -238,19 +240,15 @@ echo "--- :arrow_down: Downloading ci.hcl"
 curl -sSfL -o "${CI_HCL_PATH}" "${CI_HCL_URL}"
 echo "Downloaded to ${CI_HCL_PATH}"
 
-if [[ ! -f "${CI_HCL_PATH}" ]]; then
-    echo "Error: ci.hcl not found at ${CI_HCL_PATH}"
-    exit 1
-fi
-
 setup_buildx_builder
 
+# Compute parent commit for cache fallback (if not already set)
 resolve_parent_commit
 export PARENT_COMMIT
 
 print_bake_config
 
 echo "--- :docker: Building ${TARGET}"
-docker --debug buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --progress plain "${TARGET}"
+docker --debug buildx bake -f "${VLLM_BAKE_FILE}" -f "${CI_HCL_PATH}" --progress plain "${TARGET}"
 
 echo "--- :white_check_mark: Build complete"

.buildkite/image_build/image_build.yaml

@@ -3,63 +3,9 @@ steps:
   - label: ":docker: Build image"
     key: image-build
     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_LATEST; fi
     retry:
       automatic:
         - exit_status: -1  # Agent was lost
@@ -95,7 +41,7 @@ steps:
           limit: 2
         - exit_status: -10  # Agent was lost
           limit: 2
-
+  
   - label: ":docker: Build CPU arm64 image"
     key: cpu-arm64-image-build
     depends_on: []
@@ -110,21 +56,3 @@ 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

.buildkite/image_build/image_build_arm64.sh

@@ -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

.buildkite/image_build/image_build_cpu.sh

@@ -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

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -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

.buildkite/image_build/image_build_hpu.sh

@@ -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

.buildkite/image_build/image_build_torch_nightly.sh

@@ -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"

.buildkite/image_build/image_build_xpu.sh

@@ -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

.buildkite/intel_jobs/engine_intel.yaml

@@ -1,21 +0,0 @@
-group: Engine Intel
-depends_on:
-  - image-build-xpu
-steps:
-- label: Engine (1 GPU)
-  timeout_in_minutes: 30
-  device: intel_gpu
-  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'

.buildkite/intel_jobs/kernels_intel.yaml

@@ -1,21 +0,0 @@
-group: Kernels Intel
-depends_on: 
-  - image-build-xpu
-steps:
-- label: vLLM IR Tests
-  timeout_in_minutes: 30
-  device: intel_gpu
-  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'

.buildkite/intel_jobs/lora_intel.yaml

@@ -1,135 +0,0 @@
-group: LoRA Intel
-depends_on:
-  - image-build-xpu
-steps:
-- label: LoRA Runtime + Utils
-  timeout_in_minutes: 45
-  device: intel_gpu
-  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
-  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
-  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
-  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
-  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
-  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'

.buildkite/intel_jobs/misc_intel.yaml

@@ -1,55 +0,0 @@
-group: Miscellaneous Intel
-depends_on:
-  - image-build-xpu
-steps:
-- label: V1 Core + KV + Metrics
-  timeout_in_minutes: 30
-  device: intel_gpu
-  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
-  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/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
-      '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'

.buildkite/intel_jobs/test-intel.yaml

@@ -1,87 +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
-    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
-        '
-  - label: "XPU V1 test"
-    depends_on:
-      - image-build-xpu
-    timeout_in_minutes: 30
-    device: intel_gpu
-    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 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 &&
-        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
-    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/openai/chat_completion/test_audio_in_video.py &&
-        pytest -v -s benchmarks/test_serve_cli.py'

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -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:

.buildkite/lm-eval-harness/configs/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.yaml

@@ -1,15 +0,0 @@
-model_name: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.695
-  - name: "exact_match,flexible-extract"
-    value: 0.447
-limit: 1319
-num_fewshot: 5
-max_model_len: 262144
-enforce_eager: false
-apply_chat_template: true
-fewshot_as_multiturn: true
-trust_remote_code: true

.buildkite/lm-eval-harness/configs/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8.yaml

@@ -1,19 +0,0 @@
-model_name: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.7142
-  - name: "exact_match,flexible-extract"
-    value: 0.4579
-env_vars:
-  VLLM_USE_FLASHINFER_MOE_FP8: "1"
-  VLLM_FLASHINFER_MOE_BACKEND: "throughput"
-limit: 1319
-num_fewshot: 5
-max_model_len: 262144
-kv_cache_dtype: fp8
-enforce_eager: false
-apply_chat_template: true
-fewshot_as_multiturn: true
-trust_remote_code: true

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -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:

.buildkite/lm-eval-harness/configs/Qwen3-235B-A22B-Instruct-2507-FP8.yaml

@@ -1,7 +1,4 @@
 model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
-required_gpu_arch:
-  - gfx942
-  - gfx950
 tasks:
   - name: "mmlu_pro"
     metrics:

.buildkite/lm-eval-harness/configs/SparseLlama3.1_2of4_fp8_compressed.yaml

@@ -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 

.buildkite/lm-eval-harness/configs/models-large-hopper.txt

@@ -1,2 +1 @@
 Qwen3-235B-A22B-Instruct-2507-FP8.yaml
-NVIDIA-Nemotron-3-Nano-30B-A3B-FP8.yaml

.buildkite/lm-eval-harness/configs/models-large-rocm-fp8.txt

@@ -1 +0,0 @@
-Qwen3-235B-A22B-Instruct-2507-FP8.yaml

.buildkite/lm-eval-harness/configs/models-large.txt

@@ -3,4 +3,3 @@ Meta-Llama-3-70B-Instruct.yaml
 Mixtral-8x7B-Instruct-v0.1.yaml
 Qwen2-57B-A14-Instruct.yaml
 DeepSeek-V2-Lite-Chat.yaml
-NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.yaml

.buildkite/lm-eval-harness/configs/models-small-rocm.txt

@@ -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

.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh

@@ -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

.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh

@@ -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``

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -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``

.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh

@@ -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"
         ;;

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -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
 
 
@@ -65,9 +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"
-
     env_vars = eval_config.get("env_vars", None)
     with scoped_env_vars(env_vars):
         results = lm_eval.simple_evaluate(
@@ -90,40 +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)
@@ -138,8 +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

.buildkite/performance-benchmarks/README.md

@@ -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"
         },

.buildkite/performance-benchmarks/scripts/compare-json-results.py

@@ -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():

.buildkite/performance-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -393,7 +393,7 @@ if __name__ == "__main__":
     with open(results_folder / md_file, "w") as f:
         results = read_markdown(
             "../.buildkite/performance-benchmarks/"
-            "performance-benchmarks-descriptions.md"
+            + "performance-benchmarks-descriptions.md"
         )
         results = results.format(
             latency_tests_markdown_table=latency_md_table,

.buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh

@@ -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 [[ $gpu_count -gt 0 ]]; then
@@ -35,9 +25,9 @@ check_gpus() {
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-
+  
   declare -g arch_suffix=''
-
+  
   if command -v nvidia-smi; then
     declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
   elif command -v amd-smi; then
@@ -54,7 +44,7 @@ check_cpus() {
   declare -g numa_count=$(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() {
@@ -190,318 +181,19 @@ 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"
-}
+run_latency_tests() {
+  # run latency tests using `vllm bench latency` command
+  # $1: a json file specifying latency test cases
 
-extract_metric_ms() {
-  local metric_name=$1
-  local json_file=$2
+  local latency_test_file
+  latency_test_file=$1
 
-  [[ -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)
-  # $2: a json file specifying test cases
-
-  local test_type=$1
-  local test_file=$2
-
-  # Iterate over tests
-  jq -c '.[]' "$test_file" | while read -r params; do
+  # Iterate over latency tests
+  jq -c '.[]' "$latency_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
     test_name=$(echo "$params" | jq -r '.test_name')
-    if [[ ! "$test_name" =~ ^${test_type}_ ]]; then
-      echo "In ${test_type}-test.json, test_name must start with \"${test_type}_\"."
+    if [[ ! "$test_name" =~ ^latency_ ]]; then
+      echo "In latency-test.json, test_name must start with \"latency_\"."
       exit 1
     fi
 
@@ -512,15 +204,15 @@ run_benchmark_tests() {
     fi
 
     # get arguments
-    bench_params=$(echo "$params" | jq -r '.parameters')
-    bench_args=$(json2args "$bench_params")
-    bench_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    bench_envs=$(json2envs "$bench_environment_variables")
+    latency_params=$(echo "$params" | jq -r '.parameters')
+    latency_args=$(json2args "$latency_params")
+    latency_environment_variables=$(echo "$params" | jq -r '.environment_variables')
+    latency_envs=$(json2envs "$latency_environment_variables")
 
     # check if there is enough GPU to run the test
-    tp=$(echo "$bench_params" | jq -r '.tensor_parallel_size')
+    tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
     if [[ "$ON_CPU" == "1" ]]; then
-      pp=$(echo "$bench_params" | jq -r '.pipeline_parallel_size // 1')
+      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size // 1')
       world_size=$(($tp*$pp))
       if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
         echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -533,42 +225,118 @@ run_benchmark_tests() {
       fi
     fi
 
-    bench_command=" $bench_envs vllm bench $test_type \
+    latency_command=" $latency_envs vllm bench latency \
       --output-json $RESULTS_FOLDER/${test_name}.json \
-      $bench_args"
+      $latency_args"
 
     echo "Running test case $test_name"
-    echo "${test_type^} command: $bench_command"
+    echo "Latency command: $latency_command"
 
-    # recording benchmarking command and GPU command
+    # recoding benchmarking command ang GPU command
     jq_output=$(jq -n \
-      --arg command "$bench_command" \
+      --arg latency "$latency_command" \
       --arg gpu "$gpu_type" \
-      --arg test_type "$test_type" \
       '{
-        ($test_type + "_command"): $command,
+        latency_command: $latency,
         gpu_type: $gpu
       }')
     echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
 
     # run the benchmark
-    eval "$bench_command"
+    eval "$latency_command"
 
     kill_gpu_processes
 
   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_throughput_tests() {
+  # run throughput tests using `vllm bench throughput`
+  # $1: a json file specifying throughput test cases
 
-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='
+  local throughput_test_file
+  throughput_test_file=$1
+
+  # Iterate over throughput tests
+  jq -c '.[]' "$throughput_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 [[ ! "$test_name" =~ ^throughput_ ]]; then
+      echo "In throughput-test.json, test_name must start with \"throughput_\"."
+      exit 1
+    fi
+
+    # if TEST_SELECTOR is set, only run the test cases that match the selector
+    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
+      echo "Skip test case $test_name."
+      continue
+    fi
+
+    # get arguments
+    throughput_params=$(echo "$params" | jq -r '.parameters')
+    throughput_args=$(json2args "$throughput_params")
+    throughput_environment_variables=$(echo "$params" | jq -r '.environment_variables')
+    throughput_envs=$(json2envs "$throughput_environment_variables")
+
+    # check if there is enough GPU to run the test
+    tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
+    if [[ "$ON_CPU" == "1" ]]; then
+      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size // 1')
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
+        continue
+      fi
+    else
+      if [[ $gpu_count -lt $tp ]]; then
+        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+        continue
+      fi
+    fi
+
+    throughput_command=" $throughput_envs vllm bench throughput \
+      --output-json $RESULTS_FOLDER/${test_name}.json \
+      $throughput_args"
+
+    echo "Running test case $test_name"
+    echo "Throughput command: $throughput_command"
+    # recoding benchmarking command ang GPU command
+    jq_output=$(jq -n \
+      --arg command "$throughput_command" \
+      --arg gpu "$gpu_type" \
+      '{
+        throughput_command: $command,
+        gpu_type: $gpu
+      }')
+    echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
+
+    # run the benchmark
+    eval "$throughput_command"
+
+    kill_gpu_processes
+
+  done
+}
+
+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=$1
+
+  # Iterate over serving tests
+  jq -c '
     if type == "array" then
       # Plain format: test cases array
       .[]
@@ -590,50 +358,7 @@ merge_serving_tests_stream() {
     else
       error("Unsupported serving test file format: must be array or object with .tests")
     end
-  '
-
-  jq -c "$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 +377,10 @@ 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 +412,14 @@ 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 +427,7 @@ 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 +438,6 @@ 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
@@ -762,48 +447,34 @@ run_serving_tests() {
       fi
     fi
 
-    # save the compilation mode and optimization level on the serving results
-    # whenever they are set
-    compilation_config_mode=$(echo "$server_params" | jq -r '."compilation_config.mode" // empty')
-    optimization_level=$(echo "$server_params" | jq -r '.optimization_level // empty')
-
     # iterate over different QPS
     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
+        # pass the tensor parallel size to the client so that it can be displayed
+        # on the benchmark dashboard
         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 \
-          $client_args_effective $client_remote_args "
+          --metadata "tensor_parallel_size=$tp" \
+          $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=$(jq -n \
@@ -818,23 +489,15 @@ run_serving_tests() {
         echo "$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 [[ "$ON_CPU" == "1" ]]; then
@@ -844,13 +507,7 @@ main() {
      check_gpus
      ARCH="$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)
@@ -871,24 +528,17 @@ 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}"
 
   # 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
 }

.buildkite/performance-benchmarks/tests/latency-tests-hpu.json

@@ -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": ""
-        }
     }
 ]

.buildkite/performance-benchmarks/tests/serving-tests-arm64-cpu.json

@@ -36,7 +36,6 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
-      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -128,4 +127,4 @@
       }
     }
   ]
-}
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-asr.json

@@ -1,38 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-    "server_environment_variables": {
-      "VLLM_RPC_TIMEOUT": 100000,
-      "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": {}
-    }
-  ]
-}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-embed.json

@@ -1,41 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [
-      32,
-      64,
-      128
-    ],
-    "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,
-      "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": {}
-    }
-  ]
-}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-text.json

@@ -1,289 +0,0 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "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,
-      "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
-      }
-    }
-  ]
-}

.buildkite/performance-benchmarks/tests/serving-tests-cpu.json

@@ -26,7 +26,6 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
-      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -73,6 +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": {
@@ -95,6 +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": {
@@ -118,25 +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
       }
     }
   ]

.buildkite/performance-benchmarks/tests/serving-tests-hpu.json

@@ -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
         }
     }

.buildkite/performance-benchmarks/tests/serving-tests.json

@@ -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
-      }
-    }
-  ]
-}
+]

.buildkite/performance-benchmarks/tests/throughput-tests-hpu.json

@@ -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": ""
-        }
     }
 ]

.buildkite/scripts/annotate-build-artifact.sh

@@ -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'

.buildkite/scripts/annotate-release.sh

@@ -0,0 +1,61 @@
+#!/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-manylinux1_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
+
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
+\`\`\`
+
+To download the wheel (by version):
+\`\`\`
+aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
+
+aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu129/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu130/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux1_x86_64.whl .
+\`\`\`
+
+To download and upload the image:
+
+\`\`\`
+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:${BUILDKITE_COMMIT}-rocm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${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:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${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}-rocm vllm/vllm-openai:rocm
+docker tag vllm/vllm-openai:rocm vllm/vllm-openai:latest-rocm
+docker tag vllm/vllm-openai:rocm vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+docker push vllm/vllm-openai:latest-rocm
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+
+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}
+\`\`\`
+EOF 

.buildkite/scripts/annotate-rocm-release.sh

@@ -3,33 +3,25 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 #
 # 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"
+# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.1-complete -> extracts "7.1"
 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=$(grep '^ARG PYTORCH_ROCM_ARCH=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTORCH_ROCM_ARCH=//')
-
-# Get release version, default to 1.0.0.dev for nightly/per-commit builds
-RELEASE_VERSION=$(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)
+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")
 
 # 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"
+S3_URL="https://${S3_BUCKET}.s3.${S3_REGION}.amazonaws.com"
+ROCM_PATH="rocm/${BUILDKITE_COMMIT}"
 
-# Format ROCm version for path (e.g., "7.1" -> "rocm710")
-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
+## :rocm: ROCm Wheel Release
+
 ### Build Configuration
 | Setting | Value |
 |---------|-------|
@@ -42,72 +34,41 @@ buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' <<
 ### :package: Installation
 
 **Install from this build (by commit):**
-
 \`\`\`bash
-pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
+uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/{rocm_variant}/
 
-# Example for ROCm ${ROCM_VERSION}:
-pip install vllm --extra-index-url ${S3_URL}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
+# Example:
+uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/rocm700/
 \`\`\`
 
 **Install from nightly (if published):**
-
 \`\`\`bash
-pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
+uv pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/
 \`\`\`
 
 ### :floppy_disk: Download Wheels Directly
 
 \`\`\`bash
 # List all ROCm wheels
-aws s3 ls s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/
+aws s3 ls s3://${S3_BUCKET}/${ROCM_PATH}/
+
 # Download specific wheels
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/vllm-*.whl .
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torch-*.whl .
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton-*.whl .
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton-kernels-*.whl .
-aws s3 cp s3://${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}/flash-attn-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/vllm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torch-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/triton_rocm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torchvision-*.whl .
+aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/amdsmi-*.whl .
 \`\`\`
 
 ### :gear: Included Packages
 - **vllm**: vLLM with ROCm support
 - **torch**: PyTorch built for ROCm ${ROCM_VERSION}
-- **triton**: Triton
-- **triton-kernels**: Triton kernels
+- **triton_rocm**: Triton built for ROCm
 - **torchvision**: TorchVision for ROCm PyTorch
-- **torchaudio**: Torchaudio for ROCm PyTorch
 - **amdsmi**: AMD SMI Python bindings
-- **amd_aiter**: Aiter for ROCm
-- **flash-attn**: Flash Attention for ROCm
 
 ### :warning: Notes
 - These wheels are built for **ROCm ${ROCM_VERSION}** and will NOT work with CUDA GPUs
 - Supported GPU architectures: ${PYTORCH_ROCM_ARCH}
 - Platform: Linux x86_64 only
-
-### :package: Docker Image Release
-
-To download and upload the image:
-
-\`\`\`
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${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:${BUILDKITE_COMMIT}-base
-docker tag vllm/vllm-openai-rocm:${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${RELEASE_VERSION}-base
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${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}
-\`\`\`
-
 EOF

.buildkite/scripts/cache-rocm-base-wheels.sh

@@ -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 "========================================"

.buildkite/scripts/check-ray-compatibility.sh

@@ -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

.buildkite/scripts/cherry-pick-from-milestone.sh

@@ -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 \

.buildkite/scripts/ci-fetch-log.sh

@@ -1,74 +0,0 @@
-#!/bin/bash
-# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
-#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
-#
-# Downloads the raw log for a Buildkite job from the public, unauthenticated
-# /organizations/<org>/pipelines/<pipeline>/builds/<n>/jobs/<uuid>/download
-# endpoint, then strips ANSI/timestamps via ci-clean-log.sh.
-#
-# Find <build_number> and <job_uuid> via:
-#   gh pr checks <PR> --repo vllm-project/vllm
-# Each failing row's URL is .../builds/<build_number>#<job_uuid>.
-#
-# Default output path: ci-<build>-<uuid_first_13_chars>.log (e.g.
-# ci-68478-019e6b07-daae.log). Jobs in the same build share the UUID's
-# first 8 chars, so the second segment is needed for uniqueness when
-# fetching multiple jobs in parallel. The script refuses to overwrite an
-# existing output file; pass an explicit path or set CI_FETCH_LOG_FORCE=1
-# to override.
-
-set -euo pipefail
-
-ORG="vllm"
-PIPELINE="ci"
-
-usage() {
-    echo "Usage: $0 <buildkite_job_url> [output_file]"
-    echo "       $0 <build_number> <job_uuid> [output_file]"
-    exit 1
-}
-
-if [ $# -lt 1 ]; then usage; fi
-
-if [[ "$1" == https://* ]]; then
-    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
-    JOB=$(echo "$1" | grep -oE '[0-9a-f]{8}-[0-9a-f-]+' | head -n 1)
-    OUT="${2:-}"
-else
-    if [ $# -lt 2 ]; then usage; fi
-    BUILD="$1"
-    JOB="$2"
-    OUT="${3:-}"
-fi
-
-if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
-    echo "Could not parse build number or job UUID from: $1" >&2
-    usage
-fi
-
-# Jobs in the same build share the UUID's first segment, so include the
-# second segment (chars 9-13, e.g. "019e6b07-daae") to keep default filenames
-# unique when fetching multiple jobs from one build in parallel.
-if [ -z "$OUT" ]; then
-    OUT="ci-${BUILD}-${JOB:0:13}.log"
-fi
-
-if [ -e "$OUT" ] && [ -z "${CI_FETCH_LOG_FORCE:-}" ]; then
-    echo "Refusing to overwrite existing $OUT (set CI_FETCH_LOG_FORCE=1 or pass an explicit output path)." >&2
-    exit 1
-fi
-
-COOKIES=$(mktemp)
-trap 'rm -f "$COOKIES"' EXIT
-
-# Buildkite issues a session cookie on first hit; subsequent /download needs it.
-curl -fsSL -c "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
-
-curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
-    -o "$OUT"
-
-bash "$(dirname "$0")/ci-clean-log.sh" "$OUT"
-
-echo "$OUT"

.buildkite/scripts/cleanup-nightly-builds.sh

@@ -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

.buildkite/scripts/detect-manylinux-tag.py

@@ -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())

.buildkite/scripts/docker-build-metadata-args.sh

@@ -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}"

.buildkite/scripts/generate-and-upload-nightly-index.sh

@@ -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

.buildkite/scripts/generate-nightly-index.py

@@ -112,7 +112,7 @@ def parse_from_filename(file: str) -> WheelFileInfo:
 
 def generate_project_list(subdir_names: list[str], comment: str = "") -> str:
     """
-    Generate project list HTML content linking to each project & variant subdirectory.
+    Generate project list HTML content linking to each project & variant sub-directory.
     """
     href_tags = []
     for name in sorted(subdir_names):
@@ -168,23 +168,23 @@ def generate_index_and_metadata(
         comment (str | None): Optional comment to include in the generated HTML files.
 
     First, parse all wheel files to extract metadata.
-    We need to collect all wheel files for each variant, and generate an index for it (in a subdirectory).
+    We need to collect all wheel files for each variant, and generate an index for it (in a sub-directory).
     The index for the default variant (if any) is generated in the root index directory.
 
     If `default_variant` is provided, all wheels must have variant suffixes, and the default variant index
     is purely a copy of the corresponding variant index, with only the links adjusted.
     Otherwise, all wheels without variant suffixes are treated as the default variant.
 
-    If `alias_to_default` is provided, an additional alias subdirectory is created, it has the same content
+    If `alias_to_default` is provided, an additional alias sub-directory is created, it has the same content
     as the default variant index, but the links are adjusted accordingly.
 
     Index directory structure:
         index_base_dir/ (hosted at wheels.vllm.ai/{nightly,$commit,$version}/)
-            index.html  # project list, linking to "vllm/" and other packages, and all variant subdirectories
+            index.html  # project list, linking to "vllm/" and other packages, and all variant sub-directories
             vllm/
                 index.html # package index, pointing to actual files in wheel_base_dir (relative path)
                 metadata.json # machine-readable metadata for all wheels in this package
-            cpu/ # cpu variant subdirectory
+            cpu/ # cpu variant sub-directory
                 index.html
                 vllm/
                     index.html
@@ -194,7 +194,7 @@ def generate_index_and_metadata(
                 vllm/
                     index.html
                     metadata.json
-            cu130/ # cu130 variant subdirectory
+            cu130/ # cu130 variant sub-directory
                 index.html
                 vllm/
                     index.html

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

@@ -1,400 +1,244 @@
 #!/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
 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
-}
-
-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
+}
+
+# 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="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() {
-  # docker run uses --rm, so the container is normally already gone when the
-  # EXIT trap runs. Cleanup is best-effort and must not affect the test result.
-  docker rm -f "${container_name}" >/dev/null 2>&1 || true
+   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
+trap remove_docker_container EXIT
 
-on_exit() {
-  local exit_code=$?
-  remove_docker_container
-  exit "$exit_code"
-}
-trap on_exit EXIT
-
-# --- Prepare commands ---
 echo "--- Running container"
 
 HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"
 
-# ---- 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 $0"
+commands=$@
+echo "Commands:$commands"
+
+commands=${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
 
-echo "Raw commands: $commands"
+commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"pytest -v -s compile/test_basic_correctness.py"}
 
-# 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"
+if [[ $commands == *"pytest -v -s lora"* ]]; then
+  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
 fi
 
-echo "Final commands: $commands"
+#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
+
+# --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
+
+
+PARALLEL_JOB_COUNT=8
 MYPYTHONPATH=".."
 
-# Verify GPU access
+# 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
-
-# --- 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."
+# check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs.
+if [[ $commands == *"--shard-id="* ]]; then
+  # assign job count as the number of shards used
+  commands=$(echo "$commands" | sed -E "s/--num-shards[[:blank:]]*=[[:blank:]]*[0-9]*/--num-shards=${PARALLEL_JOB_COUNT} /g" | sed 's/ \\ / /g')
+  for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
+    # assign shard-id for each shard
+    commands_gpu=$(echo "$commands" | sed -E "s/--shard-id[[:blank:]]*=[[:blank:]]*[0-9]*/--shard-id=${GPU} /g" | sed 's/ \\ / /g')
+    echo "Shard ${GPU} commands:$commands_gpu"
+    echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
+    docker run \
+        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+        --network=host \
+        --shm-size=16gb \
+        --group-add "$render_gid" \
+        --rm \
+        -e HIP_VISIBLE_DEVICES="${GPU}" \
+        -e HF_TOKEN \
+        -e AWS_ACCESS_KEY_ID \
+        -e AWS_SECRET_ACCESS_KEY \
+        -v "${HF_CACHE}:${HF_MOUNT}" \
+        -e "HF_HOME=${HF_MOUNT}" \
+        -e "PYTHONPATH=${MYPYTHONPATH}" \
+        --name "${container_name}_${GPU}" \
+        "${image_name}" \
+        /bin/bash -c "${commands_gpu}" \
+        |& while read -r line; do echo ">>Shard $GPU: $line"; done &
+    PIDS+=($!)
+  done
+  #wait for all processes to finish and collect exit codes
+  for pid in "${PIDS[@]}"; do
+    wait "${pid}"
+    STATUS+=($?)
+  done
+  at_least_one_shard_with_tests=0
+  for st in "${STATUS[@]}"; do
+    if [[ ${st} -ne 0 ]] && [[ ${st} -ne 5 ]]; then
+      echo "One of the processes failed with $st"
+      exit "${st}"
+    elif [[ ${st} -eq 5 ]]; then
+      echo "Shard exited with status 5 (no tests collected) - treating as success"
+    else # This means st is 0
+      at_least_one_shard_with_tests=1
     fi
-
-    for i in "${!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"
-  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
+  done
+  if [[ ${#STATUS[@]} -gt 0 && ${at_least_one_shard_with_tests} -eq 0 ]]; then
+    echo "All shards reported no tests collected. Failing the build."
+    exit 1
   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 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 "PYTORCH_ROCM_ARCH=" \
-    --name "${container_name}" \
-    "${image_name}" \
-    /bin/bash -c "${commands}"
-
-  exit_code=$?
-  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

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

@@ -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

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

@@ -1,39 +0,0 @@
-#!/bin/bash
-set -euox pipefail
-export VLLM_CPU_CI_ENV=0
-export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
-
-MODE=${1:-all}
-
-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

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

@@ -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.

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -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.

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

@@ -2,19 +2,119 @@
 
 # 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 -euox pipefail
+set -ex
 
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
+# used for TP/PP E2E test
+OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
-IMAGE_NAME="cpu-test-$NUMA_NODE"
-TIMEOUT_VAL=$1
-TEST_COMMAND=$2
 
-# building the docker image
-echo "--- :docker: Building Docker image"
-docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .
+export CMAKE_BUILD_PARALLEL_LEVEL=32
+
+# Setup cleanup
+remove_docker_container() {
+    set -e;
+    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
+}
+trap remove_docker_container EXIT
+remove_docker_container
+
+# Try building the docker image
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --progress plain --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --progress plain --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --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\"; ${TEST_COMMAND}"
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
+
+function cpu_tests() {
+  set -e
+  export NUMA_NODE=$2
+
+  # list packages
+  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
+    set -e
+    pip list"
+
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pip list"
+
+  # offline inference
+  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
+    set -e
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
+
+  # Run kernel tests
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+    pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
+    pytest -x -v -s tests/kernels/test_onednn.py"
+
+  # Run basic model test
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    # Note: disable until supports V1
+    # pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
+    # pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
+
+    pytest -x -v -s tests/models/language/generation -m cpu_model
+    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model
+
+    pytest -x -v -s tests/models/language/pooling -m cpu_model
+    pytest -x -v -s tests/models/multimodal/generation \
+                --ignore=tests/models/multimodal/generation/test_pixtral.py \
+                -m cpu_model"
+
+  # Run compressed-tensor test
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -x -s -v \
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
+
+  # Run AWQ/GPTQ test
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -x -s -v \
+    tests/quantization/test_cpu_wna16.py"
+
+  # Run multi-lora tests
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -x -s -v \
+    tests/lora/test_qwenvl.py"
+
+  # online serving: tp+pp
+  docker exec cpu-test-"$NUMA_NODE" bash -c '
+    set -e
+    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
+    server_pid=$!
+    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 $server_pid &'
+
+  # online serving: tp+dp
+  docker exec cpu-test-"$NUMA_NODE" bash -c '
+    set -e
+    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
+    server_pid=$!
+    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 &'
+}
+
+# All of CPU tests are expected to be finished less than 40 mins.
+export -f cpu_tests
+timeout 2.5h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -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
 '

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

@@ -1,49 +1,21 @@
 #!/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 hpu-plugin-v1-test-env -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 VLLM_TARGET_DEVICE=empty pip install --no-build-isolation -e .
+RUN VLLM_TARGET_DEVICE=empty pip install .
 RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git
 
 # install development dependencies (for testing)
@@ -64,20 +36,15 @@ 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 hpu-plugin-v1-test || true; }
 trap 'remove_docker_containers; exit $EXITCODE;' EXIT
 remove_docker_containers
 
 echo "Running HPU plugin v1 test"
-docker run --rm --runtime=habana --name="${container_name}" --network=host \
+docker run --rm --runtime=habana --name=hpu-plugin-v1-test --network=host \
   -e HABANA_VISIBLE_DEVICES=all \
-  -e VLLM_SKIP_WARMUP=true \
-  -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
-  -e PT_HPU_LAZY_MODE=1 \
-  "${image_name}" \
-  /bin/bash -c '
-  cd vllm; timeout 120s python -u examples/basic/offline_inference/generate.py --model facebook/opt-125m
-'
+  hpu-plugin-v1-test-env \
+  /bin/bash "/workspace/vllm-gaudi/tests/upstream_tests/ci_tests.sh"
 
 EXITCODE=$?
 if [ $EXITCODE -eq 0 ]; then

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

@@ -1,382 +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=""
-if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
-  commands="${VLLM_TEST_COMMANDS}"
-  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: $commands"
-commands=$(re_quote_pytest_markers "$commands")
-echo "After re-quoting: $commands"
-commands=$(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: ${IMAGE}"
-
-if docker image inspect "${IMAGE}" >/dev/null 2>&1; then
-  echo "Image already exists locally, skipping pull"
-else
-  echo "Image not found locally, waiting for lock..."
-
-  flock /tmp/docker-pull.lock bash -c "
-    if docker image inspect '${IMAGE}' >/dev/null 2>&1; then
-      echo 'Image already pulled by another runner'
-    else
-      echo 'Pulling image...'
-      timeout 900 docker pull '${IMAGE}'
-    fi
-  "
-
-  echo "Pull step completed"
-fi
-
-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 missing before container creation, pulling again...'
-    timeout 900 docker pull "${IMAGE}"
-  fi
-
-  docker create \
-    --device /dev/dri:/dev/dri \
-    --net=host \
-    --ipc=host \
-    --privileged \
-    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v "${HOME}/.cache/huggingface:/root/.cache/huggingface" \
-    --entrypoint='' \
-    -e HF_TOKEN \
-    -e ZE_AFFINITY_MASK \
-    -e 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}"

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

@@ -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}" \

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -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" \

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -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" \

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

@@ -0,0 +1,52 @@
+#!/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 Intel/Qwen2.5-0.5B-W4A16-G128-AutoRound-LLMC-TEST-ONLY --enforce-eager
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    cd tests
+    pytest -v -s v1/core
+    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
+    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
+'

.buildkite/scripts/install-kv-connectors.sh

@@ -1,39 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-set -euo pipefail
-
-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

.buildkite/scripts/lib/manylinux.sh

@@ -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"
-}

.buildkite/scripts/lib/select-python.sh

@@ -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)"
-}

.buildkite/scripts/publish-release-images.sh

@@ -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}"

.buildkite/scripts/push-nightly-builds-rocm.sh

@@ -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"

.buildkite/scripts/push-nightly-builds.sh

@@ -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

.buildkite/scripts/run-prime-rl-test.sh

@@ -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!"

.buildkite/scripts/run-rust-frontend-cargo-ci.sh

@@ -1,156 +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="$CARGO_HOME/bin" sh
-}
-
-run_style_clippy() {
-  install_cargo_sort
-
-  log_section "Checking Rust formatting"
-  cargo fmt --manifest-path rust/Cargo.toml --all -- --check
-
-  log_section "Checking Cargo.toml ordering"
-  cargo sort --workspace --check rust
-
-  log_section "Running clippy"
-  cargo clippy \
-    --manifest-path rust/Cargo.toml \
-    --workspace \
-    --all-targets \
-    --all-features \
-    --locked \
-    -- \
-    -D warnings
-}
-
-run_tests() {
-  install_uv
-  install_cargo_nextest
-
-  log_section "Running cargo nextest"
-  cargo nextest run \
-    --manifest-path rust/Cargo.toml \
-    --workspace \
-    --all-features \
-    --locked \
-    --no-fail-fast
-}
-
-install_protoc
-install_rust_toolchain
-
-case "$MODE" in
-  style-clippy)
-    run_style_clippy
-    ;;
-  test)
-    run_tests
-    ;;
-esac

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh

@@ -43,23 +43,22 @@ trap cleanup EXIT
 
 for BACK in "${BACKENDS[@]}"; do
   VLLM_DEEP_GEMM_WARMUP=skip \
+  VLLM_ALL2ALL_BACKEND=$BACK \
   vllm serve "$MODEL" \
     --enforce-eager \
     --tensor-parallel-size 2 \
     --data-parallel-size 2 \
     --enable-expert-parallel \
     --enable-eplb \
-    --eplb-config '{"use_async": false}' \
     --trust-remote-code \
     --max-model-len 2048 \
-    --all2all-backend "$BACK" \
-    --port "$PORT" &
+    --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}")

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh

@@ -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=

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh

@@ -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}")

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh

@@ -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

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -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}")

.buildkite/scripts/tool_call/run-bfcl-eval.sh

@@ -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

.buildkite/scripts/tpu/docker_run_bm.sh

@@ -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 \

.buildkite/scripts/tpu/run_bm.sh

@@ -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..."

.buildkite/scripts/upload-nightly-wheels.sh

@@ -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

.buildkite/scripts/upload-release-wheels-pypi.sh

@@ -1,80 +0,0 @@
-#!/usr/bin/env bash
-
-set -e
-
-BUCKET="vllm-wheels"
-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)
-
-echo "Release version from Buildkite: $RELEASE_VERSION"
-
-if [[ -z "$GIT_VERSION" ]]; then
-    echo "[FATAL] Not on a git tag, cannot create release."
-    exit 1
-else
-    echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
-fi
-# sanity check for version mismatch
-if [[ "$RELEASE_VERSION" != "$GIT_VERSION" ]]; then
-  if [[ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]]; then
-    echo "[WARNING] Force release and ignore version mismatch"
-  else
-    echo "[FATAL] Release version from Buildkite does not match Git version."
-    exit 1
-  fi
-fi
-PURE_VERSION=${RELEASE_VERSION#v} # remove leading 'v'
-
-# check pypi token
-if [[ -z "$PYPI_TOKEN" ]]; then
-  echo "[FATAL] PYPI_TOKEN is not set."
-  exit 1
-else
-  export TWINE_USERNAME="__token__"
-  export TWINE_PASSWORD="$PYPI_TOKEN"
-fi
-
-set -x # avoid printing secrets above
-
-# install uv if not already available
-if ! command -v uv &> /dev/null; then
-  curl -LsSf https://astral.sh/uv/install.sh | UV_VERSION=0.11.14 sh
-  export PATH="$HOME/.local/bin:$PATH"
-fi
-
-# install twine and sdist build prerequisites using uv with Python 3.12
-uv venv --python 3.12 /tmp/vllm-release-env
-source /tmp/vllm-release-env/bin/activate
-uv pip install twine
-uv pip install -r requirements/build/cuda.txt
-python3 -m twine --version
-
-# copy release wheels to local directory
-DIST_DIR=/tmp/vllm-release-dist
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-echo "Copying wheels to local directory"
-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
-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 "$PYPI_WHEEL_FILES" ]]; then
-  echo "No default variant wheels found, quitting..."
-  exit 1
-fi
-
-python3 -m twine check "$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"

.buildkite/scripts/upload-release-wheels.sh

@@ -0,0 +1,104 @@
+#!/usr/bin/env bash
+
+set -e
+
+BUCKET="vllm-wheels"
+SUBPATH=$BUILDKITE_COMMIT
+S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
+
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
+echo "Release version from Buildkite: $RELEASE_VERSION"
+GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)
+if [ -z "$GIT_VERSION" ]; then
+    echo "[FATAL] Not on a git tag, cannot create release."
+    exit 1
+else
+    echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
+fi
+# sanity check for version mismatch
+if [ "$RELEASE_VERSION" != "$GIT_VERSION" ]; then
+  if [ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]; then
+    echo "[WARNING] Force release and ignore version mismatch"
+  else
+    echo "[FATAL] Release version from Buildkite does not match Git version."
+    exit 1
+  fi
+fi
+PURE_VERSION=${RELEASE_VERSION#v} # remove leading 'v'
+
+# check pypi token
+if [ -z "$PYPI_TOKEN" ]; then
+  echo "[FATAL] PYPI_TOKEN is not set."
+  exit 1
+else
+  export TWINE_USERNAME="__token__"
+  export TWINE_PASSWORD="$PYPI_TOKEN"
+fi
+
+# check github token
+if [ -z "$GITHUB_TOKEN" ]; then
+  echo "[FATAL] GITHUB_TOKEN is not set."
+  exit 1
+else
+  export GH_TOKEN="$GITHUB_TOKEN"
+fi
+
+set -x # avoid printing secrets above
+
+# download gh CLI from github
+# Get latest gh CLI version from GitHub API
+GH_VERSION=$(curl -s https://api.github.com/repos/cli/cli/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/' | sed 's/^v//')
+if [ -z "$GH_VERSION" ]; then
+  echo "[FATAL] Failed to get latest gh CLI version from GitHub"
+  exit 1
+fi
+echo "Downloading gh CLI version: $GH_VERSION"
+GH_TARBALL="gh_${GH_VERSION}_linux_amd64.tar.gz"
+GH_URL="https://github.com/cli/cli/releases/download/v${GH_VERSION}/${GH_TARBALL}"
+GH_INSTALL_DIR="/tmp/gh-install"
+mkdir -p "$GH_INSTALL_DIR"
+pushd "$GH_INSTALL_DIR"
+curl -L -o "$GH_TARBALL" "$GH_URL"
+tar -xzf "$GH_TARBALL"
+GH_BIN=$(realpath $(find . -name "gh" -type f -executable | head -n 1))
+if [ -z "$GH_BIN" ]; then
+  echo "[FATAL] Failed to find gh CLI executable"
+  exit 1
+fi
+echo "gh CLI downloaded successfully, version: $($GH_BIN --version)"
+echo "Last 5 releases on GitHub:" # as a sanity check of gh and GH_TOKEN
+command "$GH_BIN" release list --limit 5
+popd
+
+# install twine from pypi
+python3 -m venv /tmp/vllm-release-env
+source /tmp/vllm-release-env/bin/activate
+pip install twine
+python3 -m twine --version
+
+# copy release wheels to local directory
+DIST_DIR=/tmp/vllm-release-dist
+echo "Existing wheels on S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+echo "Copying wheels to local directory"
+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" $DIST_DIR
+echo "Wheels copied to local directory"
+# generate source tarball
+git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" $BUILDKITE_COMMIT
+ls -la $DIST_DIR
+
+
+# upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
+PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
+if [ -z "$PYPI_WHEEL_FILES" ]; then
+  echo "No default variant wheels found, quitting..."
+  exit 1
+fi
+python3 -m twine check $PYPI_WHEEL_FILES
+python3 -m twine --non-interactive --verbose upload $PYPI_WHEEL_FILES
+echo "Wheels uploaded to PyPI"
+
+# create release on GitHub with the release version and all wheels
+command "$GH_BIN" release create $GIT_VERSION -d --latest --notes-from-tag --verify-tag $DIST_DIR/*.whl

.buildkite/scripts/upload-rocm-wheels.sh

@@ -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"

.buildkite/test_areas/attention.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: V1 attention (H100)
-  key: v1-attention-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -15,9 +14,8 @@ steps:
     - pytest -v -s v1/attention
 
 - label: V1 attention (B200)
-  key: v1-attention-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention

.buildkite/test_areas/basic_correctness.yaml

@@ -3,9 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Basic Correctness
-  key: basic-correctness
   timeout_in_minutes: 30
-  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/basic_correctness/test_basic_correctness

.buildkite/test_areas/benchmarks.yaml

@@ -2,25 +2,18 @@ group: Benchmarks
 depends_on: 
   - image-build
 steps:
+- label: Benchmarks
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/.buildkite"
+  source_file_dependencies:
+  - benchmarks/
+  commands:
+  - bash scripts/run-benchmarks.sh
+
 - label: Benchmarks CLI Test
-  key: benchmarks-cli-test
   timeout_in_minutes: 20
-  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/benchmarks/
   commands:
   - pytest -v -s benchmarks/
-
-- label: Attention Benchmarks Smoke Test (B200)
-  key: attention-benchmarks-smoke-test-b200
-  device: b200-k8s
-  num_gpus: 2
-  optional: true
-  working_dir: "/vllm-workspace/"
-  timeout_in_minutes: 10
-  source_file_dependencies:
-  - benchmarks/attention_benchmarks/
-  - vllm/v1/attention/
-  commands:
-  - python3 benchmarks/attention_benchmarks/benchmark.py --backends flash flashinfer --batch-specs "8q1s1k" --repeats 1 --warmup-iters 1

.buildkite/test_areas/compile.yaml

@@ -2,225 +2,56 @@ group: Compile
 depends_on: 
   - image-build
 steps:
-- label: Sequence Parallel Correctness Tests (2 GPUs)
-  key: sequence-parallel-correctness-tests-2-gpus
-  timeout_in_minutes: 50
+- label: Fusion and Compile Tests (B200)
+  timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
-  num_devices: 2
-  source_file_dependencies:
-  - vllm/model_executor/layers/
-  - vllm/compilation/
-  - vllm/v1/worker/
-  - vllm/v1/cudagraph_dispatcher.py
-  - tests/compile/correctness_e2e/test_sequence_parallel.py
-  commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
-
-- label: Sequence Parallel Correctness Tests (2xH100)
-  key: sequence-parallel-correctness-tests-2xh100
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/"
-  device: h100
-  optional: true
-  num_devices: 2
-  commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
-
-- label: AsyncTP Correctness Tests (2xH100)
-  key: asynctp-correctness-tests-2xh100
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/"
-  device: h100
-  optional: true
-  num_devices: 2
-  commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
-
-- label: AsyncTP Correctness Tests (B200)
-  key: asynctp-correctness-tests-b200
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/"
-  device: b200-k8s
-  optional: true
-  num_devices: 2
-  commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
-
-- label: Distributed Compile Unit Tests (2xH100)
-  key: distributed-compile-unit-tests-2xh100
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/"
-  device: h100
-  num_devices: 2
-  source_file_dependencies:
-  - vllm/compilation/
-  - vllm/model_executor/layers
-  - tests/compile/passes/distributed/
-  commands:
-  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-  - pytest -s -v tests/compile/passes/distributed
-
-- label: Fusion and Compile Unit Tests (2xB200)
-  key: fusion-and-compile-unit-tests-2xb200
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
   - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/
+  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
+  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/v1/worker/
+  - vllm/v1/cudagraph_dispatcher.py
+  - vllm/compilation/
+  # can affect pattern matching
   - vllm/model_executor/layers/layernorm.py
   - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/attention/attention.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/ # TODO(luka) limit to vllm/compilation/passes
-  - tests/compile/passes/test_fusion_attn.py
-  - tests/compile/passes/test_mla_attn_quant_fusion.py
-  - tests/compile/passes/test_silu_mul_quant_fusion.py
-  - tests/compile/passes/distributed/test_fusion_all_reduce.py
+  - vllm/model_executor/layers/quantization/input_quant_fp8.py
+  - tests/compile/test_fusion_attn.py
+  - tests/compile/test_silu_mul_quant_fusion.py
+  - tests/compile/distributed/test_fusion_all_reduce.py
+  - tests/compile/distributed/test_fusions_e2e.py
   - tests/compile/fullgraph/test_full_graph.py
   commands:
-    # b200 runners are limited, so we limit the tests to the minimum set only supported on Blackwell
     - nvidia-smi
-    - pytest -v -s tests/compile/passes/test_fusion_attn.py -k FLASHINFER
-    - pytest -v -s tests/compile/passes/test_mla_attn_quant_fusion.py
-    - pytest -v -s tests/compile/passes/test_silu_mul_quant_fusion.py
+    - pytest -v -s tests/compile/test_fusion_attn.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
     # this runner has 2 GPUs available even though num_devices=2 is not set
-    - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
+    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
+    # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
+    # Wrap with quotes to escape yaml
+    - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
     # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    # TODO(luka) move to H100 once pass tests run on H100
     - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
 
-- label: Fusion E2E Quick (H100)
-  key: fusion-e2e-quick-h100
-  timeout_in_minutes: 15
+- label: Fusion E2E (2 GPUs)(B200)
+  timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
-  device: h100
-  num_devices: 1
-  source_file_dependencies:
-    - csrc/quantization/
-    - vllm/model_executor/
-    - vllm/v1/attention/
-    - vllm/compilation/
-    - tests/compile/fusions_e2e/
-  commands:
-    - nvidia-smi
-    # Run all models and attn backends but only Inductor partition and native custom ops
-    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and not +quant_fp8"
-    # Qwen/Deepseek requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
-    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and (qwen3 or deepseek)"
-
-- label: Fusion E2E Config Sweep (H100)
-  key: fusion-e2e-config-sweep-h100
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/"
-  device: h100
-  num_devices: 1
-  source_file_dependencies:
-    - csrc/quantization/
-    - vllm/compilation/
-    # can affect pattern matching
-    - vllm/model_executor/layers/layernorm.py
-    - vllm/model_executor/layers/activation.py
-    - vllm/model_executor/layers/attention/attention.py
-    - vllm/model_executor/layers/quantization/input_quant_fp8.py
-    - tests/compile/fusions_e2e/
-  commands:
-    - nvidia-smi
-    # Run just llama3 (fp8) for all config combinations
-    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
-
-- label: Fusion E2E Config Sweep (B200)
-  key: fusion-e2e-config-sweep-b200
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/"
-  device: b200-k8s
-  num_devices: 1
+  device: b200
   optional: true
-  commands:
-    - nvidia-smi
-    # Run all models but only FLASHINFER, Inductor partition and native custom ops
-    # Qwen/Deepseek requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
-    # Run just llama3 (fp8 & fp4) for all config combinations (only inductor partition)
-    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek)) or llama-3)"
-
-- label: Fusion E2E TP2 Quick (H100)
-  key: fusion-e2e-tp2-quick-h100
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/"
-  device: h100
   num_devices: 2
   source_file_dependencies:
-    - csrc/quantization/
-    - vllm/model_executor/
-    - vllm/v1/attention/
-    - vllm/compilation/
-    - tests/compile/fusions_e2e/
+  - csrc/quantization/fp4/
+  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
+  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/
+  # can affect pattern matching
+  - vllm/model_executor/layers/layernorm.py
+  - vllm/model_executor/layers/activation.py
+  - vllm/model_executor/layers/quantization/input_quant_fp8.py
+  - tests/compile/distributed/test_fusions_e2e.py
   commands:
     - nvidia-smi
-    # Run all models and attn backends but only Inductor partition and native custom ops
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
+    # Run all e2e fusion tests
+    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
 
-- label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
-  key: fusion-e2e-tp2-ar-rms-config-sweep-h100
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  device: h100
-  num_devices: 2
-  source_file_dependencies:
-    - csrc/quantization/
-    - vllm/compilation/
-    # can affect pattern matching
-    - vllm/model_executor/layers/layernorm.py
-    - vllm/model_executor/layers/activation.py
-    - vllm/model_executor/layers/attention/attention.py
-    - vllm/model_executor/layers/quantization/input_quant_fp8.py
-    - tests/compile/fusions_e2e/
-  commands:
-    - nvidia-smi
-    # Run just llama3 (fp8 & bf16) for all config combinations
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "llama-3"
-
-- label: Fusion E2E TP2 AsyncTP Config Sweep (H100)
-  key: fusion-e2e-tp2-asynctp-config-sweep-h100
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  device: h100
-  num_devices: 2
-  source_file_dependencies:
-    - csrc/quantization/
-    - vllm/compilation/
-    # can affect pattern matching
-    - vllm/model_executor/layers/layernorm.py
-    - vllm/model_executor/layers/activation.py
-    - vllm/model_executor/layers/attention/attention.py
-    - vllm/model_executor/layers/quantization/input_quant_fp8.py
-    - tests/compile/fusions_e2e/
-  commands:
-    - nvidia-smi
-    # Run just llama3 (fp8 & bf16) for all config combinations
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "llama-3"
-
-- label: Fusion E2E TP2 (B200)
-  key: fusion-e2e-tp2-b200
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/"
-  device: b200-k8s
-  num_devices: 2
-  source_file_dependencies:
-    - csrc/quantization/
-    - vllm/model_executor/
-    - vllm/v1/attention/
-    - vllm/compilation/
-    - tests/compile/fusions_e2e/
-  commands:
-    - nvidia-smi
-    # Run all models but only FLASHINFER, Inductor partition and native custom ops
-    # include qwen/deepseek with +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
-    # for ar-rms-quant-fp4, also sweep llama3
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "(FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))) or Llama-3.1-8B-Instruct-FP4"
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"