Sparse MLA on Hopper: Use SGLang's kernel for the sparse mla low latency runs

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

.buildkite/ci_config.yaml

@@ -8,7 +8,6 @@ run_all_patterns:
   - "CMakeLists.txt"
   - "requirements/common.txt"
   - "requirements/cuda.txt"
-  - "requirements/kv_connectors.txt"
   - "requirements/build/cuda.txt"
   - "requirements/test/cuda.txt"
   - "setup.py"

.buildkite/hardware_tests/cpu.yaml

@@ -12,19 +12,15 @@ steps:
   - vllm/_custom_ops.py
   - tests/kernels/attention/test_cpu_attn.py
   - tests/kernels/moe/test_cpu_fused_moe.py
-  - tests/kernels/moe/test_cpu_quant_fused_moe.py
   - tests/kernels/test_onednn.py
   - tests/kernels/test_awq_int4_to_int8.py
-  - tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
   commands:
     - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
       pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
       pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
-      pytest -x -v -s tests/kernels/moe/test_cpu_quant_fused_moe.py
       pytest -x -v -s tests/kernels/test_onednn.py
-      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
-      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py"
+      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py"
 
 - label: CPU-Compatibility Tests
   depends_on: []
@@ -54,20 +50,6 @@ steps:
       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/
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
-      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"
-
 - label: CPU-Quantization Model Tests
   depends_on: []
   device: intel_cpu
@@ -75,24 +57,23 @@ steps:
   source_file_dependencies:
   - csrc/cpu/
   - vllm/model_executor/layers/quantization/cpu_wna16.py
-  - vllm/model_executor/layers/quantization/auto_gptq.py
+  - vllm/model_executor/layers/quantization/gptq_marlin.py
   - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
   - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
   - vllm/model_executor/layers/quantization/kernels/mixed_precision/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 "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
       pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
       pytest -x -v -s tests/quantization/test_cpu_wna16.py"
       
-- label: CPU-Distributed Tests (PP+TP)
+- label: CPU-Distributed Tests
   depends_on: []
   device: intel_cpu
   no_plugin: true
-  source_file_dependencies: &cpu_distributed_deps
+  source_file_dependencies:
   - csrc/cpu/shm.cpp
   - vllm/v1/worker/cpu_worker.py
   - vllm/v1/worker/gpu_worker.py
@@ -101,21 +82,10 @@ steps:
   - vllm/platforms/cpu.py
   - vllm/distributed/parallel_state.py
   - vllm/distributed/device_communicators/cpu_communicator.py
-  - .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh tp_pp"
-
-- label: CPU-Distributed Tests (DP+TP)
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies: *cpu_distributed_deps
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh dp_tp"
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"
 
 - label: CPU-Multi-Modal Model Tests %N
   depends_on: []

.buildkite/hardware_tests/intel.yaml

@@ -8,3 +8,10 @@ steps:
     commands: 
     - bash .buildkite/scripts/hardware_ci/run-hpu-test.sh
 
+  - label: "Intel GPU Test"
+    depends_on: []
+    soft_fail: true
+    device: intel_gpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-xpu-test.sh

.buildkite/image_build/image_build.sh

@@ -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() {
@@ -192,7 +192,6 @@ 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

.buildkite/image_build/image_build.yaml

@@ -6,48 +6,6 @@ steps:
     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 $IMAGE_TAG; fi
-    # Non-root smoke 1: the default (root) image must still be importable
-    # under a non-root UID via `--user 2000:0`. Validates the `vllm` passwd
-    # entry + group-0-writable /home/vllm + uv path cleanup from #31959.
-    # Uses `import vllm` rather than `vllm serve --help` because the latter
-    # instantiates `VllmConfig` which requires a GPU attached to the
-    # container.
-    - docker run --rm --user 2000:0 --entrypoint python3 "$IMAGE_TAG" -c "import vllm; print(vllm.__version__)"
-    # Non-root 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
-      '
     retry:
       automatic:
         - exit_status: -1  # Agent was lost

.buildkite/image_build/image_build_cpu.sh

@@ -11,7 +11,7 @@ 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

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -11,7 +11,7 @@ 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

.buildkite/image_build/image_build_hpu.sh

@@ -11,7 +11,7 @@ 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

.buildkite/image_build/image_build_torch_nightly.sh

@@ -46,7 +46,7 @@ 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_CUDA_VERSION="13.0.0"
 NIGHTLY_BUILD_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-devel-ubuntu22.04"
 NIGHTLY_FINAL_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-base-ubuntu22.04"
 

.buildkite/image_build/image_build_xpu.sh

@@ -11,8 +11,8 @@ 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 "$REGISTRY" || true
-aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
 
 # skip build if image already exists
 if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then

.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

@@ -36,12 +36,9 @@ steps:
         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
-        '
+        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel'
   - label: "XPU V1 test"
     depends_on:
       - image-build-xpu
@@ -64,24 +61,5 @@ steps:
         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'
+        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
+        pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_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'

.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]>=0.4.11"
 
 usage() {
     echo``

.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]>=0.4.11"
 
 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]>=0.4.11"
 
 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]>=0.4.11"
 
 usage() {
     echo``

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

@@ -31,9 +31,30 @@
     }
   },
   "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"
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "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",
@@ -41,10 +62,158 @@
         "random-output-len": 128
       }
     },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "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": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "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": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "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_llama8B_tp2_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
     {
       "test_name": "serving_llama8B_int4_tp1_random_128_128",
       "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
+        "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": 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",
@@ -56,7 +225,8 @@
     {
       "test_name": "serving_llama8B_int8_tp1_random_128_128",
       "server_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
@@ -66,12 +236,26 @@
       }
     },
     {
-      "test_name": "serving_llama1B_tp1_random_128_128",
+      "test_name": "serving_llama8B_int8_tp2_random_128_128",
       "server_parameters": {
-        "model": "meta-llama/Llama-3.2-1B"
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 2
       },
       "client_parameters": {
-        "model": "meta-llama/Llama-3.2-1B",
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int8_tp4_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
         "dataset_name": "random",
         "random-input-len": 128,
         "random-output-len": 128
@@ -80,7 +264,8 @@
     {
       "test_name": "serving_llama3B_tp1_random_128_128",
       "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct"
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "meta-llama/Llama-3.2-3B-Instruct",
@@ -89,22 +274,11 @@
         "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"
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "ibm-granite/granite-3.2-2b-instruct",
@@ -116,7 +290,8 @@
     {
       "test_name": "serving_qwen1.7B_tp1_random_128_128",
       "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B"
+        "model": "Qwen/Qwen3-1.7B",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "Qwen/Qwen3-1.7B",
@@ -128,7 +303,8 @@
     {
       "test_name": "serving_qwen4B_tp1_random_128_128",
       "server_parameters": {
-        "model": "Qwen/Qwen3-4B"
+        "model": "Qwen/Qwen3-4B",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "Qwen/Qwen3-4B",
@@ -140,7 +316,8 @@
     {
       "test_name": "serving_qwen8B_tp1_random_128_128",
       "server_parameters": {
-        "model": "Qwen/Qwen3-8B"
+        "model": "Qwen/Qwen3-8B",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "Qwen/Qwen3-8B",
@@ -149,34 +326,11 @@
         "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"
+        "model": "zai-org/glm-4-9b-hf",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "zai-org/glm-4-9b-hf",
@@ -188,7 +342,8 @@
     {
       "test_name": "serving_gemma7B_tp1_random_128_128",
       "server_parameters": {
-        "model": "google/gemma-7b"
+        "model": "google/gemma-7b",
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "model": "google/gemma-7b",
@@ -196,94 +351,6 @@
         "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.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,
+            "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",
+            "temperature": 0,
+            "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,
+            "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",
+            "temperature": 0,
+            "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,
+            "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",
+            "temperature": 0,
+            "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,
+            "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",
+            "temperature": 0,
+            "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/release-pipeline.yaml

@@ -1,16 +1,3 @@
-# CUDA architecture lists — following PyTorch RELEASE.md
-# (https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
-# SM86 included for broader Ampere coverage; SM89 for marlin fp8 support
-env:
-  CUDA_ARCH_X86: "7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"
-  # aarch64 only architectures: 8.7 for Orin, 11.0 for Thor (since CUDA 13)
-  CUDA_ARCH_AARCH64: "8.0 8.7 8.9 9.0 10.0 11.0 12.0+PTX"
-  CUDA_ARCH_X86_CU129: "7.5 8.0 8.6 8.9 9.0 10.0 12.0"
-  CUDA_ARCH_AARCH64_CU129: "8.0 8.7 8.9 9.0 10.0 12.0"
-  MOONCAKE_WHEEL_AARCH64_2_35: "https://vllm-wheels.s3.amazonaws.com/mooncake/mooncake_transfer_engine-0.3.10.post2-0da9dfea3-cp312-cp312-manylinux_2_35_aarch64.whl"
-  MOONCAKE_WHEEL_AARCH64_2_39: "https://vllm-wheels.s3.amazonaws.com/mooncake/mooncake_transfer_engine-0.3.10.post2-0da9dfea3-cp312-cp312-manylinux_2_39_aarch64.whl"
-  MOONCAKE_WHEEL_X86_64: "https://vllm-wheels.s3.amazonaws.com/mooncake/mooncake_transfer_engine-0.3.10.post2-0da9dfea3-cp312-cp312-manylinux_2_35_x86_64.whl"
-
 steps:
   - input: "Provide Release version here"
     id: input-release-version
@@ -27,11 +14,12 @@ steps:
         agents:
           queue: arm64_cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinuxaarch64-builder:cuda12.9 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
+          # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
           - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "s3://vllm-wheels/$$BUILDKITE_COMMIT/$(cd artifacts/dist && echo *.whl)"'
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -41,11 +29,12 @@ steps:
         agents:
           queue: arm64_cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinuxaarch64-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
+          # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04  --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "s3://vllm-wheels/$$BUILDKITE_COMMIT/$(cd artifacts/dist && echo *.whl)"'
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -58,8 +47,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "s3://vllm-wheels/$$BUILDKITE_COMMIT/$(cd artifacts/dist && echo *.whl)"'
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -69,11 +57,10 @@ steps:
         agents:
           queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinux2_28-builder:cuda12.9 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "s3://vllm-wheels/$$BUILDKITE_COMMIT/$(cd artifacts/dist && echo *.whl)"'
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -83,11 +70,10 @@ steps:
         agents:
           queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinux2_28-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "s3://vllm-wheels/$$BUILDKITE_COMMIT/$(cd artifacts/dist && echo *.whl)"'
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -100,8 +86,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "s3://vllm-wheels/$$BUILDKITE_COMMIT/$(cd artifacts/dist && echo *.whl)"'
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -123,226 +108,102 @@ steps:
     depends_on: block-build-release-images
     allow_dependency_failure: true
     steps:
-      - label: "Build release image - x86_64 - CUDA 13.0"
+      - label: "Build release image - x86_64 - CUDA 12.9"
         depends_on: ~
         id: build-release-image-x86
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
           # re-tag to default image tag and push, just in case arm64 build fails
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"'
 
-      - label: "Build release image - aarch64 - CUDA 13.0"
+      - label: "Build release image - aarch64 - CUDA 12.9"
         depends_on: ~
         id: build-release-image-arm64
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"'
 
-      - label: "Build release image - x86_64 - CUDA 12.9"
+      - label: "Build release image - x86_64 - CUDA 13.0"
         depends_on: ~
-        id: build-release-image-x86-cuda-12-9
+        id: build-release-image-x86-cuda-13-0
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
           # re-tag to default image tag and push, just in case arm64 build fails
-          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"'
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
 
-      - label: "Build release image - aarch64 - CUDA 12.9"
+      - label: "Build release image - aarch64 - CUDA 13.0"
         depends_on: ~
-        id: build-release-image-arm64-cuda-12-9
+        id: build-release-image-arm64-cuda-13-0
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"'
+          # compute capability 12.0 for RTX-50 series / RTX PRO 6000 Blackwell, 12.1 for DGX Spark
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
 
-      - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
+      - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
         depends_on: ~
         id: build-release-image-x86-ubuntu2404
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"'
 
-      - label: "Build release image - aarch64 - CUDA 13.0 - Ubuntu 24.04"
+      - label: "Build release image - aarch64 - CUDA 12.9 - Ubuntu 24.04"
         depends_on: ~
         id: build-release-image-arm64-ubuntu2404
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"'
 
-      - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
+      - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
         depends_on: ~
-        id: build-release-image-x86-cuda-12-9-ubuntu2404
+        id: build-release-image-x86-cuda-13-0-ubuntu2404
         agents:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
-          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"'
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404"
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
 
-      - label: "Build release image - aarch64 - CUDA 12.9 - Ubuntu 24.04"
+      - label: "Build release image - aarch64 - CUDA 13.0 - Ubuntu 24.04"
         depends_on: ~
-        id: build-release-image-arm64-cuda-12-9-ubuntu2404
+        id: build-release-image-arm64-cuda-13-0-ubuntu2404
         agents:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"'
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404"
 
       - block: "Build release image for x86_64 CPU"
         key: block-cpu-release-image-build
         depends_on: ~
 
       - label: "Build release image - x86_64 - CPU"
-        key: build-cpu-release-image-x86
         depends_on:
           - block-cpu-release-image-build
           - input-release-version
@@ -353,7 +214,6 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"'
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -362,8 +222,7 @@ steps:
         depends_on: ~
 
       - label: "Build release image - arm64 - CPU"
-        key: build-cpu-release-image-arm64
-        depends_on:
+        depends_on: 
           - block-arm64-cpu-release-image-build
           - input-release-version
         agents:
@@ -373,14 +232,13 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"'
         env:
           DOCKER_BUILDKIT: "1"
 
   - group: "Publish release images"
     key: "publish-release-images"
     steps:
-      - label: "Create multi-arch manifest - CUDA 13.0"
+      - label: "Create multi-arch manifest - CUDA 12.9"
         depends_on:
           - build-release-image-x86
           - build-release-image-arm64
@@ -391,22 +249,29 @@ steps:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
           - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 13.0" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"'
 
-      - label: "Create multi-arch manifest - CUDA 12.9"
+      - label: "Annotate release workflow - CUDA 12.9"
         depends_on:
-          - build-release-image-x86-cuda-12-9
-          - build-release-image-arm64-cuda-12-9
-        id: create-multi-arch-manifest-cuda-12-9
+          - create-multi-arch-manifest
+        id: annotate-release-workflow
+        agents:
+          queue: small_cpu_queue_release
+        commands:
+          - "bash .buildkite/scripts/annotate-release.sh"
+
+      - label: "Create multi-arch manifest - CUDA 13.0"
+        depends_on:
+          - build-release-image-x86-cuda-13-0
+          - build-release-image-arm64-cuda-13-0
+        id: create-multi-arch-manifest-cuda-13-0
         agents:
           queue: small_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu129 --amend"
-          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 12.9" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"'
+          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
+          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
 
-      - label: "Create multi-arch manifest - CUDA 13.0 - Ubuntu 24.04"
+      - label: "Create multi-arch manifest - CUDA 12.9 - Ubuntu 24.04"
         depends_on:
           - build-release-image-x86-ubuntu2404
           - build-release-image-arm64-ubuntu2404
@@ -417,20 +282,18 @@ steps:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-ubuntu2404 --amend"
           - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 13.0 Ubuntu 24.04" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"'
 
-      - label: "Create multi-arch manifest - CUDA 12.9 - Ubuntu 24.04"
+      - label: "Create multi-arch manifest - CUDA 13.0 - Ubuntu 24.04"
         depends_on:
-          - build-release-image-x86-cuda-12-9-ubuntu2404
-          - build-release-image-arm64-cuda-12-9-ubuntu2404
-        id: create-multi-arch-manifest-cuda-12-9-ubuntu2404
+          - build-release-image-x86-cuda-13-0-ubuntu2404
+          - build-release-image-arm64-cuda-13-0-ubuntu2404
+        id: create-multi-arch-manifest-cuda-13-0-ubuntu2404
         agents:
           queue: small_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu129-ubuntu2404 --amend"
-          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
-          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 12.9 Ubuntu 24.04" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"'
+          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130-ubuntu2404 --amend"
+          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
 
       - label: "Publish nightly multi-arch image to DockerHub"
         depends_on:
@@ -450,16 +313,16 @@ steps:
           DOCKER_BUILDKIT: "1"
           DOCKERHUB_USERNAME: "vllmbot"
 
-      - label: "Publish nightly multi-arch image to DockerHub - CUDA 12.9"
+      - label: "Publish nightly multi-arch image to DockerHub - CUDA 13.0"
         depends_on:
-          - create-multi-arch-manifest-cuda-12-9
+          - create-multi-arch-manifest-cuda-13-0
         if: build.env("NIGHTLY") == "1"
         agents:
           queue: small_cpu_queue_release
         commands:
-          - "bash .buildkite/scripts/push-nightly-builds.sh cu129"
+          - "bash .buildkite/scripts/push-nightly-builds.sh cu130"
           # Clean up old nightly builds (keep only last 14)
-          - "bash .buildkite/scripts/cleanup-nightly-builds.sh cu129-nightly-"
+          - "bash .buildkite/scripts/cleanup-nightly-builds.sh cu130-nightly-"
         plugins:
           - docker-login#v3.0.0:
               username: vllmbot
@@ -468,6 +331,24 @@ steps:
           DOCKER_BUILDKIT: "1"
           DOCKERHUB_USERNAME: "vllmbot"
 
+  - group: "Publish wheels"
+    key: "publish-wheels"
+    steps:
+      - block: "Confirm update release wheels to PyPI (experimental, use with caution)?"
+        key: block-upload-release-wheels
+        depends_on:
+          - input-release-version
+          - build-wheels
+
+      - label: "Upload release wheels to PyPI"
+        depends_on:
+          - block-upload-release-wheels
+        id: upload-release-wheels
+        agents:
+          queue: small_cpu_queue_release
+        commands:
+          - "bash .buildkite/scripts/upload-release-wheels-pypi.sh"
+
   # =============================================================================
   # ROCm Release Pipeline (x86_64 only)
   # =============================================================================
@@ -581,7 +462,7 @@ steps:
           echo ""
           echo " Build complete - Image and wheels cached"
         fi
-
+        
     artifact_paths:
       - "artifacts/rocm-base-wheels/*.whl"
     env:
@@ -737,7 +618,7 @@ steps:
       - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
     env:
       S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm722"
+      VARIANT: "rocm721"
 
   # ROCm Job 6: Build ROCm Release Docker Image
   - label: ":docker: Build release image - x86_64 - ROCm"
@@ -797,7 +678,7 @@ steps:
         
         # Push to ECR
         docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm
-
+        
         echo ""
         echo " Successfully built and pushed ROCm release image"
         echo "   Image: public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm"
@@ -824,60 +705,3 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
       DOCKERHUB_USERNAME: "vllmbot"
-
-  # =============================================================================
-  # Publish to DockerHub and PyPI (at the end so all builds complete first)
-  # =============================================================================
-
-  - block: "Publish release images to DockerHub"
-    key: block-publish-release-images
-    depends_on:
-      - create-multi-arch-manifest
-      - create-multi-arch-manifest-cuda-12-9
-      - create-multi-arch-manifest-ubuntu2404
-      - create-multi-arch-manifest-cuda-12-9-ubuntu2404
-      - build-rocm-release-image
-      - input-release-version
-      # Wait for CPU builds if their block steps were unblocked, so publish
-      # doesn't race the in-progress CPU build. allow_failure lets publish
-      # proceed when the operator legitimately leaves the CPU block steps
-      # unblocked or the CPU build fails.
-      - step: build-cpu-release-image-x86
-        allow_failure: true
-      - step: build-cpu-release-image-arm64
-        allow_failure: true
-    if: build.env("NIGHTLY") != "1"
-
-  - label: "Publish release images to DockerHub"
-    depends_on:
-      - block-publish-release-images
-    key: publish-release-images-dockerhub
-    agents:
-      queue: small_cpu_queue_release
-    commands:
-      - "bash .buildkite/scripts/publish-release-images.sh"
-    plugins:
-      - docker-login#v3.0.0:
-          username: vllmbot
-          password-env: DOCKERHUB_TOKEN
-    env:
-      DOCKER_BUILDKIT: "1"
-      DOCKERHUB_USERNAME: "vllmbot"
-
-  - group: "Publish wheels"
-    key: "publish-wheels"
-    steps:
-      - block: "Confirm update release wheels to PyPI (experimental, use with caution)?"
-        key: block-upload-release-wheels
-        depends_on:
-          - input-release-version
-          - build-wheels
-
-      - label: "Upload release wheels to PyPI"
-        depends_on:
-          - block-upload-release-wheels
-        id: upload-release-wheels
-        agents:
-          queue: small_cpu_queue_release
-        commands:
-          - "bash .buildkite/scripts/upload-release-wheels-pypi.sh"

.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 +1,119 @@
+#!/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
+
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
+buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
+To download the wheel (by commit):
+\`\`\`
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_aarch64.whl .
+
+(Optional) For CUDA 13.0:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_aarch64.whl .
+
+(Optional) For CPU:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
+\`\`\`
+
+
+To download and upload the image:
+
+\`\`\`
+# Download images:
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+# Tag and push images:
+
+## CUDA
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai:latest-x86_64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130 vllm/vllm-openai:x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:latest-x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+docker push vllm/vllm-openai:latest-x86_64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker push vllm/vllm-openai:latest-aarch64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130 vllm/vllm-openai:aarch64-cu130
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker push vllm/vllm-openai:latest-aarch64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+
+## ROCm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${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${RELEASE_VERSION}-base
+
+## CPU
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai-cpu:latest-x86_64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker push vllm/vllm-openai-cpu:latest-arm64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+# Create multi-arch manifest:
+
+docker manifest rm vllm/vllm-openai:latest
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest push vllm/vllm-openai:latest
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+docker manifest rm vllm/vllm-openai:latest-cu130
+docker manifest create vllm/vllm-openai:latest-cu130 vllm/vllm-openai:latest-x86_64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker manifest push vllm/vllm-openai:latest-cu130
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu130
+
+docker manifest rm vllm/vllm-openai-cpu:latest || true
+docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker manifest push vllm/vllm-openai-cpu:latest
+docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
+\`\`\`
+EOF

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

@@ -29,7 +29,7 @@ if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
         TORCH_INDEX_URL=""
     fi
 else
-    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
+    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu129"
 fi
 echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
 

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

@@ -1,55 +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>.
-
-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:-ci-${BUILD}-${JOB:0:8}.log}"
-else
-    if [ $# -lt 2 ]; then usage; fi
-    BUILD="$1"
-    JOB="$2"
-    OUT="${3:-ci-${BUILD}-${JOB:0:8}.log}"
-fi
-
-if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
-    echo "Could not parse build number or job UUID from: $1" >&2
-    usage
-fi
-
-COOKIES=$(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/coverage/aggregate-coverage.py

@@ -1,208 +0,0 @@
-#!/usr/bin/env python3
-"""Aggregate per-step coverage JSON files into a test-selection mapping.
-
-Downloads all coverage_*.json artifacts from the current Buildkite build,
-then produces two output files:
-
-1. coverage_map.json — inverted index: {source_file: [step_keys]}
-   Used by the pipeline generator to determine which steps to trigger.
-
-2. step_coverage.json — forward index: {step_key: [source_files]}
-   Useful for debugging and understanding test coverage.
-
-Usage:
-    # Run as a Buildkite step at the end of nightly CI
-    python3 .buildkite/scripts/coverage/aggregate-coverage.py
-
-    # Or locally with downloaded artifacts
-    python3 .buildkite/scripts/coverage/aggregate-coverage.py --local-dir ./artifacts/
-"""
-
-import argparse
-import json
-import os
-import subprocess
-import sys
-import tempfile
-from collections import defaultdict
-from pathlib import Path
-
-
-def download_artifacts(dest_dir: str) -> list[str]:
-    """Download all coverage_*.json artifacts from the current build."""
-    try:
-        subprocess.run(
-            ["buildkite-agent", "artifact", "download", "coverage_*.json", dest_dir],
-            check=True,
-            capture_output=True,
-            text=True,
-        )
-    except FileNotFoundError:
-        print("buildkite-agent not found, skipping download", file=sys.stderr)
-        return []
-    except subprocess.CalledProcessError as e:
-        print(f"Artifact download failed: {e.stderr}", file=sys.stderr)
-        return []
-
-    return list(Path(dest_dir).glob("coverage_*.json"))
-
-
-def load_coverage_files(files: list[Path]) -> dict[str, list[str]]:
-    """Load coverage JSON files and extract source files per step.
-
-    Returns: {step_key: [source_files]}
-    """
-    step_coverage = {}
-
-    for filepath in files:
-        filename = filepath.name
-        # coverage_<step_key>.json -> step_key
-        step_key = filename.removeprefix("coverage_").removesuffix(".json")
-
-        try:
-            with open(filepath) as f:
-                data = json.load(f)
-        except (json.JSONDecodeError, OSError) as e:
-            print(f"Warning: skipping {filename}: {e}", file=sys.stderr)
-            continue
-
-        source_files = []
-        for fpath, fdata in data.get("files", {}).items():
-            # Skip files with zero executed lines — coverage.py reports
-            # all files in the source tree, not just those actually run.
-            # Supports both full format (summary.covered_lines) and
-            # stripped format (covered_lines directly).
-            covered = fdata.get("covered_lines") or fdata.get("summary", {}).get("covered_lines", 0)
-            if covered == 0:
-                continue
-            # If function-level data is available, skip import-only files
-            # (files where only module-level code ran but no named functions
-            # were actually called).
-            funcs_called = fdata.get("functions_called")
-            if funcs_called is not None and funcs_called == 0:
-                continue
-            # Normalize paths to be relative to the vllm package root.
-            # coverage.py may report absolute paths or paths relative to
-            # the installed package location. We only care about files
-            # under the vllm/ directory.
-            normalized = _normalize_path(fpath)
-            if normalized:
-                source_files.append(normalized)
-
-        if source_files:
-            step_coverage[step_key] = sorted(set(source_files))
-            print(f"  {step_key}: {len(source_files)} source files")
-
-    return step_coverage
-
-
-def _normalize_path(path: str) -> str | None:
-    """Normalize a coverage path to a vllm-relative path.
-
-    Returns None for paths outside the vllm package (tests, third-party, etc).
-    """
-    # Strip common prefixes from installed package paths
-    markers = ["/site-packages/", "/dist-packages/", "/vllm-workspace/src/"]
-    for marker in markers:
-        idx = path.find(marker)
-        if idx != -1:
-            path = path[idx + len(marker):]
-            break
-
-    # Also handle paths that are already relative
-    if path.startswith("vllm/"):
-        return path
-
-    # Handle absolute paths that contain /vllm/
-    idx = path.find("/vllm/")
-    if idx != -1:
-        return path[idx + 1:]
-
-    return None
-
-
-def build_inverted_index(
-    step_coverage: dict[str, list[str]],
-) -> dict[str, list[str]]:
-    """Build {source_file: [step_keys]} from {step_key: [source_files]}."""
-    inverted = defaultdict(list)
-    for step_key, source_files in step_coverage.items():
-        for src_file in source_files:
-            inverted[src_file].append(step_key)
-
-    # Sort step lists for deterministic output
-    return {k: sorted(v) for k, v in sorted(inverted.items())}
-
-
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--local-dir",
-        help="Directory containing coverage_*.json files (skip artifact download)",
-    )
-    parser.add_argument(
-        "--output-dir",
-        default=".",
-        help="Directory to write output files (default: cwd)",
-    )
-    args = parser.parse_args()
-
-    if args.local_dir:
-        artifact_dir = args.local_dir
-        files = list(Path(artifact_dir).glob("coverage_*.json"))
-    else:
-        artifact_dir = tempfile.mkdtemp(prefix="coverage_artifacts_")
-        files = download_artifacts(artifact_dir)
-
-    if not files:
-        print("No coverage files found. Nothing to aggregate.")
-        sys.exit(0)
-
-    print(f"Found {len(files)} coverage files:")
-
-    # Build the forward index: step -> source files
-    step_coverage = load_coverage_files(files)
-
-    if not step_coverage:
-        print("No valid coverage data found.")
-        sys.exit(0)
-
-    # Build the inverted index: source file -> steps
-    coverage_map = build_inverted_index(step_coverage)
-
-    # Write outputs
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    step_coverage_path = output_dir / "step_coverage.json"
-    with open(step_coverage_path, "w") as f:
-        json.dump(step_coverage, f, indent=2)
-    print(f"\nWrote {step_coverage_path} ({len(step_coverage)} steps)")
-
-    coverage_map_path = output_dir / "coverage_map.json"
-    with open(coverage_map_path, "w") as f:
-        json.dump(coverage_map, f, indent=2)
-    print(f"Wrote {coverage_map_path} ({len(coverage_map)} source files)")
-
-    # Summary stats
-    total_files = len(coverage_map)
-    total_mappings = sum(len(v) for v in coverage_map.values())
-    print(f"\nSummary: {total_files} source files mapped to "
-          f"{len(step_coverage)} steps ({total_mappings} total mappings)")
-
-    # Upload aggregated files as artifacts
-    for output_file in [step_coverage_path, coverage_map_path]:
-        try:
-            subprocess.run(
-                ["buildkite-agent", "artifact", "upload", str(output_file)],
-                check=True,
-                capture_output=True,
-                text=True,
-            )
-            print(f"Uploaded {output_file}")
-        except (FileNotFoundError, subprocess.CalledProcessError):
-            pass  # Not in Buildkite or upload failed — that's fine for local runs
-
-
-if __name__ == "__main__":
-    main()

.buildkite/scripts/coverage/upload-step-coverage.sh

@@ -1,42 +0,0 @@
-#!/bin/bash
-# Upload coverage data for the current Buildkite step.
-# Called automatically at the end of each step when COLLECT_COVERAGE=1.
-#
-# Expects:
-#   - .coverage.${BUILDKITE_STEP_KEY} data file from coverage run --append
-#   - BUILDKITE_STEP_KEY, BUILDKITE_BUILD_NUMBER env vars
-#
-# Produces:
-#   - coverage_${BUILDKITE_STEP_KEY}.json uploaded as a Buildkite artifact
-
-set -euo pipefail
-
-STEP_KEY="${BUILDKITE_STEP_KEY:-unknown}"
-DATA_FILE=".coverage.${STEP_KEY}"
-OUTPUT_JSON="coverage_${STEP_KEY}.json"
-
-if [ ! -f "$DATA_FILE" ]; then
-    echo "~~~ No coverage data file found ($DATA_FILE), skipping upload"
-    exit 0
-fi
-
-echo "~~~ :bar_chart: Exporting coverage data for step: ${STEP_KEY}"
-
-coverage json \
-    --data-file="$DATA_FILE" \
-    -o "$OUTPUT_JSON" \
-    --omit='*/tests/*,*/test_*,*/__pycache__/*' \
-    2>&1 || {
-        echo "Warning: coverage json export failed, skipping"
-        exit 0
-    }
-
-FILE_COUNT=$(python3 -c "import json; d=json.load(open('$OUTPUT_JSON')); print(len(d.get('files', {})))" 2>/dev/null || echo "?")
-echo "Coverage captured ${FILE_COUNT} source files for step ${STEP_KEY}"
-
-buildkite-agent artifact upload "$OUTPUT_JSON" 2>&1 || {
-    echo "Warning: artifact upload failed"
-    exit 0
-}
-
-echo "Uploaded $OUTPUT_JSON"

.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

@@ -9,14 +9,21 @@ set -ex
 
 BUCKET="vllm-wheels"
 INDICES_OUTPUT_DIR="indices"
-DEFAULT_VARIANT_ALIAS="cu130" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
+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/"
 
-# 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
+# detect if python3.12+ 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 -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
+
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
 
 # ======== generate and upload indices ========
 

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

@@ -114,7 +114,8 @@ handle_pytest_exit() {
 # 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.
+# through untouched to avoid double-quoting values injected by
+# apply_rocm_test_overrides.
 #
 # NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
 # double-quotes stripped by the calling shell (see header comment).
@@ -246,6 +247,102 @@ re_quote_pytest_markers() {
   echo "${output% }"
 }
 
+###############################################################################
+# ROCm-specific pytest command rewrites
+#
+# These apply ignore flags and environment overrides for tests that are not
+# yet supported or behave differently on ROCm hardware. Kept as a single
+# function so new exclusions are easy to add in one place.
+###############################################################################
+
+apply_rocm_test_overrides() {
+  local cmds="$1"
+
+  # --- Model registry filter ---
+  if [[ $cmds == *"pytest -v -s models/test_registry.py"* ]]; then
+    cmds=${cmds//"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
+
+  # --- LoRA: disable custom paged attention ---
+  if [[ $cmds == *"pytest -v -s lora"* ]]; then
+    cmds=${cmds//"pytest -v -s lora"/"pytest -v -s lora"}
+  fi
+
+  # --- Kernel ignores ---
+  if [[ $cmds == *" kernels/core"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/core/test_fused_quant_layernorm.py \
+    --ignore=kernels/core/test_permute_cols.py"
+  fi
+
+  if [[ $cmds == *" kernels/attention"* ]]; then
+    cmds="${cmds} \
+    --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 [[ $cmds == *" kernels/quantization"* ]]; then
+    cmds="${cmds} \
+    --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 [[ $cmds == *" kernels/mamba"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/mamba/test_mamba_mixer2.py \
+    --ignore=kernels/mamba/test_causal_conv1d.py \
+    --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
+  fi
+
+  if [[ $cmds == *" kernels/moe"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/moe/test_moe.py \
+    --ignore=kernels/moe/test_cutlass_moe.py"
+  fi
+
+  # --- Entrypoint ignores ---
+  if [[ $cmds == *" entrypoints/openai "* ]]; then
+    cmds=${cmds//" entrypoints/openai "/" entrypoints/openai \
+    --ignore=entrypoints/openai/chat_completion/test_audio.py \
+    --ignore=entrypoints/openai/completion/test_shutdown.py \
+    --ignore=entrypoints/openai/test_completion.py \
+    --ignore=entrypoints/openai/models/test_models.py \
+    --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
+    --ignore=entrypoints/openai/chat_completion/test_root_path.py \
+    --ignore=entrypoints/openai/completion/test_prompt_validation.py "}
+  fi
+
+  if [[ $cmds == *" entrypoints/serve"* ]]; then
+    cmds="${cmds} \
+    --ignore=entrypoints/serve/lora/test_lora_adapters.py"
+  fi
+
+  if [[ $cmds == *" entrypoints/llm "* ]]; then
+    cmds=${cmds//" 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
+
+  # Clean up escaped newlines from --ignore appends
+  cmds=$(echo "$cmds" | sed 's/ \\ / /g')
+
+  echo "$cmds"
+}
+
 ###############################################################################
 # Main
 ###############################################################################
@@ -281,11 +378,9 @@ HF_MOUNT="/root/.cache/huggingface"
 # double-quotes will have been stripped by the calling shell.
 if [[ -n "${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
@@ -302,16 +397,11 @@ fi
 
 echo "Raw commands: $commands"
 
-# Only try to repair stripped pytest -m/-k quoting in legacy argv mode.
-# VLLM_TEST_COMMANDS preserves inner quoting already, and re-quoting that path
-# can corrupt embedded echo strings or otherwise well-formed shell fragments.
-if [[ "$commands_source" == "argv" ]]; then
-  commands=$(re_quote_pytest_markers "$commands")
-  echo "After re-quoting: $commands"
-else
-  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
-fi
+# Fix quoting before ROCm overrides (so overrides see correct structure)
+commands=$(re_quote_pytest_markers "$commands")
+echo "After re-quoting: $commands"
 
+commands=$(apply_rocm_test_overrides "$commands")
 echo "Final commands: $commands"
 
 MYPYTHONPATH=".."

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

@@ -3,37 +3,42 @@ set -euox pipefail
 export VLLM_CPU_CI_ENV=0
 export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
 
-MODE=${1:-all}
+echo "--- PP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 --max-model-len=4096 &
+server_pid=$!
+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 tp_pp.json \
+    --save-result \
+    --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/tp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+  echo "Some requests were failed!"
+  exit 1
+fi
 
-run_scenario() {
-    local label="$1" result_file="$2"
-    shift 2
-    echo "--- $label"
-    vllm serve meta-llama/Llama-3.2-3B-Instruct "$@" --max-model-len=4096 &
-    local server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
-    vllm bench serve \
-        --backend vllm \
-        --dataset-name random \
-        --model meta-llama/Llama-3.2-3B-Instruct \
-        --num-prompts 20 \
-        --result-dir ./test_results \
-        --result-filename "$result_file" \
-        --save-result \
-        --endpoint /v1/completions
-    kill -s SIGTERM "$server_pid"; wait "$server_pid" || true
-    if [ "$(jq '.failed' "./test_results/$result_file")" -ne 0 ]; then
-        echo "Some requests were failed in $label!"
-        exit 1
-    fi
-}
-
-case "$MODE" in
-    tp_pp) run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2 ;;
-    dp_tp) run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2 ;;
-    all)
-        run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2
-        run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2
-        ;;
-    *) echo "ERROR: unknown mode '$MODE' (expected: tp_pp | dp_tp | all)" >&2; exit 1 ;;
-esac
+echo "--- DP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 --max-model-len=4096 &
+server_pid=$!
+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 dp_pp.json \
+   --save-result \
+   --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/dp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+ echo "Some requests were failed!"
+ exit 1
+fi

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

@@ -31,21 +31,6 @@ 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"
-
-  # 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
@@ -61,6 +46,13 @@ function cpu_tests() {
     set -e
     pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
 
+  # 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"
 
   # basic online serving
   docker exec cpu-test bash -c '
@@ -75,21 +67,6 @@ 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-intel-test.sh

@@ -25,100 +25,22 @@ export PYTHONPATH=".."
 ###############################################################################
 
 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
+    exit 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
+    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    docker image prune -f
+    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 $threshold%. Checking old CI images anyway."
-    cleanup_old_ci_images "${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)."
+    echo "Disk usage is below $threshold%. No cleanup needed."
   fi
 }
 
@@ -318,6 +240,7 @@ fi
 cleanup_docker
 
 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
 
 # --- Build or pull test image ---
 IMAGE="${IMAGE_TAG_XPU:-${image_name}}"
@@ -343,6 +266,8 @@ fi
 
 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
 
@@ -352,31 +277,16 @@ 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 "${IMAGE}"
-  fi
-
-  docker create \
+docker run \
     --device /dev/dri:/dev/dri \
     --net=host \
     --ipc=host \
     --privileged \
     -v /dev/dri/by-path:/dev/dri/by-path \
-    -v "${HOME}/.cache/huggingface:/root/.cache/huggingface" \
-    --entrypoint='' \
-    -e HF_TOKEN \
-    -e ZE_AFFINITY_MASK \
-    -e CMDS \
+    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN:-}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK:-}" \
+    -e "CMDS=${commands}" \
     --name "${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 "${container_name}"
+    "${image_name}" \
+    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"'

.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]>=0.4.11" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 

.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]>=0.4.11" \
     && 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 +1,55 @@
+#!/bin/bash
+
+# This script build the CPU docker image and run the offline inference inside the container.
+# It serves a sanity check for compilation and basic model usage.
+set -ex
+
+image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+
+# Try building the docker image
+docker build -t "${image_name}" -f docker/Dockerfile.xpu .
+
+# Setup cleanup
+remove_docker_container() {
+  docker rm -f "${container_name}" || true;
+  docker image rm -f "${image_name}" || true;
+  docker system prune -f || true;
+}
+trap remove_docker_container EXIT
+
+# Run the image and test offline inference/tensor parallel
+docker run \
+    --device /dev/dri:/dev/dri \
+    --net=host \
+    --ipc=host \
+    --privileged \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK}" \
+    --name "${container_name}" \
+    "${image_name}" \
+    bash -c '
+    set -e
+    echo $ZE_AFFINITY_MASK
+    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 ray
+    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 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 OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc --block-size 64 --enforce-eager --max-model-len 8192
+    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
+    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 --ignore=v1/worker/test_worker_memory_snapshot.py
+    pytest -v -s v1/structured_output
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_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
+    pytest -v -s v1/test_serial_utils.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/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_prefetch_offload.sh

@@ -51,7 +51,6 @@ vllm serve "$MODEL" \
   --offload-num-in-group 2 \
   --offload-prefetch-step 1 \
   --offload-params w13_weight w2_weight \
-  --generation-config vllm \
   --port "$PORT" \
   ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
 SERVER_PID=$!

.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/tool_call/run-bfcl-eval.sh

@@ -28,7 +28,6 @@
 #   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
@@ -44,7 +43,6 @@ 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
@@ -141,7 +139,7 @@ echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
 # be patched in-process so BFCL knows to use the OpenAI-compatible handler
 # against our local vLLM server.
 bfcl_exit_code=0
-python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$TEMPERATURE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
+python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
 import os
 import sys
 
@@ -150,8 +148,7 @@ 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()
+output_dir = sys.argv[6] if len(sys.argv) > 6 and sys.argv[6] else os.getcwd()
 
 os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
 os.environ["OPENAI_API_KEY"] = "dummy"
@@ -207,7 +204,6 @@ 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 ----

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

@@ -2,18 +2,14 @@
 
 set -ex
 
-# Upload a single wheel to S3, after detecting and applying the appropriate
-# manylinux platform tag with auditwheel.
+# Upload a single wheel to S3 (rename linux -> manylinux).
 # Index generation is handled separately by generate-and-upload-nightly-index.sh.
 
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
-
 BUCKET="vllm-wheels"
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# ========= locate the wheel ==========
+# ========= collect, rename & upload the wheel ==========
 
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -25,9 +21,19 @@ 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

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

@@ -39,17 +39,10 @@ fi
 
 set -x # avoid printing secrets above
 
-# install uv if not already available
-if ! command -v uv &> /dev/null; then
-  curl -LsSf https://astral.sh/uv/install.sh | UV_VERSION=0.11.14 sh
-  export PATH="$HOME/.local/bin:$PATH"
-fi
-
-# install twine and sdist build prerequisites using uv with Python 3.12
-uv venv --python 3.12 /tmp/vllm-release-env
+# install twine from pypi
+python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
-uv pip install twine
-uv pip install -r requirements/build/cuda.txt
+pip install twine
 python3 -m twine --version
 
 # copy release wheels to local directory

.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 ========
 
@@ -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

.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

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Basic Correctness
-  key: basic-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/benchmarks.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Benchmarks CLI Test
-  key: benchmarks-cli-test
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
@@ -13,8 +12,7 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Attention Benchmarks Smoke Test (B200)
-  key: attention-benchmarks-smoke-test-b200
-  device: b200-k8s
+  device: b200
   num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/"

.buildkite/test_areas/compile.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
-  key: sequence-parallel-correctness-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -18,7 +17,6 @@ steps:
   - 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
@@ -29,7 +27,6 @@ steps:
   - 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
@@ -40,10 +37,9 @@ steps:
   - 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
+  device: b200
   optional: true
   num_devices: 2
   commands:
@@ -51,7 +47,6 @@ steps:
   - 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
@@ -65,10 +60,9 @@ steps:
   - 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/
@@ -95,7 +89,6 @@ steps:
     - 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
   working_dir: "/vllm-workspace/"
   device: h100
@@ -114,7 +107,6 @@ steps:
     - 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
@@ -134,10 +126,9 @@ steps:
     - 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
+  device: b200
   num_devices: 1
   optional: true
   commands:
@@ -148,7 +139,6 @@ steps:
     - 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
@@ -166,7 +156,6 @@ steps:
     - 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))"
 
 - 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
@@ -186,7 +175,6 @@ steps:
     - 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
@@ -206,10 +194,9 @@ steps:
     - 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
+  device: b200
   num_devices: 2
   source_file_dependencies:
     - csrc/quantization/

.buildkite/test_areas/cuda.yaml

@@ -3,22 +3,16 @@ depends_on:
   - image-build
 steps:
 - label: Platform Tests (CUDA)
-  key: platform-tests-cuda
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
-  - vllm/envs.py
-  - vllm/logger.py
-  - vllm/platforms/
-  - vllm/plugins/
-  - vllm/utils/
+  - vllm/
   - tests/cuda
   commands:
     - pytest -v -s cuda/test_cuda_context.py
     - pytest -v -s cuda/test_platform_no_cuda_init.py
 
 - label: Cudagraph
-  key: cudagraph
   timeout_in_minutes: 20
   source_file_dependencies:
   - tests/v1/cudagraph
@@ -27,5 +21,4 @@ steps:
   - vllm/compilation
   commands:
     - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
-    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
-    - pytest -v -s v1/cudagraph/test_breakable_cudagraph.py
+    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py

.buildkite/test_areas/disaggregated.yaml

@@ -1,106 +0,0 @@
-group: Disaggregated
-depends_on: 
-  - image-build
-steps:
-- label: Distributed NixlConnector PD accuracy (4 GPUs)
-  key: distributed-nixlconnector-pd-accuracy-4-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-- label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
-  key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
-  key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
-  key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
-  key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 25
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
-  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
-
-- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
-  key: nixlconnector-pd-spec-decode-acceptance-2-gpus
-  timeout_in_minutes: 30
-  device: a100
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - vllm/v1/worker/kv_connector_model_runner_mixin.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/config_sweep_spec_decode_test.sh
-
-- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
-  key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh

.buildkite/test_areas/distributed.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Comm Ops
-  key: distributed-comm-ops
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -17,7 +16,6 @@ steps:
   - pytest -v -s distributed/test_shm_storage.py
 
 - label: Distributed DP Tests (2 GPUs)
-  key: distributed-dp-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -39,7 +37,6 @@ steps:
   - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
 - label: Distributed Compile + RPC Tests (2 GPUs)
-  key: distributed-compile-rpc-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -62,7 +59,6 @@ steps:
   - pytest -v -s ./compile/test_wrapper.py
 
 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
-  key: distributed-torchrun-shutdown-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -85,7 +81,6 @@ steps:
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
 - label: Distributed Torchrun + Examples (4 GPUs)
-  key: distributed-torchrun-examples-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace"
   num_devices: 4
@@ -93,8 +88,9 @@ steps:
   - vllm/distributed/
   - tests/distributed/test_torchrun_example.py
   - tests/distributed/test_torchrun_example_moe.py
+  - examples/offline_inference/rlhf_colocate.py
   - examples/rl/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
@@ -111,13 +107,12 @@ steps:
   # test with torchrun tp=2 and dp=2 with ep
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  - python3 examples/offline_inference/data_parallel.py --enforce-eager
   # rlhf examples
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
 
 - label: Distributed DP Tests (4 GPUs)
-  key: distributed-dp-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -138,7 +133,6 @@ steps:
   - pytest -v -s distributed/test_utils.py
 
 - label: Distributed Compile + Comm (4 GPUs)
-  key: distributed-compile-comm-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -160,28 +154,24 @@ steps:
   - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
-  key: distributed-tests-8-gpus-h100
   timeout_in_minutes: 10
   device: h100
   num_devices: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/features/torchrun/torchrun_dp_example_offline.py
+  - examples/offline_inference/torchrun_dp_example.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
   - vllm/v1/executor/uniproc_executor.py
   - vllm/v1/worker/gpu_worker.py
-  - tests/distributed/test_mnnvl_alltoall.py
-
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
-  key: distributed-tests-4-gpus-a100
   device: a100
   optional: true
   num_devices: 4
@@ -196,7 +186,6 @@ steps:
   - pytest -v -s -x lora/test_mixtral.py
 
 - label: Distributed Tests (2 GPUs)(H100)
-  key: distributed-tests-2-gpus-h100
   timeout_in_minutes: 15
   device: h100
   optional: true
@@ -205,14 +194,13 @@ steps:
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
-    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
     - pytest -v -s tests/distributed/test_packed_tensor.py
 
 - label: Distributed Tests (2 GPUs)(B200)
-  key: distributed-tests-2-gpus-b200
-  device: b200-k8s
+  device: b200
   optional: true
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -220,12 +208,8 @@ steps:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
     - pytest -v -s tests/v1/distributed/test_dbo.py
-    - pytest -v -s tests/distributed/test_mnnvl_alltoall.py
-
-    
 
 - label: 2 Node Test (4 GPUs)
-  key: 2-node-test-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -238,12 +222,96 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
+
+- label: Distributed NixlConnector PD accuracy (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: Hyrbid SSM NixlConnector PD accuracy tests (4 GPUs)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
+
+- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
+  timeout_in_minutes: 30
+  device: a100
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - vllm/v1/worker/kv_connector_model_runner_mixin.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+
+- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
 
 - label: Pipeline + Context Parallelism (4 GPUs)
-  key: pipeline-context-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -258,7 +326,6 @@ steps:
   - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: RayExecutorV2 (4 GPUs)
-  key: rayexecutorv2-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4

.buildkite/test_areas/docker.yaml

@@ -1,16 +0,0 @@
-group: Docker
-depends_on:
-  - image-build-cpu
-steps:
-- label: Docker Build Metadata
-  timeout_in_minutes: 10
-  device: cpu-small
-  source_file_dependencies:
-    - .buildkite/release-pipeline.yaml
-    - .buildkite/scripts/docker-build-metadata-args.sh
-    - docker/Dockerfile
-    - docker/Dockerfile.cpu
-    - docker/docker-bake.hcl
-    - tests/tools/test_docker_build_metadata_args.py
-  commands:
-    - pytest -v -s tools/test_docker_build_metadata_args.py

.buildkite/test_areas/e2e_integration.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: DeepSeek V2-Lite Accuracy
-  key: deepseek-v2-lite-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -13,7 +12,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy
-  key: qwen3-30b-a3b-fp8-block-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -23,27 +21,15 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
-  key: qwen3-30b-a3b-fp8-block-accuracy-b200
   timeout_in_minutes: 60
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-- label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
-  key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
-  timeout_in_minutes: 60
-  device: h100
-  optional: true
-  num_devices: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh 0.8 200 8050
-
 - label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
-  key: deepseek-v2-lite-prefetch-offload-accuracy-h100
   timeout_in_minutes: 60
   device: h100
   optional: true

.buildkite/test_areas/engine.yaml

@@ -3,32 +3,19 @@ depends_on:
   - image-build
 steps:
 - label: Engine
-  key: engine
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
-  - vllm/compilation/
-  - vllm/config/
-  - vllm/engine/
-  - vllm/entrypoints/logger.py
-  - vllm/envs.py
-  - vllm/logger.py
-  - vllm/logging_utils/
-  - vllm/platforms/
-  - vllm/sequence.py
-  - vllm/triton_utils/
-  - vllm/utils/
+  - vllm/
   - tests/engine
   - tests/test_sequence
   - tests/test_config
   - tests/test_logger
   - tests/test_vllm_port
-  - tests/test_jit_monitor.py
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
 - label: Engine (1 GPU)
-  key: engine-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/engine/
@@ -36,15 +23,8 @@ steps:
   commands:
     - pytest -v -s v1/engine/test_preprocess_error_handling.py
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
-  mirror:
-    amd:
-      device: mi300_1
-      timeout_in_minutes: 40
-      depends_on:
-      - image-build-amd
 
 - label: e2e Scheduling (1 GPU)
-  key: e2e-scheduling-1-gpu
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -52,16 +32,8 @@ steps:
     - tests/v1/e2e/general/
   commands:
     - pytest -v -s v1/e2e/general/test_async_scheduling.py
-  mirror:
-    amd:
-      device: mi250_1
-      timeout_in_minutes: 60
-      depends_on:
-      - image-build-amd
 
 - label: e2e Core (1 GPU)
-  device: h200_35gb
-  key: e2e-core-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/
@@ -70,63 +42,38 @@ steps:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
 
 - label: V1 e2e (2 GPUs)
-  key: v1-e2e-2-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 2
   source_file_dependencies:
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/logger.py
-    - vllm/logging_utils/
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
-    - tests/v1/e2e/spec_decode
+    - vllm/
+    - tests/v1/e2e
   commands:
     # Only run tests that need exactly 2 GPUs
     - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
+  mirror:
+    amd:
+      device: mi325_2
+      depends_on:
+      - image-build-amd
 
 - label: V1 e2e (4 GPUs)
-  key: v1-e2e-4-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 4
   source_file_dependencies:
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/logger.py
-    - vllm/logging_utils/
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
-    - tests/v1/e2e/spec_decode
+    - vllm/
+    - tests/v1/e2e
   commands:
     # Only run tests that need 4 GPUs
     - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
+  mirror:
+    amd:
+      device: mi325_4
+      depends_on:
+      - image-build-amd
 
 - label: V1 e2e (4xH100)
-  key: v1-e2e-4xh100
   timeout_in_minutes: 60
   device: h100
   num_devices: 4

.buildkite/test_areas/entrypoints.yaml

@@ -2,8 +2,7 @@ group: Entrypoints
 depends_on: 
   - image-build
 steps:
-- label: Entrypoints Unit Tests
-  key: entrypoints-unit-tests
+- label: Entrypoints Unit Tests  
   timeout_in_minutes: 10
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -11,10 +10,9 @@ steps:
   - tests/entrypoints/
   commands:
   - pytest -v -s entrypoints/openai/tool_parsers
-  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling --ignore=entrypoints/speech_to_text
+  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
 - label: Entrypoints Integration (LLM)
-  key: entrypoints-integration-llm
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -26,14 +24,8 @@ steps:
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
-  mirror:
-    amd:
-      device: mi300_1
-      depends_on:
-      - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 1)
-  key: entrypoints-integration-api-server-openai-part-1
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -45,13 +37,12 @@ steps:
   - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
   mirror:
     amd:
-      device: mi300_1
-      timeout_in_minutes: 80
+      device: mi325_1
       depends_on:
       - image-build-amd
 
+
 - label: Entrypoints Integration (API Server openai - Part 2)
-  key: entrypoints-integration-api-server-openai-part-2
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -60,16 +51,15 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - pytest -v -s entrypoints/openai/speech_to_text/
   - pytest -v -s entrypoints/test_chat_utils.py
   mirror:
     amd:
-      device: mi300_1
-      timeout_in_minutes: 60
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 3)
-  key: entrypoints-integration-api-server-openai-part-3
   timeout_in_minutes: 50
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -79,17 +69,9 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
-  mirror:
-    amd:
-      device: mi300_1
-      timeout_in_minutes: 60
-      depends_on:
-      - image-build-amd
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
 - label: Entrypoints Integration (API Server 2)
-  device: h200_35gb
-  key: entrypoints-integration-api-server-2
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -102,26 +84,8 @@ steps:
   - pytest -v -s entrypoints/serve/instrumentator
   - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
   - pytest -v -s tool_use
-  mirror:
-    amd:
-      device: mi300_1
-      depends_on:
-      - image-build-amd
-
-- label: Entrypoints Integration (Speech to Text)
-  device: h200_35gb
-  key: entrypoints-integration-speech_to_text
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/speech_to_text
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/speech_to_text
 
 - label: Entrypoints Integration (Pooling)
-  key: entrypoints-integration-pooling
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -132,7 +96,6 @@ steps:
   - pytest -v -s entrypoints/pooling
 
 - label: Entrypoints Integration (Responses API)
-  key: entrypoints-integration-responses-api
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -142,12 +105,11 @@ steps:
   - pytest -v -s entrypoints/openai/responses
 
 - label: OpenAI API Correctness
-  key: openai-api-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/
   - vllm/model_executor/models/whisper.py
-  commands: # LMEval
+  commands: # LMEval+Transcription WER check
   - pytest -s entrypoints/openai/correctness/

.buildkite/test_areas/expert_parallelism.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: EPLB Algorithm
-  key: eplb-algorithm
   timeout_in_minutes: 15
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -16,7 +15,6 @@ steps:
   - pytest -v -s distributed/test_eplb_utils.py
 
 - label: EPLB Execution # 17min
-  key: eplb-execution
   timeout_in_minutes: 27
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -28,7 +26,6 @@ steps:
   - pytest -v -s distributed/test_eplb_spec_decode.py
 
 - label: Elastic EP Scaling Test
-  key: elastic-ep-scaling-test
   timeout_in_minutes: 20
   device: h100
   working_dir: "/vllm-workspace/tests"

.buildkite/test_areas/kernels.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: vLLM IR Tests
-  key: vllm-ir-tests
   timeout_in_minutes: 10
   device: h200_18gb
   working_dir: "/vllm-workspace/"
@@ -15,7 +14,6 @@ steps:
     - pytest -v -s tests/kernels/ir
 
 - label: Kernels Core Operation Test
-  key: kernels-core-operation-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -25,7 +23,6 @@ steps:
     - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py
 
 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
-  key: kernels-minimax-reduce-rms-test-2-gpus
   timeout_in_minutes: 15
   num_devices: 2
   device: h100
@@ -39,7 +36,6 @@ steps:
     - pytest -v -s kernels/core/test_minimax_reduce_rms.py
 
 - label: Kernels Attention Test %N
-  key: kernels-attention-test
   timeout_in_minutes: 35
   source_file_dependencies:
   - csrc/attention/
@@ -53,7 +49,6 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  key: kernels-quantization-test
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/quantization/
@@ -62,22 +57,8 @@ steps:
   commands:
     - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
-  mirror:
-    amd:
-      device: mi300_1
-      source_file_dependencies:
-      - csrc/quantization/
-      - vllm/model_executor/layers/quantization
-      - tests/kernels/quantization
-      - tests/kernels/quantization/test_rocm_skinny_gemms.py
-      - vllm/_aiter_ops.py
-      - vllm/platforms/rocm.py
-      - vllm/model_executor/kernels/
-      depends_on:
-      - image-build-amd
 
 - label: Kernels MoE Test %N
-  key: kernels-moe-test
   timeout_in_minutes: 25
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
@@ -93,7 +74,6 @@ steps:
   parallelism: 5
 
 - label: Kernels Mamba Test
-  key: kernels-mamba-test
   timeout_in_minutes: 45
   source_file_dependencies:
   - csrc/mamba/
@@ -102,28 +82,12 @@ steps:
   commands:
     - pytest -v -s kernels/mamba
 
-- label: Kernels KDA Test
-  timeout_in_minutes: 20
-  device: h200_18gb
-  source_file_dependencies:
-  - vllm/model_executor/layers/fla/ops/kda.py
-  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
-  - vllm/model_executor/layers/fla/ops/l2norm.py
-  - tests/kernels/test_kda.py
-  commands:
-    - pytest -v -s kernels/test_kda.py
-
 - label: Kernels DeepGEMM Test (H100)
-  key: kernels-deepgemm-test-h100
   timeout_in_minutes: 45
   device: h100
   num_devices: 1
   source_file_dependencies:
-  - cmake/external_projects/deepgemm.cmake
   - tools/install_deepgemm.sh
-  - tools/build_deepgemm_C.py
-  - tools/setup_deepgemm_pythons.sh
-  - tools/check_wheel_deepgemm.py
   - vllm/utils/deep_gemm.py
   - vllm/model_executor/layers/fused_moe
   - vllm/model_executor/layers/quantization
@@ -131,31 +95,24 @@ steps:
   - tests/kernels/moe/test_deepgemm.py
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
-  - tests/quantization/test_cutlass_w4a16.py
   commands:
-    - python3 ../tools/check_wheel_deepgemm.py
     - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
-    - pytest -v -s quantization/test_cutlass_w4a16.py
 
 - label: Kernels (B200)
-  key: kernels-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
   - csrc/attention/mla/
   - csrc/quantization/cutlass_w8a8/moe/
-  - vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/nvfp4.py
-  - vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py
-  - vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py
+  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_a2a_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
@@ -184,17 +141,14 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
     - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
-    - pytest -v -s tests/kernels/moe/test_mxfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
     - pytest -v -s tests/kernels/moe/test_flashinfer_moe.py
-    - pytest -v -s tests/kernels/moe/test_trtllm_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
     # e2e
     - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Kernels Helion Test
-  key: kernels-helion-test
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -206,7 +160,6 @@ steps:
 
  
 - label: Kernels FP8 MoE Test (1 H100)
-  key: kernels-fp8-moe-test-1-h100
   timeout_in_minutes: 90
   device: h100
   num_devices: 1
@@ -223,7 +176,6 @@ steps:
     - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
 
 - label: Kernels FP8 MoE Test (2 H100s)
-  key: kernels-fp8-moe-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -233,9 +185,8 @@ steps:
     - pytest -v -s kernels/moe/test_deepep_moe.py
 
 - label: Kernels Fp4 MoE Test (B200)
-  key: kernels-fp4-moe-test-b200
   timeout_in_minutes: 60
-  device: b200-k8s
+  device: b200
   num_devices: 1
   optional: true
   commands:
@@ -246,7 +197,6 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 H100s)
-  key: kernels-fusedmoe-layer-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -263,9 +213,8 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 B200s)
-  key: kernels-fusedmoe-layer-test-2-b200s
   timeout_in_minutes: 90
-  device: b200-k8s
+  device: b200
   num_devices: 2
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/

.buildkite/test_areas/lm_eval.yaml

@@ -3,8 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: LM Eval Small Models
-  device: h200_35gb
-  key: lm-eval-small-models
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -26,7 +24,6 @@ steps:
 #   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
-  key: lm-eval-large-models-4-gpus-h100
   device: h100
   optional: true
   num_devices: 4
@@ -39,9 +36,8 @@ steps:
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
 - label: LM Eval Small Models (B200)
-  key: lm-eval-small-models-b200
   timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
   optional: true
   source_file_dependencies:
   - csrc/
@@ -49,22 +45,9 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
 
-- label: LM Eval Large Models (B200, EP)
-  key: lm-eval-large-models-b200-ep
-  timeout_in_minutes: 120
-  device: b200-k8s
-  optional: true
-  num_devices: 2
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell-ep.txt
-
 - label: LM Eval Qwen3.5 Models (B200)
-  key: lm-eval-qwen3-5-models-b200
   timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   source_file_dependencies:
@@ -79,7 +62,6 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-blackwell.txt
 
 - label: LM Eval Large Models (H200)
-  key: lm-eval-large-models-h200
   timeout_in_minutes: 60
   device: h200
   optional: true
@@ -88,7 +70,6 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
 
 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
-  key: moe-refactor-integration-test-h100-temporary
   device: h100
   optional: true
   num_devices: 2
@@ -96,16 +77,14 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
   
 - label: MoE Refactor Integration Test (B200 - TEMPORARY)
-  key: moe-refactor-integration-test-b200-temporary
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
 
 - label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
-  key: moe-refactor-integration-test-b200-dp-temporary
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   commands:
@@ -113,9 +92,7 @@ steps:
 
 
 - label: LM Eval TurboQuant KV Cache
-  key: lm-eval-turboquant-kv-cache
   timeout_in_minutes: 75
-  device: h200_18gb
   source_file_dependencies:
   - vllm/model_executor/layers/quantization/turboquant/
   - vllm/v1/attention/backends/turboquant_attn.py
@@ -125,7 +102,6 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/models-turboquant.txt
 
 - label: GPQA Eval (GPT-OSS) (H100)
-  key: gpqa-eval-gpt-oss-h100
   timeout_in_minutes: 120
   device: h100
   optional: true
@@ -139,9 +115,8 @@ steps:
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
 
 - label: GPQA Eval (GPT-OSS) (B200)
-  key: gpqa-eval-gpt-oss-b200
   timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   source_file_dependencies:
@@ -151,11 +126,3 @@ steps:
   commands:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt
-
-- label: MRCR Eval Small Models
-  device: h200_35gb
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - tests/evals/mrcr/
-  commands:
-    - pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt

.buildkite/test_areas/lora.yaml

@@ -3,24 +3,20 @@ depends_on:
   - image-build
 steps:
 - label: LoRA %N
-  device: h200_35gb
-  key: lora
   timeout_in_minutes: 30
   source_file_dependencies:
   - vllm/lora
   - tests/lora
   commands:
-    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_qwen3_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py 
+    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py 
   parallelism: 4
 
 
 - label: LoRA TP (Distributed)
-  key: lora-tp-distributed
   timeout_in_minutes: 30
   num_devices: 4
   source_file_dependencies:
   - vllm/lora
-  - vllm/model_executor/layers/fused_moe/
   - tests/lora
   commands:
     # FIXIT: find out which code initialize cuda before running the test
@@ -32,7 +28,7 @@ steps:
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_qwen3_with_multi_loras.py
+    - pytest -v -s -x lora/test_llm_with_multi_loras.py
     - pytest -v -s -x lora/test_olmoe_tp.py
     - pytest -v -s -x lora/test_gptoss_tp.py
     - pytest -v -s -x lora/test_qwen35_densemodel_lora.py

.buildkite/test_areas/misc.yaml

@@ -3,41 +3,25 @@ depends_on:
   - image-build
 steps:
 - label: V1 Spec Decode
-  device: h200_35gb
-  key: v1-spec-decode
   timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/inputs/
-    - vllm/model_executor/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1/spec_decode
   commands:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # TODO: create another `optional` test group for slow tests
     - pytest -v -s -m 'not slow_test' v1/spec_decode
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: V1 Sample + Logits
-  key: v1-sample-logits
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/inputs/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1/sample
     - tests/v1/logits_processors
     - tests/v1/test_oracle.py
@@ -52,35 +36,17 @@ steps:
     - pytest -v -s v1/test_outputs.py
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: V1 Core + KV + Metrics
-  key: v1-core-kv-metrics
   timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/entrypoints/pooling/
-    - vllm/inputs/
-    - vllm/lora/
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/outputs.py
-    - vllm/platforms/
-    - vllm/pooling_params.py
-    - vllm/profiler/
-    - vllm/sampling_params.py
-    - vllm/tokenizers/
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1/core
     - tests/v1/executor
     - tests/v1/kv_offload
-    - tests/v1/simple_kv_offload
     - tests/v1/worker
     - tests/v1/kv_connector/unit
     - tests/v1/metrics
@@ -92,34 +58,23 @@ steps:
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor
     - pytest -v -s v1/kv_offload
-    - pytest -v -s v1/simple_kv_offload
     - pytest -v -s v1/worker
     - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
     - pytest -v -s -m 'not cpu_test' v1/metrics
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: V1 Others (CPU)
-  key: v1-others-cpu
   depends_on:
     - image-build-cpu
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/inputs/
-    - vllm/lora/
-    - vllm/multimodal/
-    - vllm/outputs.py
-    - vllm/platforms/
-    - vllm/pooling_params.py
-    - vllm/profiler/
-    - vllm/sampling_params.py
-    - vllm/tokenizers/
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1
   device: cpu-small
   commands:
@@ -130,35 +85,11 @@ steps:
     - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
     - pytest -v -s -m 'cpu_test' v1/metrics
 
-- label: Extract Hidden States Integration
-  key: extract-hidden-states-integration
-  timeout_in_minutes: 20
-  device: h200_18gb
-  source_file_dependencies:
-    - vllm/v1/spec_decode/extract_hidden_states.py
-    - vllm/model_executor/models/extract_hidden_states.py
-    - vllm/transformers_utils/configs/extract_hidden_states.py
-    - tests/v1/kv_connector/extract_hidden_states_integration
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s v1/kv_connector/extract_hidden_states_integration
-
 - label: Regression
-  key: regression
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/test_regression
   commands:
   - pip install modelscope
@@ -166,8 +97,6 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Examples
-  device: h200_35gb
-  key: examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -184,37 +113,25 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
      # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Metrics, Tracing (2 GPUs)
-  key: metrics-tracing-2-gpus
   timeout_in_minutes: 20
   num_devices: 2
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/tracing/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/v1/tracing
   commands:
   - "pip install \
@@ -225,11 +142,8 @@ steps:
   - pytest -v -s v1/tracing
 
 - label: Python-only Installation
-  key: python-only-installation
   depends_on: ~
-  optional: true
   timeout_in_minutes: 20
-  device: h200_18gb
   source_file_dependencies:
   - tests/standalone_tests/python_only_compile.sh
   - setup.py
@@ -237,23 +151,9 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Async Engine, Inputs, Utils, Worker
-  device: h200_35gb
-  key: async-engine-inputs-utils-worker
   timeout_in_minutes: 50
   source_file_dependencies:
-  - vllm/assets/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/tokenizers/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/detokenizer
   - tests/multimodal
   - tests/utils_
@@ -263,35 +163,11 @@ steps:
   - pytest -v -s utils_
 
 - label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  key: async-engine-inputs-utils-worker-config-cpu
-  depends_on:
+  depends_on: 
   - image-build-cpu
   timeout_in_minutes: 30
   source_file_dependencies:
-  - vllm/assets/
-  - vllm/config/
-  - vllm/engine/arg_utils.py
-  - vllm/entrypoints/chat_utils.py
-  - vllm/entrypoints/mcp/
-  - vllm/entrypoints/openai/chat_completion/protocol.py
-  - vllm/entrypoints/openai/engine/protocol.py
-  - vllm/envs.py
-  - vllm/exceptions.py
-  - vllm/inputs/
-  - vllm/model_executor/layers/quantization/quark/
-  - vllm/multimodal/
-  - vllm/outputs.py
-  - vllm/platforms/
-  - vllm/pooling_params.py
-  - vllm/ray/
-  - vllm/reasoning/
-  - vllm/renderers/
-  - vllm/sampling_params.py
-  - vllm/tokenizers/
-  - vllm/tool_parsers/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/test_pooling_params.py
@@ -302,7 +178,6 @@ steps:
   - tests/tokenizers_
   - tests/reasoning
   - tests/tool_parsers
-  - tests/parser
   - tests/transformers_utils
   - tests/config
   device: cpu-small
@@ -315,28 +190,12 @@ steps:
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
-  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py --ignore=reasoning/test_gemma4_reasoning_parser.py
   - pytest -v -s tool_parsers
-  - pytest -v -s parser
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
-- label: Batch Invariance (A100)
-  key: batch-invariance-a100
-  timeout_in_minutes: 30
-  device: a100
-  source_file_dependencies:
-    - vllm/v1/attention
-    - vllm/model_executor/layers
-    - tests/v1/determinism/
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
-
 - label: Batch Invariance (H100)
-  key: batch-invariance-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -352,9 +211,8 @@ steps:
     - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
 
 - label: Batch Invariance (B200)
-  key: batch-invariance-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
     - vllm/v1/attention
     - vllm/model_executor/layers
@@ -367,11 +225,8 @@ steps:
     - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
     - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
     - pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
-    - pytest -v -s v1/determinism/test_nvfp4_batch_invariant_scaled_mm.py
   
 - label: Acceptance Length Test (Large Models) # optional
-  device: h200_35gb
-  key: acceptance-length-test-large-models
   timeout_in_minutes: 25
   gpu: h100
   optional: true

.buildkite/test_areas/model_executor.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Model Executor
-  key: model-executor
   timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/engine/arg_utils.py

.buildkite/test_areas/model_runner_v2.yaml

@@ -3,8 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Model Runner V2 Core Tests
-  device: h200_35gb
-  key: model-runner-v2-core-tests
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/v1/worker/gpu/
@@ -27,19 +25,16 @@ steps:
   - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
 
 - label: Model Runner V2 Examples
-  device: h200_35gb
-  key: model-runner-v2-examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
     - vllm/v1/worker/gpu/
     - vllm/v1/core/sched/
     - vllm/v1/worker/gpu_worker.py
+    - examples/offline_inference/
     - examples/basic/offline_inference/
-    - examples/generate/multimodal/
-    - examples/features/
     - examples/pooling/embed/vision_embedding_offline.py
-    - examples/features/tensorize_vllm_model.py
+    - examples/others/tensorize_vllm_model.py
   commands:
     - set -x
     - export VLLM_USE_V2_MODEL_RUNNER=1
@@ -49,22 +44,21 @@ steps:
     #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
     #- python3 basic/offline_inference/embed.py   # TODO
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Model Runner V2 Distributed (2 GPUs)
-  key: model-runner-v2-distributed-2-gpus
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -85,7 +79,6 @@ steps:
     - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
 
 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
-  key: model-runner-v2-pipeline-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -101,20 +94,19 @@ steps:
     - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
 
 - label: Model Runner V2 Spec Decode
-  device: h200_35gb
-  key: model-runner-v2-spec-decode
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/v1/worker/gpu/
   - vllm/v1/worker/gpu_worker.py
   - tests/v1/spec_decode/test_max_len.py
-  - tests/v1/spec_decode/test_rejection_sampler_utils.py
+  - tests/v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
+  - tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
   - tests/v1/e2e/spec_decode/test_spec_decode.py
   commands:
   - set -x
   - export VLLM_USE_V2_MODEL_RUNNER=1
   - pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
-  - pytest -v -s v1/spec_decode/test_rejection_sampler_utils.py
+  - pytest -v -s v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
   - pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
   - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"

.buildkite/test_areas/models_basic.yaml

@@ -3,9 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Basic Models Tests (Initialization)
-  key: basic-models-tests-initialization
   timeout_in_minutes: 45
-  device: h200_18gb
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -18,8 +16,6 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Extra Initialization) %N
-  device: h200_35gb
-  key: basic-models-tests-extra-initialization
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -35,8 +31,6 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Other)
-  device: h200_35gb
-  key: basic-models-tests-other
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -45,9 +39,14 @@ steps:
   - tests/models/test_registry.py
   commands:
     - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+
 
 - label: Basic Models Test (Other CPU) # 5min
-  key: basic-models-test-other-cpu
   depends_on:
   - image-build-cpu
   timeout_in_minutes: 10
@@ -60,8 +59,6 @@ steps:
     - pytest -v -s models/test_utils.py models/test_vision.py
 
 - label: Transformers Nightly Models
-  device: h200_35gb
-  key: transformers-nightly-models
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -72,13 +69,11 @@ steps:
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
 - label: Transformers Backward Compatibility Models Test
-  device: h200_35gb
-  key: transformers-backward-compatibility-models-test
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -88,7 +83,7 @@ steps:
     - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

.buildkite/test_areas/models_distributed.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Model Tests (2 GPUs)
-  key: distributed-model-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/models_language.yaml

@@ -3,9 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Language Models Tests (Standard)
-  key: language-models-tests-standard
   timeout_in_minutes: 25
-  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/models/language
@@ -17,7 +15,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Extra Standard) %N
-  key: language-models-tests-extra-standard
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -34,7 +31,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Hybrid) %N
-  key: language-models-tests-hybrid
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/
@@ -49,18 +45,8 @@ steps:
   parallelism: 2
   mirror:
     torch_nightly: {}
-    amd:
-      device: mi300_1
-      depends_on:
-      - image-build-amd
-      commands:
-      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
-      - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
 - label: Language Models Test (Extended Generation) # 80min
-  device: h200_35gb
-  key: language-models-test-extended-generation
   timeout_in_minutes: 110
   optional: true
   source_file_dependencies:
@@ -72,9 +58,17 @@ steps:
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+      commands:
+      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+      - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
-  key: language-models-test-ppl
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true
@@ -85,8 +79,6 @@ steps:
     - pytest -v -s models/language/generation_ppl_test
 
 - label: Language Models Test (Extended Pooling)  # 36min
-  device: h200_35gb
-  key: language-models-test-extended-pooling
   timeout_in_minutes: 50
   optional: true
   source_file_dependencies:
@@ -96,13 +88,11 @@ steps:
     - pytest -v -s models/language/pooling -m 'not core_model'
   mirror:
     amd:
-      device: mi300_1
-      timeout_in_minutes: 100
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: Language Models Test (MTEB)
-  key: language-models-test-mteb
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true

.buildkite/test_areas/models_multimodal.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
-  key: multi-modal-models-standard-1-qwen2
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -15,12 +14,11 @@ steps:
     - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
-  key: multi-modal-models-standard-2-qwen3-gemma
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -30,16 +28,13 @@ steps:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
     - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
-    - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
-  device: h200_35gb
-  key: multi-modal-models-standard-3-llava-qwen2-vl
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -50,13 +45,11 @@ steps:
     - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
-  device: h200_35gb
-  key: multi-modal-models-standard-4-other-whisper
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -66,10 +59,14 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
     - pytest models/multimodal/generation/test_memory_leak.py -m core_model
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: Multi-Modal Processor (CPU)
-  key: multi-modal-processor-cpu
-  depends_on:
+  depends_on: 
   - image-build-cpu
   timeout_in_minutes: 60
   source_file_dependencies:
@@ -82,7 +79,6 @@ steps:
     - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Processor # 44min
-  key: multi-modal-processor
   timeout_in_minutes: 60
   device: h200_18gb
   source_file_dependencies:
@@ -94,8 +90,6 @@ steps:
     - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  device: h200_35gb
-  key: multi-modal-accuracy-eval-small-models
   timeout_in_minutes: 70
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
@@ -106,7 +100,6 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
 - label: Multi-Modal Models (Extended Generation 1)
-  key: multi-modal-models-extended-generation-1
   optional: true
   source_file_dependencies:
   - vllm/
@@ -118,13 +111,11 @@ steps:
     - pytest -v -s models/multimodal/test_mapping.py
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: Multi-Modal Models (Extended Generation 2)
-  device: h200_35gb
-  key: multi-modal-models-extended-generation-2
   optional: true
   source_file_dependencies:
   - vllm/
@@ -134,8 +125,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models (Extended Generation 3)
-  device: h200_35gb
-  key: multi-modal-models-extended-generation-3
   optional: true
   source_file_dependencies:
   - vllm/
@@ -145,7 +134,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Multi-Modal Models (Extended Pooling)
-  key: multi-modal-models-extended-pooling
   optional: true
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/plugins.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Plugin Tests (2 GPUs)
-  key: plugin-tests-2-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/pytorch.yaml

@@ -3,34 +3,9 @@ depends_on:
   - image-build
 steps:
 - label: PyTorch Compilation Unit Tests
-  device: h200_35gb
-  key: pytorch-compilation-unit-tests
   timeout_in_minutes: 10
   source_file_dependencies:
-    - vllm/__init__.py
-    - vllm/_aiter_ops.py
-    - vllm/_custom_ops.py
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/env_override.py
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/ir/
-    - vllm/kernels/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/plugins/
-    - vllm/sampling_params.py
-    - vllm/sequence.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/compile
   commands:
   # Run unit tests defined directly under compile/,
@@ -43,99 +18,27 @@ steps:
   - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Unit Tests (H100)
-  key: pytorch-compilation-unit-tests-h100
   timeout_in_minutes: 30
   device: h100
   num_devices: 1
   source_file_dependencies:
-    - vllm/__init__.py
-    - vllm/_aiter_ops.py
-    - vllm/_custom_ops.py
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/env_override.py
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/ir/
-    - vllm/kernels/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/plugins/
-    - vllm/sampling_params.py
-    - vllm/sequence.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/compile/h100/
   commands:
   - "find compile/h100/ -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Passes Unit Tests
-  key: pytorch-compilation-passes-unit-tests
   timeout_in_minutes: 20
   source_file_dependencies:
-    - vllm/__init__.py
-    - vllm/_aiter_ops.py
-    - vllm/_custom_ops.py
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/env_override.py
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/ir/
-    - vllm/kernels/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/plugins/
-    - vllm/sampling_params.py
-    - vllm/sequence.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/compile/passes
   commands:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
 
 - label: PyTorch Fullgraph Smoke Test
-  key: pytorch-fullgraph-smoke-test
   timeout_in_minutes: 35
   source_file_dependencies:
-  - vllm/__init__.py
-  - vllm/_aiter_ops.py
-  - vllm/_custom_ops.py
-  - vllm/compilation/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/env_override.py
-  - vllm/envs.py
-  - vllm/forward_context.py
-  - vllm/inputs/
-  - vllm/ir/
-  - vllm/kernels/
-  - vllm/logger.py
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/plugins/
-  - vllm/sampling_params.py
-  - vllm/sequence.py
-  - vllm/transformers_utils/
-  - vllm/triton_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/compile
   commands:
   # Run smoke tests under fullgraph directory, except test_full_graph.py
@@ -145,41 +48,16 @@ steps:
   - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph
-  key: pytorch-fullgraph
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
-  - vllm/__init__.py
-  - vllm/_aiter_ops.py
-  - vllm/_custom_ops.py
-  - vllm/compilation/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/env_override.py
-  - vllm/envs.py
-  - vllm/forward_context.py
-  - vllm/inputs/
-  - vllm/ir/
-  - vllm/kernels/
-  - vllm/logger.py
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/plugins/
-  - vllm/sampling_params.py
-  - vllm/sequence.py
-  - vllm/transformers_utils/
-  - vllm/triton_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/compile
   commands:
     # fp8 kv scales not supported on sm89, tested on Blackwell instead
   - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
 
 - label: Pytorch Nightly Dependency Override Check # 2min
-  key: pytorch-nightly-dependency-override-check
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
   # in /vllm/tools/pre_commit/generate_nightly_torch_test.py

.buildkite/test_areas/quantization.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Quantization
-  key: quantization
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/
@@ -22,10 +21,9 @@ steps:
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: Quantized MoE Test (B200)
-  key: quantized-moe-test-b200
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
   - tests/quantization/test_blackwell_moe.py
   - vllm/model_executor/models/deepseek_v2.py
@@ -40,7 +38,6 @@ steps:
     - pytest -s -v tests/quantization/test_blackwell_moe.py
 
 - label: Quantized Models Test
-  key: quantized-models-test
   timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/model_executor/layers/quantization

.buildkite/test_areas/ray_compat.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Ray Dependency Compatibility Check
-  key: ray-dependency-compatibility-check
   # Informational only — does not block the pipeline.
   # If this fails, it means the PR introduces a dependency that
   # conflicts with Ray's dependency constraints.

.buildkite/test_areas/rust_frontend.yaml

@@ -1,107 +0,0 @@
-group: Rust Frontend E2E
-depends_on:
-  - image-build
-steps:
-- label: Rust Frontend OpenAI Coverage
-  timeout_in_minutes: 90
-  device: h200_18gb
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/benchmarks/
-  - vllm/entrypoints/openai/
-  - vllm/entrypoints/serve/
-  - vllm/v1/sample/
-  - tests/utils.py
-  - tests/benchmarks/test_serve_cli.py
-  - tests/entrypoints/openai/chat_completion/test_chat_completion.py
-  # - tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
-  # - tests/entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py
-  # - tests/entrypoints/openai/completion/test_prompt_validation.py
-  - tests/entrypoints/openai/completion/test_shutdown.py
-  # - tests/entrypoints/openai/test_return_token_ids.py
-  # - tests/entrypoints/openai/test_uds.py
-  - tests/v1/sample/test_logprobs_e2e.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
-  - pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py
-  # - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not invalid"
-  # - pytest -v -s entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py
-  # - pytest -v -s entrypoints/openai/completion/test_prompt_validation.py -k "not prompt_embeds"
-  - pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
-  # - pytest -v -s entrypoints/openai/test_return_token_ids.py
-  # - pytest -v -s entrypoints/openai/test_uds.py
-  - pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
-
-- label: Rust Frontend Serve/Admin Coverage
-  timeout_in_minutes: 60
-  device: h200_18gb
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - vllm/entrypoints/serve/
-  - vllm/v1/engine/
-  - tests/utils.py
-  # - tests/entrypoints/rpc/test_collective_rpc.py
-  - tests/entrypoints/serve/disagg/test_serving_tokens.py
-  - tests/entrypoints/serve/instrumentator/test_basic.py
-  - tests/entrypoints/serve/instrumentator/test_metrics.py
-  # - tests/entrypoints/serve/instrumentator/test_sleep.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  # - pytest -v -s entrypoints/rpc/test_collective_rpc.py
-  - pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
-  - pytest -v -s entrypoints/serve/disagg/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
-  - pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
-  # - pytest -v -s entrypoints/serve/instrumentator/test_sleep.py
-
-- label: Rust Frontend Core Correctness
-  timeout_in_minutes: 30
-  device: h200_18gb
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - tests/utils.py
-  - tests/entrypoints/openai/correctness/test_lmeval.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
-- label: Rust Frontend Tool Use
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - vllm/tool_parsers/
-  - tests/utils.py
-  - tests/tool_use/
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
-
-- label: Rust Frontend Distributed
-  timeout_in_minutes: 30
-  num_devices: 4
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/utils.py
-  - tests/v1/distributed/test_internal_lb_dp.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py -k "not 4 and not server_info"

.buildkite/test_areas/rust_frontend_cargo.yaml

@@ -1,30 +0,0 @@
-group: Rust Frontend Cargo
-depends_on: []
-steps:
-- label: Rust Frontend Cargo Style + Clippy
-  key: rust-frontend-cargo-style-clippy
-  depends_on: []
-  timeout_in_minutes: 30
-  device: cpu-medium
-  no_plugin: true
-  source_file_dependencies:
-  - rust/
-  - rust-toolchain.toml
-  - .buildkite/test_areas/rust_frontend_cargo.yaml
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh
-  commands:
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh style-clippy
-
-- label: Rust Frontend Cargo Tests
-  key: rust-frontend-cargo-tests
-  depends_on: []
-  timeout_in_minutes: 30
-  device: cpu-medium
-  no_plugin: true
-  source_file_dependencies:
-  - rust/
-  - rust-toolchain.toml
-  - .buildkite/test_areas/rust_frontend_cargo.yaml
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh
-  commands:
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh test

.buildkite/test_areas/samplers.yaml

@@ -3,23 +3,18 @@ depends_on:
   - image-build
 steps:
 - label: Samplers Test
-  device: h200_35gb
-  key: samplers-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers
   - vllm/sampling_metadata.py
   - tests/samplers
   - tests/conftest.py
-  - vllm/entrypoints/generate/beam_search
   commands:
-    # VLLM_USE_FLASHINFER_SAMPLER defaults to 1 now, so we need to pin both
-    # values explicitly to still cover the PyTorch-native (Triton) path.
-    - VLLM_USE_FLASHINFER_SAMPLER=0 pytest -v -s samplers
+    - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:
-      device: mi250_1
+      device: mi325_1
       depends_on:
       - image-build-amd
       commands:

.buildkite/test_areas/spec_decode.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Spec Decode Eagle
-  key: spec-decode-eagle
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -13,20 +12,7 @@ steps:
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
-- label: Spec Decode Eagle Nightly B200
-  key: spec-decode-eagle-nightly-b200
-  timeout_in_minutes: 30
-  device: b200-k8s
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/spec_decode/
-    - vllm/v1/worker/gpu/spec_decode/
-    - tests/v1/e2e/spec_decode/
-  commands:
-    - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
-
 - label: Spec Decode Speculators + MTP
-  key: spec-decode-speculators-mtp
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -37,21 +23,7 @@ steps:
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
-- label: Spec Decode Speculators + MTP Nightly B200
-  key: spec-decode-speculators-mtp-nightly-b200
-  timeout_in_minutes: 30
-  device: b200-k8s
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/spec_decode/
-    - vllm/v1/worker/gpu/spec_decode/
-    - vllm/transformers_utils/configs/speculators/
-    - tests/v1/e2e/spec_decode/
-  commands:
-    - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
-  
 - label: Spec Decode Ngram + Suffix
-  key: spec-decode-ngram-suffix
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -62,7 +34,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
 
 - label: Spec Decode Draft Model
-  key: spec-decode-draft-model
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -72,41 +43,15 @@ steps:
   commands:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
-- label: Spec Decode Draft Model Nightly B200
-  key: spec-decode-draft-model-nightly-b200
+- label: DFlash Speculators Correctness
   timeout_in_minutes: 30
-  device: b200-k8s
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/spec_decode/
-    - vllm/v1/worker/gpu/spec_decode/
-    - tests/v1/e2e/spec_decode/
-  commands:
-    - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
-
-- label: Speculators Correctness
-  key: speculators-correctness
-  timeout_in_minutes: 60
   device: h100
   optional: true
   num_devices: 1
   source_file_dependencies:
     - vllm/v1/spec_decode/
     - vllm/model_executor/models/qwen3_dflash.py
-    - tests/v1/spec_decode/test_speculators_correctness.py
+    - tests/v1/spec_decode/test_speculators_dflash.py
   commands:
     - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
-    - pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
-
-- label: Spec Decode MTP hybrid (B200)
-  timeout_in_minutes: 30
-  device: b200-k8s
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/spec_decode/
-    - vllm/v1/worker/gpu/spec_decode/
-    - vllm/model_executor/models/qwen3_5.py
-    - vllm/model_executor/models/qwen3_5_mtp.py
-    - tests/v1/e2e/spec_decode/
-  commands:
-    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"
+    - pytest -v -s v1/spec_decode/test_speculators_dflash.py -m slow_test

.buildkite/test_areas/weight_loading.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Weight Loading Multiple GPU  # 33min
-  key: weight-loading-multiple-gpu
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.dockerignore

@@ -2,7 +2,6 @@
 /build
 dist
 vllm/*.so
-vllm/vllm-rs
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -32,4 +31,3 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
-rust/target/

.github/CODEOWNERS

@@ -6,8 +6,8 @@
 /vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery @xuechendi
 /vllm/lora @jeejeelee
 /vllm/model_executor/layers/attention @LucasWilkinson @MatthewBonanni
-/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety @zyongye
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety @zyongye
+/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
 /vllm/model_executor/layers/mamba @tdoublep @tomeras91
 /vllm/model_executor/layers/mamba/gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
 /vllm/model_executor/layers/rotary_embedding.py @vadiklyutiy
@@ -18,8 +18,7 @@
 /vllm/kernels/helion @ProExpertProg @zou3519
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
 /vllm/vllm_flash_attn @LucasWilkinson @MatthewBonanni
-/CMakeLists.txt @tlrmchlsmth @LucasWilkinson @Harry-Chen
-/cmake @tlrmchlsmth @LucasWilkinson @Harry-Chen
+CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
@@ -31,8 +30,8 @@
 /vllm/entrypoints/cli @hmellor @mgoin @DarkLight1337 @russellb
 /vllm/entrypoints/mcp @heheda12345
 /vllm/entrypoints/openai @aarnphm @chaunceyjiang @DarkLight1337 @russellb
-/vllm/entrypoints/speech_to_text/realtime @njhill
-/vllm/entrypoints/speech_to_text @NickLucche
+/vllm/entrypoints/openai/realtime @njhill
+/vllm/entrypoints/openai/speech_to_text @NickLucche
 /vllm/entrypoints/pooling @noooop
 /vllm/entrypoints/sagemaker @DarkLight1337
 /vllm/entrypoints/serve @njhill
@@ -71,10 +70,6 @@
 /vllm/v1/worker/gpu @WoosukKwon @njhill
 /vllm/v1/worker/gpu/kv_connector.py @orozery
 
-# CI & building
-/.buildkite @Harry-Chen
-/docker/Dockerfile @Harry-Chen
-
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin 
 /tests/distributed/test_multi_node_assignment.py @youkaichao
@@ -82,11 +77,11 @@
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche
 /tests/evals @mgoin @vadiklyutiy
-/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye
+/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/kernels/ir @ProExpertProg @tjtanaa
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -152,10 +147,6 @@ mkdocs.yaml @hmellor
 # MTP-specific files
 /vllm/model_executor/models/deepseek_mtp.py @luccafong
 
-# DeepseekV4-specific files
-/vllm/models/deepseek_v4 @zyongye
-/vllm/model_executor/layers/sparse_attn_indexer.py @zyongye
-
 # Mistral-specific files
 /vllm/model_executor/models/mistral*.py @patrickvonplaten
 /vllm/model_executor/models/mixtral*.py @patrickvonplaten
@@ -170,13 +161,13 @@ mkdocs.yaml @hmellor
 /vllm/model_executor/layers/fla @ZJY0516 @vadiklyutiy
 
 # ROCm related: specify owner with write access to notify AMD folks for careful code review
-/vllm/**/*rocm* @tjtanaa @dllehr-amd
-/docker/Dockerfile.rocm* @tjtanaa @dllehr-amd
-/vllm/v1/attention/backends/rocm*.py @tjtanaa @dllehr-amd
-/vllm/v1/attention/backends/mla/rocm*.py @tjtanaa @dllehr-amd
-/vllm/v1/attention/ops/rocm*.py @tjtanaa @dllehr-amd
-/vllm/model_executor/layers/fused_moe/rocm*.py @tjtanaa @dllehr-amd
-/csrc/rocm @tjtanaa @dllehr-amd
+/vllm/**/*rocm* @tjtanaa
+/docker/Dockerfile.rocm* @gshtras @tjtanaa
+/vllm/v1/attention/backends/rocm*.py @gshtras @tjtanaa
+/vllm/v1/attention/backends/mla/rocm*.py @gshtras @tjtanaa
+/vllm/v1/attention/ops/rocm*.py @gshtras @tjtanaa
+/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras @tjtanaa
+/csrc/rocm @gshtras @tjtanaa
 /requirements/*rocm* @tjtanaa
 /tests/**/*rocm* @tjtanaa
 /docs/**/*rocm* @tjtanaa

.github/mergify.yml

@@ -262,7 +262,7 @@ pull_request_rules:
       - files~=^docker/Dockerfile.xpu
       - files~=^\\.buildkite/intel_jobs/
       - files=\.buildkite/ci_config_intel.yaml
-      - files=vllm/model_executor/layers/fused_moe/experts/xpu_moe.py
+      - files=vllm/model_executor/layers/fused_moe/xpu_fused_moe.py
       - files=vllm/model_executor/kernels/linear/mixed_precision/xpu.py
       - files=vllm/model_executor/kernels/linear/mxfp8/xpu.py
       - files=vllm/model_executor/kernels/linear/scaled_mm/xpu.py
@@ -308,7 +308,8 @@ pull_request_rules:
       - files=benchmarks/benchmark_serving_structured_output.py
       - files=benchmarks/run_structured_output_benchmark.sh
       - files=docs/features/structured_outputs.md
-      - files=^examples/features/structured_outputs/
+      - files=examples/offline_inference/structured_outputs.py
+      - files=examples/online_serving/structured_outputs/structured_outputs.py
       - files~=^tests/v1/structured_output/
       - files=tests/entrypoints/llm/test_struct_output_generate.py
       - files~=^vllm/v1/structured_output/
@@ -324,7 +325,7 @@ pull_request_rules:
     - or:
       - files~=^vllm/v1/spec_decode/
       - files~=^tests/v1/spec_decode/
-      - files=^examples/features/speculative_decoding/
+      - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
       - files~=^vllm/model_executor/models/.*eagle.*\.py
       - files=vllm/model_executor/models/mlp_speculator.py
       - files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
@@ -388,7 +389,11 @@ pull_request_rules:
       - files~=^tests/entrypoints/anthropic/.*tool.*
       - files~=^vllm/tool_parsers/
       - files=docs/features/tool_calling.md
-      - files~=^examples/tool_calling/
+      - files~=^examples/tool_chat_*
+      - files=examples/offline_inference/chat_with_tools.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
+      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
   actions:
     label:
       add:
@@ -477,7 +482,9 @@ pull_request_rules:
   conditions:
     - label != stale
     - or:
-      - files~=^examples/disaggregated/
+      - files~=^examples/online_serving/disaggregated[^/]*/.*
+      - files~=^examples/offline_inference/disaggregated[^/]*/.*
+      - files~=^examples/others/lmcache/
       - files~=^tests/v1/kv_connector/
       - files~=^vllm/distributed/kv_transfer/
       - title~=(?i)\bP/?D\b

.github/workflows/pre-commit.yml

@@ -16,7 +16,11 @@ permissions:
 
 jobs:
   pre-run-check:
-    if: github.event_name == 'pull_request'
+    if: >-
+      github.event_name == 'pull_request' &&
+      (github.event.action != 'labeled' ||
+       github.event.label.name == 'ready' ||
+       github.event.label.name == 'verified')
     runs-on: ubuntu-latest
     steps:
     - name: Check PR label and author merge count
@@ -45,7 +49,12 @@ jobs:
 
   pre-commit:
     needs: pre-run-check
-    if: always() && (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
+    if: >-
+      always() &&
+      (github.event.action != 'labeled' ||
+       github.event.label.name == 'ready' ||
+       github.event.label.name == 'verified') &&
+      (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1

.github/workflows/scripts/build.sh

@@ -9,15 +9,12 @@ PATH=${cuda_home}/bin:$PATH
 LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH
 
 # Install requirements
-if [ "$(echo $2 | cut -d. -f1)" = "12" ]; then
-    sed -i 's/^nvidia-cutlass-dsl\[cu13\]>=/nvidia-cutlass-dsl>=/' requirements/cuda.txt
-fi
 $python_executable -m pip install -r requirements/build/cuda.txt -r requirements/cuda.txt
 
 # Limit the number of parallel jobs to avoid OOM
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
-export TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"
+export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
 
 bash tools/check_repo.sh
 

.gitignore

@@ -26,9 +26,6 @@ __pycache__/
 # C extensions
 *.so
 
-# Rust binaries
-vllm/vllm-rs
-
 # Distribution / packaging
 .Python
 build/
@@ -240,7 +237,6 @@ ep_kernels_workspace/
 
 # Allow tracked library source folders under submodules (e.g., benchmarks/lib)
 !vllm/benchmarks/lib/
-!.buildkite/scripts/lib/
 
 # Generated gRPC protobuf files (compiled at build time from vllm_engine.proto)
 vllm/grpc/vllm_engine_pb2.py

.pre-commit-config.yaml

@@ -131,16 +131,6 @@ repos:
         --python-version, "3.12",
       ]
       files: ^requirements/(common|xpu|test/xpu)\.(in|txt)$
-    - id: pip-compile
-      alias: pip-compile-docs
-      name: pip-compile-docs
-      args: [
-        requirements/docs.in,
-        -o, requirements/docs.txt,
-        --python-platform, x86_64-manylinux_2_28,
-        --python-version, "3.12",
-      ]
-      files: ^requirements/docs\.(in|txt)$
 - repo: local
   hooks:
   - id: format-torch-nightly-test
@@ -222,12 +212,6 @@ repos:
     name: Update Dockerfile dependency graph
     entry: tools/pre_commit/update-dockerfile-graph.sh
     language: script
-  - id: test-nonroot-entrypoint
-    name: Test non-root entrypoint wrapper
-    entry: bash docker/entrypoints/test_vllm_nonroot_entrypoint.sh
-    language: system
-    pass_filenames: false
-    files: ^docker/entrypoints/(vllm-nonroot-entrypoint|test_vllm_nonroot_entrypoint)\.sh$
   - id: check-forbidden-imports
     name: Check for forbidden imports
     entry: python tools/pre_commit/check_forbidden_imports.py
@@ -262,32 +246,6 @@ repos:
     entry: python tools/pre_commit/check_boolean_context_manager.py
     language: python
     types: [python]
-  # Rust hooks. These shell out to `cargo`; tools/pre_commit/rust-check.sh
-  # skips with a warning when cargo is not installed.
-  - id: rust-cargo-autoinherit
-    name: Rust - Normalize Cargo manifests with autoinherit
-    entry: tools/pre_commit/rust-check.sh autoinherit --prefer-simple-dotted
-    language: script
-    pass_filenames: false
-    require_serial: true
-    stages: [pre-commit] # Only run locally as Buildkite will cover this
-    files: ^rust/(Cargo\.toml|src/.*/Cargo\.toml)$
-  - id: rust-cargo-sort
-    name: Rust - Sort Cargo manifest sections
-    entry: tools/pre_commit/rust-check.sh sort --workspace
-    language: script
-    pass_filenames: false
-    require_serial: true
-    stages: [pre-commit] # Only run locally as Buildkite will cover this
-    files: ^rust/(Cargo\.toml|src/.*/Cargo\.toml)$
-  - id: rust-cargo-fmt
-    name: Rust - Format code
-    entry: tools/pre_commit/rust-check.sh fmt
-    language: script
-    pass_filenames: false
-    require_serial: true
-    stages: [pre-commit] # Only run locally as Buildkite will cover this
-    files: ^rust/.*(\.rs|Cargo\.toml|rustfmt\.toml)$
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

.readthedocs.yaml

@@ -9,7 +9,7 @@ build:
     python: "3.12"
   jobs:
     post_checkout:
-      - bash docs/pre_run_check.sh
+      # - bash docs/maybe_skip_pr_build.sh
       - git fetch origin main --unshallow --no-tags --filter=blob:none || true
     pre_create_environment:
       - pip install uv

CMakeLists.txt

@@ -13,12 +13,8 @@ cmake_minimum_required(VERSION 3.26)
 # cmake --install . --component _C
 project(vllm_extensions LANGUAGES CXX)
 
-set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CUDA_STANDARD 20)
-set(CMAKE_CUDA_STANDARD_REQUIRED ON)
-set(CMAKE_HIP_STANDARD 20)
-set(CMAKE_HIP_STANDARD_REQUIRED ON)
 
 
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
@@ -38,10 +34,10 @@ install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
-set(PYTHON_SUPPORTED_VERSIONS "3.10" "3.11" "3.12" "3.13" "3.14")
+set(PYTHON_SUPPORTED_VERSIONS "3.10" "3.11" "3.12" "3.13")
 
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201")
 
 # ROCm installation prefix. Default to /opt/rocm but allow override via
 # -DROCM_PATH=/your/rocm/path when invoking cmake.
@@ -98,35 +94,14 @@ find_package(Torch REQUIRED)
 # This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
 if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
    CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0)
-   # starting from CUDA 12.9 and Blackwell (10.0), we use family-specific targets (10.0f, 12.0f, etc)
-   # to support the whole generation without specifying all sub-architectures
-   # see: https://developer.nvidia.com/blog/nvidia-blackwell-and-nvidia-cuda-12-9-introduce-family-specific-architecture-features/
-  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;11.0;12.0")
+  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;11.0;12.0;12.1")
 elseif(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
    CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
-  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;10.3;12.0;12.1")
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0;12.1")
 else()
-  set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.7;8.9;9.0")
+  set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
 endif()
 
-#
-# spinloop extension (pure CXX; must stay above the non-CUDA device branch so
-# CPU builds define the target before the early return)
-#
-set(VLLM_SPINLOOP_EXT_SRC "csrc/spinloop.cpp")
-set(SPINLOOP_COMPILE_FLAGS "")
-if(CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64|amd64")
-  list(APPEND SPINLOOP_COMPILE_FLAGS "-mmwaitx")
-endif()
-define_extension_target(
-  spinloop
-  DESTINATION vllm
-  LANGUAGE CXX
-  SOURCES ${VLLM_SPINLOOP_EXT_SRC}
-  COMPILE_FLAGS ${SPINLOOP_COMPILE_FLAGS}
-  USE_SABI 3.11
-  WITH_SOABI)
-
 #
 # Forward the non-CUDA device extensions to external CMake scripts.
 #
@@ -311,19 +286,28 @@ set(VLLM_EXT_SRC
   "csrc/attention/paged_attention_v1.cu"
   "csrc/attention/paged_attention_v2.cu"
   "csrc/attention/merge_attn_states.cu"
+  "csrc/attention/vertical_slash_index.cu"
+  "csrc/pos_encoding_kernels.cu"
+  "csrc/activation_kernels.cu"
+  "csrc/layernorm_kernels.cu"
+  "csrc/fused_qknorm_rope_kernel.cu"
+  "csrc/layernorm_quant_kernels.cu"
   "csrc/sampler.cu"
   "csrc/topk.cu"
   "csrc/cuda_view.cu"
+  "csrc/quantization/gptq/q_gemm.cu"
+  "csrc/quantization/w8a8/int8/scaled_quant.cu"
+  "csrc/quantization/w8a8/fp8/common.cu"
+  "csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
   "csrc/quantization/fused_kernels/fused_silu_mul_block_quant.cu"
+  "csrc/quantization/gguf/gguf_kernel.cu"
   "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/custom_all_reduce.cu"
-  "csrc/torch_bindings.cpp"
-  "csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
+  "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  list(APPEND VLLM_EXT_SRC
-    "csrc/minimax_reduce_rms_kernel.cu")
+  list(APPEND VLLM_EXT_SRC "csrc/minimax_reduce_rms_kernel.cu")
 
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
@@ -358,6 +342,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   FetchContent_MakeAvailable(cutlass)
 
   list(APPEND VLLM_EXT_SRC
+    "csrc/quantization/awq/gemm_kernels.cu"
     "csrc/cutlass_extensions/common.cpp")
 
   set_gencode_flags_for_srcs(
@@ -489,6 +474,46 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                    " in CUDA target architectures")
   endif()
 
+  # Only build AllSpark kernels if we are building for at least some compatible archs.
+  cuda_archs_loose_intersection(ALLSPARK_ARCHS "8.0;8.6;8.7;8.9" "${CUDA_ARCHS}")
+  if (ALLSPARK_ARCHS)
+    set(ALLSPARK_SRCS
+       "csrc/quantization/gptq_allspark/allspark_repack.cu"
+       "csrc/quantization/gptq_allspark/allspark_qgemm_w8a16.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${ALLSPARK_SRCS}"
+      CUDA_ARCHS "${ALLSPARK_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${ALLSPARK_SRCS}")
+    message(STATUS "Building AllSpark kernels for archs: ${ALLSPARK_ARCHS}")
+  else()
+    message(STATUS "Not building AllSpark kernels as no compatible archs found"
+                   " in CUDA target architectures")
+  endif()
+
+  # CUTLASS MLA Archs and flags
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
+    cuda_archs_loose_intersection(MLA_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
+  else()
+    cuda_archs_loose_intersection(MLA_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
+  endif()
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS)
+    set(SRCS
+      "csrc/attention/mla/sm100_cutlass_mla_kernel.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${MLA_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MLA=1")
+    # Add MLA-specific include directories only to MLA source files
+    set_source_files_properties(${SRCS}
+      PROPERTIES INCLUDE_DIRECTORIES "${CUTLASS_DIR}/examples/77_blackwell_fmha;${CUTLASS_DIR}/examples/common")
+    message(STATUS "Building CUTLASS MLA for archs: ${MLA_ARCHS}")
+  else()
+    message(STATUS "Not building CUTLASS MLA as no compatible archs were found.")
+    # clear MLA_ARCHS
+    set(MLA_ARCHS)
+  endif()
+
   # Expert-specialization MXFP8 blockscaled grouped kernels (SM100+).
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
     cuda_archs_loose_intersection(ES_MXFP8_GROUPED_MM_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
@@ -516,6 +541,24 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
+  # DeepSeek V3 fused A GEMM kernel (requires SM 9.0+, Hopper and later)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
+    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0f;11.0f" "${CUDA_ARCHS}")
+  else()
+    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
+  endif()
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND DSV3_FUSED_A_GEMM_ARCHS)
+    set(DSV3_FUSED_A_GEMM_SRC "csrc/dsv3_fused_a_gemm.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${DSV3_FUSED_A_GEMM_SRC}"
+      CUDA_ARCHS "${DSV3_FUSED_A_GEMM_ARCHS}")
+    list(APPEND VLLM_EXT_SRC ${DSV3_FUSED_A_GEMM_SRC})
+    message(STATUS "Building dsv3_fused_a_gemm for archs: ${DSV3_FUSED_A_GEMM_ARCHS}")
+  else()
+    message(STATUS "Not building dsv3_fused_a_gemm as no compatible archs found "
+                   "in CUDA target architectures.")
+  endif()
+
   #
   # Machete kernels
 
@@ -587,6 +630,16 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 
 
+  # Hadacore kernels
+  cuda_archs_loose_intersection(HADACORE_ARCHS "8.0+PTX;9.0+PTX" "${CUDA_ARCHS}")
+  if(HADACORE_ARCHS)
+    set(SRCS "csrc/quantization/hadamard/hadacore/hadamard_transform_cuda.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${HADACORE_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    message(STATUS "Building hadacore")
+  endif()
 
 # if CUDA endif
 endif()
@@ -618,71 +671,31 @@ define_extension_target(
 # Setting this variable sidesteps the issue by calling the driver directly.
 target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 
-if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
+# add OR VLLM_GPU_LANG STREQUAL "HIP" here once
+# https://github.com/vllm-project/vllm/issues/35163 is resolved
+if(VLLM_GPU_LANG STREQUAL "CUDA")
   #
   # _C_stable_libtorch extension (ops registered via STABLE_TORCH_LIBRARY)
   #
   set(VLLM_STABLE_EXT_SRC
     "csrc/libtorch_stable/torch_bindings.cpp"
-    "csrc/libtorch_stable/activation_kernels.cu"
-    "csrc/libtorch_stable/quantization/w8a8/int8/scaled_quant.cu"
-    "csrc/libtorch_stable/quantization/w8a8/fp8/common.cu"
-    "csrc/libtorch_stable/quantization/gptq/q_gemm.cu"
-    "csrc/libtorch_stable/quantization/gguf/gguf_kernel.cu"
-    "csrc/libtorch_stable/pos_encoding_kernels.cu"
-    "csrc/libtorch_stable/fused_qknorm_rope_kernel.cu"
-    "csrc/libtorch_stable/layernorm_kernels.cu"
-    "csrc/libtorch_stable/layernorm_quant_kernels.cu"
-    "csrc/libtorch_stable/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu")
+    "csrc/cutlass_extensions/common.cpp"
+    "csrc/cuda_utils_kernels.cu"
+    "csrc/libtorch_stable/quantization/w8a8/cutlass/scaled_mm_entry.cu"
+    "csrc/libtorch_stable/quantization/fp4/nvfp4_quant_entry.cu"
+    "csrc/libtorch_stable/quantization/fp4/nvfp4_scaled_mm_entry.cu")
 
   if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND VLLM_STABLE_EXT_SRC
-      "csrc/cuda_utils_kernels.cu"
-      "csrc/cutlass_extensions/common.cpp"
-      "csrc/libtorch_stable/quantization/w8a8/cutlass/scaled_mm_entry.cu"
-      "csrc/libtorch_stable/quantization/fp4/nvfp4_quant_entry.cu"
-      "csrc/libtorch_stable/quantization/fp4/nvfp4_scaled_mm_entry.cu"
       "csrc/libtorch_stable/permute_cols.cu"
       "csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu"
-      "csrc/libtorch_stable/quantization/w8a8/int8/per_token_group_quant.cu"
-      "csrc/libtorch_stable/quantization/awq/gemm_kernels.cu")
+      "csrc/libtorch_stable/quantization/w8a8/int8/per_token_group_quant.cu")
+  endif()
 
+  if(VLLM_GPU_LANG STREQUAL "CUDA")
     set_gencode_flags_for_srcs(
       SRCS "${VLLM_STABLE_EXT_SRC}"
       CUDA_ARCHS "${CUDA_ARCHS}")
-
-  # DeepSeek V3 fused A GEMM kernel (requires SM 9.0+, Hopper and later)
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0f;11.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
-  endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND DSV3_FUSED_A_GEMM_ARCHS)
-    set(SRCS "csrc/libtorch_stable/dsv3_fused_a_gemm.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${DSV3_FUSED_A_GEMM_ARCHS}")
-    list(APPEND VLLM_STABLE_EXT_SRC "${SRCS}")
-    message(STATUS "Building dsv3_fused_a_gemm for archs: ${DSV3_FUSED_A_GEMM_ARCHS}")
-  else()
-    message(STATUS "Not building dsv3_fused_a_gemm as no compatible archs found "
-                   "in CUDA target architectures.")
-  endif()
-
-  # Only build AllSpark kernels if we are building for at least some compatible archs.
-  cuda_archs_loose_intersection(ALLSPARK_ARCHS "8.0;8.6;8.7;8.9" "${CUDA_ARCHS}")
-  if (ALLSPARK_ARCHS)
-    set(SRCS
-       "csrc/libtorch_stable/quantization/gptq_allspark/allspark_repack.cu"
-       "csrc/libtorch_stable/quantization/gptq_allspark/allspark_qgemm_w8a16.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${ALLSPARK_ARCHS}")
-    list(APPEND VLLM_STABLE_EXT_SRC "${SRCS}")
-    message(STATUS "Building AllSpark kernels for archs: ${ALLSPARK_ARCHS}")
-  else()
-    message(STATUS "Not building AllSpark kernels as no compatible archs found"
-                   " in CUDA target architectures")
   endif()
 
   #
@@ -939,9 +952,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
       "csrc/libtorch_stable/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
       "csrc/libtorch_stable/quantization/fp4/nvfp4_experts_quant.cu"
       "csrc/libtorch_stable/quantization/fp4/nvfp4_scaled_mm_kernels.cu"
-      "csrc/libtorch_stable/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
-      "csrc/libtorch_stable/quantization/fp4/mxfp4_experts_quant.cu"
-      "csrc/libtorch_stable/quantization/fp4/mxfp4_blockwise_moe_kernel.cu")
+      "csrc/libtorch_stable/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
@@ -994,44 +1005,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
     endif()
   endif()
 
-  # CUTLASS MLA Archs and flags
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(MLA_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(MLA_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
-  endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS)
-    set(SRCS
-      "csrc/libtorch_stable/attention/mla/sm100_cutlass_mla_kernel.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${MLA_ARCHS}")
-    list(APPEND VLLM_STABLE_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MLA=1")
-    # Add MLA-specific include directories only to MLA source files
-    set_source_files_properties(${SRCS}
-      PROPERTIES INCLUDE_DIRECTORIES "${CUTLASS_DIR}/examples/77_blackwell_fmha;${CUTLASS_DIR}/examples/common")
-    message(STATUS "Building CUTLASS MLA for archs: ${MLA_ARCHS}")
-  else()
-    message(STATUS "Not building CUTLASS MLA as no compatible archs were found.")
-    # clear MLA_ARCHS
-    set(MLA_ARCHS)
-  endif()
-
-  # Hadacore kernels
-  cuda_archs_loose_intersection(HADACORE_ARCHS "8.0+PTX;9.0+PTX" "${CUDA_ARCHS}")
-  if(HADACORE_ARCHS)
-    set(SRCS "csrc/libtorch_stable/quantization/hadamard/hadacore/hadamard_transform_cuda.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${HADACORE_ARCHS}")
-    list(APPEND VLLM_STABLE_EXT_SRC "${SRCS}")
-    message(STATUS "Building hadacore")
-  endif()
-
-  # if CUDA endif
-  endif()
-
   message(STATUS "Enabling C_stable extension.")
   define_extension_target(
     _C_stable_libtorch
@@ -1051,34 +1024,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   target_compile_definitions(_C_stable_libtorch PRIVATE
     TORCH_TARGET_VERSION=0x020A000000000000ULL)
 
-  # Needed to use cuda/hip APIs from C-shim
-  if(VLLM_GPU_LANG STREQUAL "CUDA")
-    target_compile_definitions(_C_stable_libtorch PRIVATE USE_CUDA)
-    # Needed by CUTLASS kernels
-    target_compile_definitions(_C_stable_libtorch PRIVATE
-      CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
-  elseif(VLLM_GPU_LANG STREQUAL "HIP")
-    target_compile_definitions(_C_stable_libtorch PRIVATE USE_ROCM)
-  endif()
+  # Needed to use cuda APIs from C-shim
+  target_compile_definitions(_C_stable_libtorch PRIVATE
+    USE_CUDA)
 
-  # On ROCm, _C_stable_libtorch calls raw HIP APIs (e.g. hipGetDevice in
-  # get_device_prop()) which must resolve to the same libamdhip64.so that
-  # PyTorch uses.  When PyTorch bundles its own copy (pip/conda wheels),
-  # the raw HIP calls would otherwise resolve to the system ROCm copy,
-  # initializing a second HIP runtime that corrupts device state (wrong
-  # device on DeviceGuard, core dumps on multi-GPU tests).
-  #
-  # If PyTorch doesn't bundle libamdhip64 (built from source against system
-  # ROCm), there is only one copy in the process and no action is needed —
-  # the HIP compiler already links the system libamdhip64 automatically.
-  if(VLLM_GPU_LANG STREQUAL "HIP")
-    find_library(_STABLE_TORCH_AMDHIP64 amdhip64
-      PATHS "${TORCH_INSTALL_PREFIX}/lib" NO_DEFAULT_PATH)
-    if(_STABLE_TORCH_AMDHIP64)
-      message(STATUS "Found PyTorch-bundled libamdhip64 at ${_STABLE_TORCH_AMDHIP64}")
-      target_link_libraries(_C_stable_libtorch PRIVATE ${_STABLE_TORCH_AMDHIP64})
-    endif()
-  endif()
+  # Needed by CUTLASS kernels
+  target_compile_definitions(_C_stable_libtorch PRIVATE
+    CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 endif()
 
 #
@@ -1088,13 +1040,13 @@ endif()
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/topk_softmax_kernels.cu"
-  "csrc/moe/topk_softplus_sqrt_kernels.cu")
+  "csrc/moe/topk_softmax_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC
     "csrc/moe/moe_wna16.cu"
-    "csrc/moe/grouped_topk_kernels.cu")
+    "csrc/moe/grouped_topk_kernels.cu"
+    "csrc/moe/router_gemm.cu")
 endif()
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
@@ -1279,12 +1231,6 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
     WITH_SOABI)
 endif()
 
-# Must run after the last HIP `define_extension_target` so every extension
-# has registered its sources.
-if (VLLM_GPU_LANG STREQUAL "HIP")
-  vllm_finalize_hipify_target()
-endif()
-
 # For CUDA and HIP builds also build the triton_kernels external package.
 if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
     include(cmake/external_projects/triton_kernels.cmake)
@@ -1294,6 +1240,7 @@ endif()
 if (VLLM_GPU_LANG STREQUAL "CUDA")
     include(cmake/external_projects/deepgemm.cmake)
     include(cmake/external_projects/flashmla.cmake)
+    include(cmake/external_projects/cutlass_fa3.cmake)
     include(cmake/external_projects/qutlass.cmake)
 
     # vllm-flash-attn should be last as it overwrites some CMake functions

README.md

@@ -14,7 +14,7 @@ Easy, fast, and cheap LLM serving for everyone
 | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
-🔥 We have built a vLLM website to help you get started with vLLM. Please visit [vllm.ai](https://vllm.ai) to learn more.
+🔥 We have built a vllm website to help you get started with vllm. Please visit [vllm.ai](https://vllm.ai) to learn more.
 For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.
 
 ---
@@ -50,7 +50,7 @@ vLLM is flexible and easy to use with:
 - Efficient multi-LoRA support for dense and MoE layers
 - Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
 
-vLLM seamlessly supports 200+ model architectures on Hugging Face, including:
+vLLM seamlessly supports 200+ model architectures on HuggingFace, including:
 
 - Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
 - Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)

benchmarks/attention_benchmarks/configs/mla_decode.yaml

@@ -53,7 +53,6 @@ backends:
   - FLASHINFER_MLA
   - FLASH_ATTN_MLA  # Hopper only
   - FLASHMLA        # Hopper only
-  - TOKENSPEED_MLA  # Blackwell + R1 dims + FP8 KV (use --kv-cache-dtype fp8)
 
 device: "cuda:0"
 repeats: 100

benchmarks/attention_benchmarks/configs/mla_prefill.yaml

@@ -3,7 +3,6 @@
 # Compares all available MLA prefill backends:
 #   FA backends:  fa2, fa3, fa4 (FlashAttention versions)
 #   Non-FA:       flashinfer, cudnn, trtllm (Blackwell-only, require flashinfer)
-#   CuTe DSL:     tokenspeed (Blackwell + R1 dims, requires tokenspeed_mla)
 #
 # Uses cutlass_mla as the decode backend for impl construction
 # (only the prefill path is exercised).
@@ -121,7 +120,6 @@ prefill_backends:
   - flashinfer
   - cudnn
   - trtllm
-  - tokenspeed
 
 device: "cuda:0"
 repeats: 20

benchmarks/attention_benchmarks/mla_runner.py

@@ -29,7 +29,6 @@ from vllm.config import (
     VllmConfig,
     set_current_vllm_config,
 )
-from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum
 
 # ============================================================================
 # VllmConfig Creation
@@ -80,8 +79,8 @@ def create_minimal_vllm_config(
         index_topk: Optional topk value for sparse MLA backends. If provided,
                     the config will include index_topk for sparse attention.
         prefill_backend: Prefill backend name (e.g., "fa3", "fa4", "flashinfer",
-                        "trtllm"). Configures the attention config to force
-                        the specified prefill backend.
+                        "cudnn", "trtllm"). Configures the attention config to
+                        force the specified prefill backend.
 
     Returns:
         VllmConfig for benchmarking
@@ -180,13 +179,19 @@ def create_minimal_vllm_config(
 
     if prefill_backend is not None:
         prefill_cfg = get_prefill_backend_config(prefill_backend)
-        vllm_config.attention_config.mla_prefill_backend = prefill_cfg[
-            "mla_prefill_backend"
-        ]
         if prefill_cfg["flash_attn_version"] is not None:
             vllm_config.attention_config.flash_attn_version = prefill_cfg[
                 "flash_attn_version"
             ]
+        vllm_config.attention_config.disable_flashinfer_prefill = prefill_cfg[
+            "disable_flashinfer_prefill"
+        ]
+        vllm_config.attention_config.use_cudnn_prefill = prefill_cfg[
+            "use_cudnn_prefill"
+        ]
+        vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill = prefill_cfg[
+            "use_trtllm_ragged_deepseek_prefill"
+        ]
 
     return vllm_config
 
@@ -201,27 +206,39 @@ def create_minimal_vllm_config(
 _PREFILL_BACKEND_CONFIG: dict[str, dict] = {
     "fa2": {
         "flash_attn_version": 2,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
     },
     "fa3": {
         "flash_attn_version": 3,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
     },
     "fa4": {
         "flash_attn_version": 4,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
     },
     "flashinfer": {
         "flash_attn_version": None,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASHINFER,
+        "disable_flashinfer_prefill": False,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
+    },
+    "cudnn": {
+        "flash_attn_version": None,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": True,
+        "use_trtllm_ragged_deepseek_prefill": False,
     },
     "trtllm": {
         "flash_attn_version": None,
-        "mla_prefill_backend": MLAPrefillBackendEnum.TRTLLM_RAGGED,
-    },
-    "tokenspeed": {
-        "flash_attn_version": None,
-        "mla_prefill_backend": MLAPrefillBackendEnum.TOKENSPEED_MLA,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": True,
     },
 }
 
@@ -387,7 +404,6 @@ def _build_attention_metadata(
         query_start_loc=q_start_gpu,
         query_start_loc_cpu=q_start_cpu,
         seq_lens=seq_lens_gpu,
-        seq_lens_cpu_upper_bound=seq_lens_cpu,
         _seq_lens_cpu=seq_lens_cpu,
         _num_computed_tokens_cpu=num_computed_tokens_cpu,
         slot_mapping=slot_mapping,
@@ -608,21 +624,6 @@ def _create_backend_impl(
     # Create mock layer
     layer = MockLayer(device, impl=impl, kv_cache_spec=kv_cache_spec)
 
-    # Attach a prefill backend (MLAAttention does this in __init__; the metadata
-    # builder reads layer.prefill_backend from static_forward_context).
-    from vllm.v1.attention.backends.mla.prefill import get_mla_prefill_backend
-
-    prefill_backend_cls = get_mla_prefill_backend(vllm_config)
-    layer.prefill_backend = prefill_backend_cls(
-        num_heads=mla_dims["num_q_heads"],
-        scale=(mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"]) ** -0.5,
-        kv_lora_rank=mla_dims["kv_lora_rank"],
-        qk_nope_head_dim=mla_dims["qk_nope_head_dim"],
-        qk_rope_head_dim=mla_dims["qk_rope_head_dim"],
-        v_head_dim=mla_dims["v_head_dim"],
-        vllm_config=vllm_config,
-    )
-
     # Create builder instance if needed
     builder_instance = None
     if builder_class:
@@ -959,6 +960,19 @@ def _run_mla_benchmark_batched(
     results = []
 
     with set_current_vllm_config(vllm_config):
+        # Clear cached prefill backend detection functions so they re-evaluate
+        # with the current VllmConfig. These are @functools.cache decorated and
+        # would otherwise return stale results from a previous backend's config.
+        from vllm.model_executor.layers.attention.mla_attention import (
+            use_cudnn_prefill,
+            use_flashinfer_prefill,
+            use_trtllm_ragged_deepseek_prefill,
+        )
+
+        use_flashinfer_prefill.cache_clear()
+        use_cudnn_prefill.cache_clear()
+        use_trtllm_ragged_deepseek_prefill.cache_clear()
+
         # Create backend impl, layer, builder, and indexer (reused across benchmarks)
         impl, layer, builder_instance, indexer = _create_backend_impl(
             backend_cfg,
@@ -970,36 +984,38 @@ def _run_mla_benchmark_batched(
             kv_cache_dtype=kv_cache_dtype,
         )
 
-        # Verify the actual prefill backend matches what was requested. The
-        # selector + impl construction already raise on misuse; here we just
-        # check the resolved class against the requested name as a sanity guard.
+        # Verify the actual prefill backend matches what was requested
         if prefill_backend is not None:
-            expected_class = {
-                "fa2": "FlashAttnPrefillBackend",
-                "fa3": "FlashAttnPrefillBackend",
-                "fa4": "FlashAttnPrefillBackend",
-                "flashinfer": "FlashInferPrefillBackend",
-                "trtllm": "TrtllmRaggedPrefillBackend",
-                "tokenspeed": "TokenspeedMLAPrefillBackend",
-            }.get(prefill_backend)
-            actual_class = type(getattr(layer, "prefill_backend", None)).__name__
-            if expected_class and actual_class != expected_class:
-                raise RuntimeError(
-                    f"Prefill backend '{prefill_backend}' requested "
-                    f"{expected_class}, got {actual_class}. Check "
-                    f"attention_config plumbing or installed deps."
-                )
-            if prefill_backend in {"fa2", "fa3", "fa4"}:
-                fa_version = int(prefill_backend[2:])
-                actual_fa_version = getattr(
-                    layer.prefill_backend, "vllm_flash_attn_version", None
-                )
+            prefill_cfg = get_prefill_backend_config(prefill_backend)
+            fa_version = prefill_cfg["flash_attn_version"]
+
+            if fa_version is not None:
+                # FA backend: verify the impl's FA version
+                actual_fa_version = getattr(impl, "vllm_flash_attn_version", None)
                 if actual_fa_version != fa_version:
                     raise RuntimeError(
                         f"Prefill backend '{prefill_backend}' requested FA "
-                        f"version {fa_version}, got "
-                        f"{actual_fa_version} on {actual_class}."
+                        f"version {fa_version}, but the impl is using FA "
+                        f"version {actual_fa_version}. Check "
+                        f"vllm/v1/attention/backends/fa_utils.py."
                     )
+            else:
+                # Non-FA backend: verify the builder picked the right path
+                expected_flags = {
+                    "flashinfer": "_use_fi_prefill",
+                    "cudnn": "_use_cudnn_prefill",
+                    "trtllm": "_use_trtllm_ragged_prefill",
+                }
+                flag_name = expected_flags.get(prefill_backend)
+                if flag_name and not getattr(builder_instance, flag_name, False):
+                    raise RuntimeError(
+                        f"Prefill backend '{prefill_backend}' was requested "
+                        f"but the metadata builder did not enable it. This "
+                        f"usually means a dependency is missing (e.g., "
+                        f"flashinfer not installed) or the platform doesn't "
+                        f"support it."
+                    )
+
         # Run each benchmark with the shared impl
         for config, threshold, num_splits in configs_with_params:
             # Set threshold for this benchmark (FlashAttn/FlashMLA only)

benchmarks/benchmark_hidden_state_extraction.py

@@ -1,415 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Benchmark hidden state extraction throughput.
-
-Measures two modes:
-  1. Baseline: bulk inference with max_tokens=1, no extraction.
-  2. Extract:  async hidden state extraction via ExampleHiddenStatesConnector
-               with N concurrent clients, each consuming hidden states as
-               soon as their request finishes (overlapping I/O with generation).
-
-Reports tokens/s and prompts/s for each mode.
-
-Usage:
-  python benchmarks/benchmark_hidden_state_extraction.py \
-      --model Qwen/Qwen3-0.6B \
-      --num-prompts 64 \
-      --num-clients 8 \
-      --prompt-len 8192 \
-      --layers 1 2 3 4
-"""
-
-import argparse
-import asyncio
-import time
-from concurrent.futures import ThreadPoolExecutor
-
-import torch
-from transformers import AutoConfig
-
-from vllm import LLM, SamplingParams
-from vllm.config.kv_transfer import KVTransferConfig
-from vllm.distributed.kv_transfer.kv_connector.v1 import (
-    example_hidden_states_connector,
-)
-from vllm.engine.arg_utils import AsyncEngineArgs
-from vllm.sampling_params import RequestOutputKind
-from vllm.v1.engine.async_llm import AsyncLLM
-
-
-def _make_profiler_config(profile_dir: str) -> dict:
-    """Build a profiler_config dict for torch profiling."""
-    return {
-        "profiler": "torch",
-        "torch_profiler_dir": profile_dir,
-        "torch_profiler_with_stack": True,
-    }
-
-
-def make_random_prompts(
-    num_prompts: int, prompt_len: int, vocab_size: int, seed: int = 42
-) -> list[list[int]]:
-    """Generate lists of random token IDs."""
-    # Set seed for reproducibility
-    torch.manual_seed(seed)
-    return [
-        torch.randint(0, vocab_size, (prompt_len,)).tolist() for _ in range(num_prompts)
-    ]
-
-
-def consume_hidden_states(path: str) -> float:
-    """Load hidden states from disk and compute per-position mean.
-
-    Returns a single float: the grand mean of all hidden state values.
-    This forces the benchmark to actually read and reduce the data.
-
-    Uses :func:`load_hidden_states` which acquires a shared flock,
-    blocking (without polling) until the async writer releases its
-    exclusive lock.
-    """
-    obj = example_hidden_states_connector.load_hidden_states(path)
-    hs = obj["hidden_states"]
-    total = hs.mean().item()
-
-    example_hidden_states_connector.cleanup_hidden_states(path)
-
-    return total
-
-
-def run_baseline(
-    model: str,
-    prompts: list[list[int]],
-    extra_args: dict,
-    profile_dir: str | None = None,
-) -> dict:
-    """Baseline: bulk inference, no hidden state extraction."""
-    if profile_dir:
-        extra_args = {
-            **extra_args,
-            "profiler_config": _make_profiler_config(profile_dir),
-        }
-    llm = LLM(
-        model=model,
-        enable_prefix_caching=False,
-        enable_chunked_prefill=False,
-        **extra_args,
-    )
-    sampling_params = SamplingParams(max_tokens=1)
-    prompt_inputs = [{"prompt_token_ids": p} for p in prompts]
-
-    # Warmup
-    llm.generate(prompt_inputs[:4], sampling_params, use_tqdm=False)
-
-    if profile_dir:
-        llm.start_profile()
-
-    t0 = time.perf_counter()
-    outputs = llm.generate(prompt_inputs, sampling_params, use_tqdm=True)
-    elapsed = time.perf_counter() - t0
-
-    if profile_dir:
-        llm.stop_profile()
-
-    total_prompt_tokens = sum(len(o.prompt_token_ids) for o in outputs)
-    num_prompts = len(outputs)
-
-    del llm
-    torch.accelerator.empty_cache()
-
-    return {
-        "mode": "baseline",
-        "elapsed_s": elapsed,
-        "num_prompts": num_prompts,
-        "total_prompt_tokens": total_prompt_tokens,
-        "tokens_per_s": total_prompt_tokens / elapsed,
-        "prompts_per_s": num_prompts / elapsed,
-    }
-
-
-# ---- Async extraction benchmark ----
-
-
-async def _client_loop(
-    engine: AsyncLLM,
-    prompt_queue: asyncio.Queue,
-    consume_pool: ThreadPoolExecutor,
-    results: list[dict],
-    client_id: int,
-):
-    """A single async client: pulls prompts, submits to engine, consumes
-    hidden states as soon as each request finishes."""
-    loop = asyncio.get_event_loop()
-    while True:
-        item = await prompt_queue.get()
-        if item is None:
-            prompt_queue.task_done()
-            break
-        idx, token_ids = item
-
-        request_id = f"req-{idx}"
-        sampling_params = SamplingParams(
-            max_tokens=1,
-            output_kind=RequestOutputKind.FINAL_ONLY,
-        )
-
-        final_output = None
-        async for output in engine.generate(
-            request_id=request_id,
-            prompt={"prompt_token_ids": token_ids},
-            sampling_params=sampling_params,
-        ):
-            if output.finished:
-                final_output = output
-
-        # Consume hidden states on a thread (disk I/O)
-        path = final_output.kv_transfer_params["hidden_states_path"]
-        mean_val = await loop.run_in_executor(consume_pool, consume_hidden_states, path)
-        num_tokens = len(final_output.prompt_token_ids)
-
-        results.append(
-            {
-                "request_id": request_id,
-                "num_prompt_tokens": num_tokens,
-                "mean_hidden_value": mean_val,
-            }
-        )
-        prompt_queue.task_done()
-
-
-async def _run_extraction_async(
-    model: str,
-    prompts: list[list[int]],
-    num_clients: int,
-    layers: list[int],
-    tmpdir: str,
-    extra_args: dict,
-    profile_dir: str | None = None,
-) -> dict:
-    if profile_dir:
-        extra_args = {
-            **extra_args,
-            "profiler_config": _make_profiler_config(profile_dir),
-        }
-    engine_args = AsyncEngineArgs(
-        model=model,
-        enable_prefix_caching=False,
-        enable_chunked_prefill=False,
-        max_num_batched_tokens=40960,
-        max_model_len=40960,
-        speculative_config={
-            "method": "extract_hidden_states",
-            "num_speculative_tokens": 1,
-            "draft_model_config": {
-                "hf_config": {
-                    "eagle_aux_hidden_state_layer_ids": layers,
-                },
-            },
-        },
-        kv_transfer_config=KVTransferConfig(
-            kv_connector="ExampleHiddenStatesConnector",
-            kv_role="kv_producer",
-            kv_connector_extra_config={
-                "shared_storage_path": tmpdir,
-            },
-        ),
-        **extra_args,
-    )
-    engine = AsyncLLM.from_engine_args(engine_args)
-
-    try:
-        # Warmup: run a few prompts sequentially, cleaning up generated files
-        for i in range(min(4, len(prompts))):
-            sp = SamplingParams(max_tokens=1, output_kind=RequestOutputKind.FINAL_ONLY)
-            final_output = None
-            async for output in engine.generate(
-                request_id=f"warmup-{i}",
-                prompt={"prompt_token_ids": prompts[i]},
-                sampling_params=sp,
-            ):
-                if output.finished:
-                    final_output = output
-            if final_output and final_output.kv_transfer_params:
-                path = final_output.kv_transfer_params.get("hidden_states_path")
-                if path:
-                    example_hidden_states_connector.cleanup_hidden_states(path)
-
-        if profile_dir:
-            await engine.start_profile()
-
-        # Fill prompt queue
-        prompt_queue: asyncio.Queue = asyncio.Queue()
-        for idx, token_ids in enumerate(prompts):
-            prompt_queue.put_nowait((idx, token_ids))
-        # Sentinel per client
-        for _ in range(num_clients):
-            prompt_queue.put_nowait(None)
-
-        results: list[dict] = []
-        consume_pool = ThreadPoolExecutor(max_workers=num_clients)
-
-        t0 = time.perf_counter()
-        tasks = [
-            asyncio.create_task(
-                _client_loop(engine, prompt_queue, consume_pool, results, i)
-            )
-            for i in range(num_clients)
-        ]
-        await asyncio.gather(*tasks)
-        elapsed = time.perf_counter() - t0
-
-        consume_pool.shutdown(wait=True)
-
-        if profile_dir:
-            await engine.stop_profile()
-
-        total_prompt_tokens = sum(r["num_prompt_tokens"] for r in results)
-        num_prompts = len(results)
-        mean_hidden = sum(r["mean_hidden_value"] for r in results) / max(
-            len(results), 1
-        )
-
-        return {
-            "mode": "extract",
-            "elapsed_s": elapsed,
-            "num_prompts": num_prompts,
-            "total_prompt_tokens": total_prompt_tokens,
-            "tokens_per_s": total_prompt_tokens / elapsed,
-            "prompts_per_s": num_prompts / elapsed,
-            "mean_hidden_value": mean_hidden,
-        }
-    finally:
-        engine.shutdown()
-
-
-def run_extraction(
-    model: str,
-    prompts: list[list[int]],
-    num_clients: int,
-    layers: list[int],
-    extra_args: dict,
-    profile_dir: str | None = None,
-) -> dict:
-    return asyncio.run(
-        _run_extraction_async(
-            model,
-            prompts,
-            num_clients,
-            layers,
-            "/dev/shm",
-            extra_args,
-            profile_dir=profile_dir,
-        )
-    )
-
-
-def print_results(results: dict):
-    mode = results["mode"]
-    print(f"\n{'=' * 60}")
-    print(f"  {mode.upper()} RESULTS")
-    print(f"{'=' * 60}")
-    print(f"  Prompts:             {results['num_prompts']}")
-    print(f"  Total prompt tokens: {results['total_prompt_tokens']:,}")
-    print(f"  Wall time:           {results['elapsed_s']:.2f}s")
-    print(f"  Tokens/s:            {results['tokens_per_s']:,.0f}")
-    print(f"  Prompts/s:           {results['prompts_per_s']:.2f}")
-    if mode == "extract":
-        print(f"  Mean hidden value:   {results['mean_hidden_value']:.6f}")
-    print(f"{'=' * 60}\n")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Benchmark hidden state extraction throughput"
-    )
-    parser.add_argument("--model", type=str, required=True)
-    parser.add_argument("--num-prompts", type=int, default=64)
-    parser.add_argument("--num-clients", type=int, default=8)
-    parser.add_argument("--prompt-len", type=int, default=8192)
-    parser.add_argument("--layers", type=int, nargs="+", default=[1, 2, 3, 4])
-    parser.add_argument("--skip-baseline", action="store_true")
-    parser.add_argument("--skip-extract", action="store_true")
-    parser.add_argument("--gpu-memory-utilization", type=float, default=0.9)
-    parser.add_argument("--max-num-batched-tokens", type=int, default=None)
-    parser.add_argument("--max-cudagraph-capture-size", type=int, default=None)
-    parser.add_argument("--max-model-len", type=int, default=None)
-    parser.add_argument("--enforce-eager", action="store_true")
-    parser.add_argument("--load-format", type=str, default=None)
-    parser.add_argument(
-        "--profile",
-        action="store_true",
-        help="Enable torch profiler for both baseline and extraction runs.",
-    )
-    parser.add_argument(
-        "--torch-profiler-dir",
-        type=str,
-        default="./vllm_profile",
-        help="Directory to save torch profiler traces (default: ./vllm_profile).",
-    )
-    parser.add_argument(
-        "--enable-flashinfer-autotune",
-        action="store_true",
-        default=False,
-        help="Enable FlashInfer autotuning (can be slow).",
-    )
-    args = parser.parse_args()
-
-    extra_args = {
-        "gpu_memory_utilization": args.gpu_memory_utilization,
-    }
-    if args.max_model_len is not None:
-        extra_args["max_model_len"] = args.max_model_len
-    if args.max_num_batched_tokens is not None:
-        extra_args["max_num_batched_tokens"] = args.max_num_batched_tokens
-        if args.max_model_len and args.max_num_batched_tokens < args.max_model_len:
-            raise ValueError(
-                "max_num_batched_tokens must be >= max_model_len since chunked prefill"
-                " is not supported by hidden state extraction."
-            )
-    if args.enforce_eager:
-        extra_args["enforce_eager"] = True
-    if args.load_format is not None:
-        extra_args["load_format"] = args.load_format
-    if args.max_cudagraph_capture_size is not None:
-        extra_args["max_cudagraph_capture_size"] = args.max_cudagraph_capture_size
-    extra_args["enable_flashinfer_autotune"] = args.enable_flashinfer_autotune
-
-    # Get vocab size from HF config without loading the full model
-    hf_config = AutoConfig.from_pretrained(args.model, trust_remote_code=True)
-    vocab_size = hf_config.vocab_size
-    prompts = make_random_prompts(args.num_prompts, args.prompt_len, vocab_size)
-    print(
-        f"Generated {args.num_prompts} prompts, "
-        f"{args.prompt_len} tokens each (vocab {vocab_size})"
-    )
-
-    profile_dir = args.torch_profiler_dir if args.profile else None
-    if profile_dir:
-        print(f"Torch profiler enabled, traces will be saved to {profile_dir}/")
-
-    if not args.skip_baseline:
-        baseline_profile_dir = f"{profile_dir}/baseline" if profile_dir else None
-        baseline = run_baseline(
-            args.model, prompts, extra_args, profile_dir=baseline_profile_dir
-        )
-        print_results(baseline)
-
-    if not args.skip_extract:
-        extract_profile_dir = f"{profile_dir}/extract" if profile_dir else None
-        extract = run_extraction(
-            args.model,
-            prompts,
-            args.num_clients,
-            args.layers,
-            extra_args,
-            profile_dir=extract_profile_dir,
-        )
-        print_results(extract)
-
-    if not args.skip_baseline and not args.skip_extract:
-        slowdown = baseline["tokens_per_s"] / extract["tokens_per_s"]
-        print("Extraction slowdown factor: {:.2f}x".format(slowdown))
-
-
-if __name__ == "__main__":
-    main()

benchmarks/benchmark_serving_structured_output.py

@@ -115,39 +115,6 @@ class SampleRequest:
 def sample_requests(
     tokenizer: PreTrainedTokenizerBase, args: argparse.Namespace
 ) -> list[SampleRequest]:
-    def _apply_random_prefix(
-        tokenizer: PreTrainedTokenizerBase,
-        requests: list[SampleRequest],
-        prefix_len: int,
-        seed: int,
-    ) -> list[SampleRequest]:
-        if prefix_len <= 0:
-            return requests
-        rng = np.random.default_rng(seed)
-        vocab_size = tokenizer.vocab_size
-        prohibited = getattr(tokenizer, "all_special_ids", None) or []
-        allowed = np.array([i for i in range(vocab_size) if i not in prohibited])
-        if len(allowed) == 0:
-            return requests
-        prefix_ids = rng.integers(0, len(allowed), size=prefix_len)
-        prefix_token_ids = allowed[prefix_ids].tolist()
-        out = []
-        for req in requests:
-            prompt_ids = tokenizer(req.prompt, add_special_tokens=False).input_ids
-            full_ids = prefix_token_ids + prompt_ids
-            full_prompt = tokenizer.decode(full_ids, skip_special_tokens=False)
-            out.append(
-                SampleRequest(
-                    prompt=full_prompt,
-                    prompt_len=len(tokenizer(full_prompt).input_ids),
-                    expected_output_len=req.expected_output_len,
-                    schema=req.schema,
-                    structure_type=req.structure_type,
-                    completion=req.completion,
-                )
-            )
-        return out
-
     if args.dataset == "json" or args.dataset == "json-unique":
         if args.json_schema_path is None:
             dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -294,9 +261,6 @@ def sample_requests(
                 )
             )
 
-    requests = _apply_random_prefix(
-        tokenizer, requests, args.random_prefix_len, args.seed
-    )
     return requests
 
 
@@ -981,15 +945,6 @@ def create_argument_parser():
         "results in a more uniform arrival of requests.",
     )
     parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument(
-        "--random-prefix-len",
-        type=int,
-        default=0,
-        help=(
-            "Number of prefix tokens to prepend to every prompt. "
-            "The same prefix is used for all prompts to enable prefix caching."
-        ),
-    )
     parser.add_argument(
         "--trust-remote-code",
         action="store_true",

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -16,7 +16,7 @@ from vllm.model_executor.layers.fused_moe.all2all_utils import (
     maybe_make_prepare_finalize,
 )
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser

benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py

@@ -22,7 +22,7 @@ from vllm.model_executor.layers.fused_moe.config import (
     fp8_w8a8_moe_quant_config,
     nvfp4_moe_quant_config,
 )
-from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
+from vllm.model_executor.layers.fused_moe.cutlass_moe import (
     CutlassExpertsFp4,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk

benchmarks/kernels/benchmark_fused_moe_lora_one_shot.py

@@ -1,465 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Benchmark the fused MoE-LoRA fast path (one-shot) vs two-kernel baseline.
-
-The "one_shot" provider goes through `vllm.lora.ops.triton_ops.fused_moe_lora`
-which dispatches to the single-kernel one-shot implementation when
-fully_sharded=False (the prefill default).
-
-The "two_kernel" provider drives `fused_moe_lora_shrink` + `fused_moe_lora_expand`
-directly, bypassing the dispatch and matching the legacy two-kernel path's
-work distribution. This isolates the win from kernel fusion.
-
-Run:
-    .venv/bin/python -m benchmarks.kernels.benchmark_fused_moe_lora_one_shot
-    .venv/bin/python -m benchmarks.kernels.benchmark_fused_moe_lora_one_shot \\
-        --model qwen3moe
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import random
-
-import torch
-
-from vllm import _custom_ops as ops
-from vllm.lora.ops.triton_ops import (
-    fused_moe_lora,
-    fused_moe_lora_expand,
-    fused_moe_lora_shrink,
-)
-from vllm.triton_utils import triton
-
-DTYPE = torch.bfloat16
-DEVICE = "cuda"
-
-
-# ----- input fabrication -----------------------------------------------------
-
-
-def _round_up(x: int, base: int) -> int:
-    return ((x + base - 1) // base) * base
-
-
-def _ceildiv(x: int, y: int) -> int:
-    return (x + y - 1) // y
-
-
-def _assign_loras(num_tokens: int, num_sequences: int, max_loras: int) -> torch.Tensor:
-    tokens_per_seq = num_tokens // num_sequences
-    rem = num_tokens % num_sequences
-    out = torch.empty(num_tokens, dtype=torch.int32)
-    start = 0
-    for i in range(num_sequences):
-        end = start + tokens_per_seq + (1 if i < rem else 0)
-        out[start:end] = random.randint(0, max_loras - 1)
-        start = end
-    return out
-
-
-def _assign_experts(num_tokens: int, num_experts: int, top_k: int):
-    expert_indices = torch.empty((num_tokens, top_k), dtype=torch.int32)
-    for i in range(num_tokens):
-        expert_indices[i] = torch.randperm(num_experts)[:top_k]
-    weights = torch.rand((num_tokens, top_k), dtype=torch.float32)
-    weights = weights / weights.sum(dim=1, keepdim=True)
-    return expert_indices, weights
-
-
-def _make_inputs(
-    M: int,
-    K: int,
-    N_per_slice: int,
-    rank: int,
-    num_experts: int,
-    top_k: int,
-    max_loras: int,
-    num_slices: int,
-    block_size_m: int,
-):
-    """Mirrors the production caller's tensor layout."""
-    torch.manual_seed(0)
-    random.seed(0)
-
-    num_sequences = max(1, min(M, 8))
-    topk_ids_cpu, topk_weights_cpu = _assign_experts(M, num_experts, top_k)
-    token_lora_cpu = _assign_loras(M, num_sequences, max_loras)
-    lora_ids_cpu = torch.full((max_loras + 1,), -1, dtype=torch.int32)
-    uniq = torch.unique(token_lora_cpu, sorted=True)
-    lora_ids_cpu[: uniq.size(0)].copy_(uniq)
-
-    topk_ids = topk_ids_cpu.to(DEVICE)
-    topk_weights = topk_weights_cpu.to(device=DEVICE, dtype=DTYPE)
-    token_lora_mapping = token_lora_cpu.to(DEVICE)
-    lora_ids = lora_ids_cpu.to(DEVICE)
-    adapter_enabled = torch.ones(max_loras + 1, dtype=torch.int32, device=DEVICE)
-
-    lora_a = [
-        torch.randn((max_loras, num_experts, rank, K), dtype=DTYPE, device=DEVICE)
-        / max(K, 1) ** 0.5
-        for _ in range(num_slices)
-    ]
-    lora_b = [
-        torch.randn(
-            (max_loras, num_experts, N_per_slice, rank),
-            dtype=DTYPE,
-            device=DEVICE,
-        )
-        / max(rank, 1) ** 0.5
-        for _ in range(num_slices)
-    ]
-    hidden = torch.randn((M, K), dtype=DTYPE, device=DEVICE)
-    out_template = torch.zeros(
-        (M, top_k, num_slices * N_per_slice), dtype=DTYPE, device=DEVICE
-    )
-
-    # Sorted-path metadata (the prefill default).
-    max_pad = topk_ids.numel() + num_experts * (block_size_m - 1)
-    max_pad = _round_up(max_pad, block_size_m)
-    max_blocks = _ceildiv(max_pad, block_size_m)
-    sorted_token_ids = torch.empty(
-        (max_loras * max_pad,), dtype=torch.int32, device=DEVICE
-    )
-    expert_ids = torch.empty(
-        (max_loras * max_blocks,), dtype=torch.int32, device=DEVICE
-    )
-    num_post = torch.empty((max_loras,), dtype=torch.int32, device=DEVICE)
-    ops.moe_lora_align_block_size(
-        topk_ids,
-        token_lora_mapping,
-        num_experts,
-        block_size_m,
-        max_loras,
-        max_pad,
-        max_blocks,
-        sorted_token_ids,
-        expert_ids,
-        num_post,
-        adapter_enabled,
-        lora_ids,
-    )
-    expert_ids = expert_ids.view(max_loras, -1).contiguous()
-    sorted_token_ids = sorted_token_ids.view(max_loras, -1).contiguous()
-    num_active = torch.tensor([max_loras + 1], dtype=torch.int32, device="cpu")
-
-    return dict(
-        hidden=hidden,
-        lora_a=lora_a,
-        lora_b=lora_b,
-        topk_weights=topk_weights,
-        sorted_token_ids=sorted_token_ids,
-        expert_ids=expert_ids,
-        num_post=num_post,
-        token_lora_mapping=token_lora_mapping,
-        lora_ids=lora_ids,
-        num_active=num_active,
-        adapter_enabled=adapter_enabled,
-        out_template=out_template,
-        # bookkeeping
-        M=M,
-        K=K,
-        N_per_slice=N_per_slice,
-        rank=rank,
-        num_experts=num_experts,
-        top_k=top_k,
-        max_loras=max_loras,
-        num_slices=num_slices,
-        block_size_m=block_size_m,
-    )
-
-
-# ----- providers -------------------------------------------------------------
-
-
-def _run_one_shot(inp: dict):
-    """Drive `fused_moe_lora` with fully_sharded=False -> one-shot fast path."""
-    out = inp["out_template"].clone()
-    fused_moe_lora(
-        out,
-        inp["hidden"],
-        inp["lora_a"],
-        inp["lora_b"],
-        inp["topk_weights"],
-        inp["sorted_token_ids"],
-        inp["expert_ids"],
-        inp["num_post"],
-        inp["token_lora_mapping"],
-        inp["rank"],
-        inp["top_k"],
-        inp["lora_ids"],
-        inp["num_active"],
-        inp["adapter_enabled"],
-        inp["block_size_m"],
-        64,
-        32,
-        8,
-        4,
-        3,
-        1,
-        inp["block_size_m"],
-        64,
-        32,
-        8,
-        4,
-        3,
-        1,
-        False,
-        False,
-        0,
-    )
-    return out
-
-
-def _run_two_kernel(inp: dict):
-    """Drive `fused_moe_lora_shrink` + `fused_moe_lora_expand` directly,
-    bypassing the dispatch. Matches the legacy two-kernel work distribution.
-    """
-    M = inp["M"]
-    top_k = inp["top_k"]
-    rank = inp["rank"]
-    num_slices = inp["num_slices"]
-    N_per_slice = inp["N_per_slice"]
-    K = inp["K"]
-    num_experts = inp["num_experts"]
-    block_m = inp["block_size_m"]
-
-    intermediate = torch.zeros((num_slices, M, top_k, rank), dtype=DTYPE, device=DEVICE)
-    out = inp["out_template"].clone()
-    EM = inp["sorted_token_ids"].shape[1]
-    num_tokens = M * top_k
-
-    fused_moe_lora_shrink(
-        intermediate,
-        inp["hidden"],
-        inp["lora_a"],
-        inp["topk_weights"],
-        inp["sorted_token_ids"],
-        inp["expert_ids"],
-        inp["num_post"],
-        inp["token_lora_mapping"],
-        top_k,
-        inp["lora_ids"],
-        inp["adapter_enabled"],
-        torch.device(DEVICE),
-        rank,
-        M,
-        EM,
-        K,
-        num_tokens,
-        num_experts,
-        num_slices,
-        block_m,
-        64,
-        32,
-        8,
-        4,
-        3,
-        1,
-        inp["num_active"],
-        False,
-    )
-    fused_moe_lora_expand(
-        out,
-        intermediate,
-        inp["lora_b"],
-        inp["topk_weights"],
-        inp["sorted_token_ids"],
-        inp["expert_ids"],
-        inp["num_post"],
-        inp["token_lora_mapping"],
-        top_k,
-        inp["lora_ids"],
-        inp["adapter_enabled"],
-        torch.device(DEVICE),
-        rank,
-        M,
-        EM,
-        K,
-        num_tokens,
-        num_experts,
-        num_slices,
-        rank,
-        N_per_slice,
-        block_m,
-        64,
-        32,
-        8,
-        4,
-        3,
-        1,
-        inp["num_active"],
-        False,
-        0,
-    )
-    return out
-
-
-PROVIDER_FNS = {
-    "one_shot": _run_one_shot,
-    "two_kernel": _run_two_kernel,
-}
-
-
-# ----- model presets ---------------------------------------------------------
-
-
-MODEL_PRESETS: dict[str, dict] = {
-    # Mixtral-8x7B style: E=8, top_k=2, hidden=4096, intermediate=14336
-    "mixtral": dict(
-        K=4096,
-        N_per_slice=7168,
-        num_experts=8,
-        top_k=2,
-        max_loras=4,
-        num_slices=2,
-        block_size_m=64,
-    ),
-    # Qwen3-MoE / DeepSeek-V2 style: E=64, top_k=8, hidden=2048, inter=1408
-    "qwen3moe": dict(
-        K=2048,
-        N_per_slice=1408,
-        num_experts=64,
-        top_k=8,
-        max_loras=4,
-        num_slices=2,
-        block_size_m=64,
-    ),
-    # GLM-5.1 (zai-org/GLM-5.1-FP8): E=256, top_k=8, hidden=6144,
-    # moe_intermediate=2048
-    "glm5_1": dict(
-        K=6144,
-        N_per_slice=2048,
-        num_experts=256,
-        top_k=8,
-        max_loras=4,
-        num_slices=2,
-        block_size_m=64,
-    ),
-}
-
-
-M_RANGE = [16, 64, 256, 1024, 4096, 16384]
-RANK_RANGE = [8, 16, 32, 64]
-
-
-def get_benchmark(model: str, max_loras: int | None = None):
-    preset = dict(MODEL_PRESETS[model])
-    if max_loras is not None:
-        preset["max_loras"] = max_loras
-
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=["M", "rank"],
-            x_vals=[(M, R) for M in M_RANGE for R in RANK_RANGE],
-            line_arg="provider",
-            line_vals=list(PROVIDER_FNS.keys()),
-            line_names=["one_shot (fused)", "two_kernel (legacy)"],
-            styles=[("red", "-"), ("blue", "-")],
-            ylabel="ms",
-            plot_name=f"fused_moe_lora-{model}-loras{preset['max_loras']}",
-            args={"preset": preset},
-        )
-    )
-    def benchmark(M, rank, provider, preset):
-        inp = _make_inputs(
-            M=M,
-            K=preset["K"],
-            N_per_slice=preset["N_per_slice"],
-            rank=rank,
-            num_experts=preset["num_experts"],
-            top_k=preset["top_k"],
-            max_loras=preset["max_loras"],
-            num_slices=preset["num_slices"],
-            block_size_m=preset["block_size_m"],
-        )
-        fn = PROVIDER_FNS[provider]
-        quantiles = [0.5, 0.2, 0.8]
-        ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: fn(inp), quantiles=quantiles
-        )
-        return ms, max_ms, min_ms
-
-    return benchmark
-
-
-# ----- correctness sanity ---------------------------------------------------
-
-
-def calculate_diff(model: str, M: int, rank: int, max_loras: int | None = None):
-    preset = dict(MODEL_PRESETS[model])
-    if max_loras is not None:
-        preset["max_loras"] = max_loras
-    inp = _make_inputs(
-        M=M,
-        K=preset["K"],
-        N_per_slice=preset["N_per_slice"],
-        rank=rank,
-        num_experts=preset["num_experts"],
-        top_k=preset["top_k"],
-        max_loras=preset["max_loras"],
-        num_slices=preset["num_slices"],
-        block_size_m=preset["block_size_m"],
-    )
-    out_one = _run_one_shot(inp)
-    out_two = _run_two_kernel(inp)
-    max_abs = (out_one.float() - out_two.float()).abs().max().item()
-    print(
-        f"  model={model:<9} M={M:<6} rank={rank:<3}  "
-        f"max|one_shot - two_kernel|={max_abs:.4g}  "
-        f"ref|max|={out_two.float().abs().max().item():.3g}"
-    )
-    if max_abs <= 5e-2:
-        print("  ✅ outputs match within bf16 tolerance")
-    else:
-        print("  ❌ outputs differ beyond expected bf16 noise")
-
-
-# ----- main ------------------------------------------------------------------
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="mixtral",
-        choices=list(MODEL_PRESETS.keys()),
-        help="Model preset to sweep",
-    )
-    parser.add_argument(
-        "--save-path",
-        type=str,
-        default="./configs/fused_moe_lora_one_shot/",
-        help="Directory to save benchmark results",
-    )
-    parser.add_argument(
-        "--check-only",
-        action="store_true",
-        help="Run correctness sanity check only, no perf sweep",
-    )
-    parser.add_argument(
-        "--max-loras",
-        type=int,
-        default=None,
-        help="Override max_loras in the model preset (number of LoRA adapters "
-        "active in the batch). Defaults to the preset's value.",
-    )
-    args = parser.parse_args()
-
-    print(f"Correctness check ({args.model}):")
-    calculate_diff(args.model, M=256, rank=32, max_loras=args.max_loras)
-    if args.check_only:
-        raise SystemExit(0)
-
-    effective_max_loras = (
-        args.max_loras
-        if args.max_loras is not None
-        else MODEL_PRESETS[args.model]["max_loras"]
-    )
-    print(f"\nGPU: {torch.cuda.get_device_name()}")
-    print(f"Model preset: {args.model}  max_loras={effective_max_loras}\n")
-    benchmark = get_benchmark(args.model, max_loras=args.max_loras)
-    os.makedirs(args.save_path, exist_ok=True)
-    benchmark.run(print_data=True, save_path=args.save_path)

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -13,7 +13,7 @@ from vllm.model_executor.layers.fused_moe.all2all_utils import (
     maybe_make_prepare_finalize,
 )
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
-from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import CutlassExpertsFp8
+from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts,
     fused_topk,

benchmarks/kernels/benchmark_moe.py

@@ -27,10 +27,10 @@ from vllm.model_executor.layers.fused_moe.config import (
     RoutingMethodType,
     _get_config_dtype_str,
 )
-from vllm.model_executor.layers.fused_moe.experts.triton_deep_gemm_moe import (
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
     TritonOrDeepGemmExperts,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser