vllm/.buildkite/test-pipeline.yaml

# In this file, you can add more tests to run either by adding a new step or
# adding a new command to an existing step. See different options here for examples.

# This script will be feed into Jinja template in `test-template-aws.j2` at
# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2
# to generate the final pipeline yaml file.

# Documentation
# label(str): the name of the test. emojis allowed.
# fast_check(bool): whether to run this on each commit on the fastcheck pipeline.
# torch_nightly(bool): whether to run this on vllm against the torch nightly pipeline.
# fast_check_only(bool): run this test on the fastcheck pipeline only
# optional(bool): never run this test by default (i.e. need to unblock manually) unless it's a scheduled nightly run.
# soft_fail(bool): allow this step to fail without failing the entire pipeline (useful for flaky or experimental tests).
# command(str): the single command to run for tests. incompatible with commands.
# commands(list): the list of commands to run for the test. incompatible with command.
# mirror_hardwares(list): the list of hardware to run the test on as well. currently only supports [amdexperimental]
# gpu(str): override the GPU selection for the test. default is L4 GPUs. supports a100, b200, h200
# num_gpus(int): override the number of GPUs for the test. defaults to 1 GPU. currently supports 2,4.
# num_nodes(int): whether to simulate multi-node setup by launching multiple containers on one host,
#     in this case, commands must be specified. the first command runs on the first host, the second
#     command runs on the second host.
# timeout_in_minutes(int): sets a timeout for the step in minutes. if not specified, uses the default timeout.
# parallelism(int): number of parallel jobs to run for this step. enables test sharding using $$BUILDKITE_PARALLEL_JOB
#     and $$BUILDKITE_PARALLEL_JOB_COUNT environment variables.
# working_dir(str): specify the place where the command should execute, default to /vllm-workspace/tests
# source_file_dependencies(list): the list of prefixes to opt-in the test for, if empty, the test will always run.
# autorun_on_main (bool): default to false, if true, the test will run automatically when commit is pushed to main branch.

# When adding a test
# - If the test belongs to an existing group, add it there
# - If the test is short, add to any existing step
# - If the test takes more than 10min, then it is okay to create a new step.
#   Note that all steps execute in parallel.

steps:
##### fast check tests  #####

- label: Pytorch Nightly Dependency Override Check # 2min
  # 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
  soft_fail: true
  source_file_dependencies:
  - requirements/nightly_torch_test.txt
  commands:
  - bash standalone_tests/pytorch_nightly_dependency.sh

- label: Async Engine, Inputs, Utils, Worker Test # 36min
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/multimodal
  - tests/utils_
  commands:
  - pytest -v -s -m 'not cpu_test' multimodal
  - pytest -v -s utils_

- label: Async Engine, Inputs, Utils, Worker, Config Test (CPU) # 15min
  timeout_in_minutes: 20
  source_file_dependencies:
  - vllm/
  - tests/test_inputs.py
  - tests/test_outputs.py
  - tests/multimodal
  - tests/standalone_tests/lazy_imports.py
  - tests/tokenizers_
  - tests/transformers_utils
  - tests/config
  no_gpu: true
  commands:
  - python3 standalone_tests/lazy_imports.py
  - pytest -v -s test_inputs.py
  - pytest -v -s test_outputs.py
  - pytest -v -s -m 'cpu_test' multimodal
  - pytest -v -s tokenizers_
  - pytest -v -s transformers_utils
  - pytest -v -s config

- label: Python-only Installation Test # 10min
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - tests/standalone_tests/python_only_compile.sh
  - setup.py
  commands:
  - bash standalone_tests/python_only_compile.sh

- label: Basic Correctness Test # 20min
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  fast_check: true
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/basic_correctness/test_basic_correctness
  - tests/basic_correctness/test_cpu_offload
  - tests/basic_correctness/test_cumem.py
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -v -s basic_correctness/test_cumem.py
  - pytest -v -s basic_correctness/test_basic_correctness.py
  - pytest -v -s basic_correctness/test_cpu_offload.py

- label: Entrypoints Unit Tests # 5min
  timeout_in_minutes: 10
  working_dir: "/vllm-workspace/tests"
  fast_check: true
  source_file_dependencies:
  - vllm/entrypoints
  - tests/entrypoints/
  commands:
  - pytest -v -s entrypoints/openai/tool_parsers
  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling

- label: Entrypoints Integration Test (LLM) # 30min
  timeout_in_minutes: 40
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  fast_check: true
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/entrypoints/llm
  - tests/entrypoints/offline_mode
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - 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

- label: Entrypoints Integration Test (API Server) # 100min
  timeout_in_minutes: 130
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  fast_check: true
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/entrypoints/openai
  - tests/entrypoints/test_chat_utils
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/openai/test_collective_rpc.py # PYTHONPATH is needed to import custom Worker extension
  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_collective_rpc.py --ignore=entrypoints/openai/tool_parsers/
  - pytest -v -s entrypoints/test_chat_utils.py

- label: Entrypoints Integration Test (Pooling)
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  fast_check: true
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/entrypoints/pooling
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -v -s entrypoints/pooling

- label: Distributed Tests (4 GPUs) # 35min
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
  - vllm/distributed/
  - tests/distributed/test_utils
  - tests/distributed/test_pynccl
  - tests/distributed/test_events
  - tests/compile/fullgraph/test_basic_correctness.py
  - examples/offline_inference/rlhf.py
  - examples/offline_inference/rlhf_colocate.py
  - tests/examples/offline_inference/data_parallel.py
  - tests/v1/distributed
  - tests/v1/engine/test_engine_core_client.py
  - tests/distributed/test_symm_mem_allreduce.py
  commands:
  # https://github.com/NVIDIA/nccl/issues/1838
  - export NCCL_CUMEM_HOST_ENABLE=0
  # test with torchrun tp=2 and external_dp=2
  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
  # test with torchrun tp=2 and pp=2
  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
  # test with torchrun tp=4 and dp=1
  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
  # test with torchrun tp=2, pp=2 and dp=1
  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
  # test with torchrun tp=1 and dp=4 with ep
  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
  # test with torchrun tp=2 and dp=2 with ep
  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
  # test with internal dp
  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
  - pytest -v -s distributed/test_utils.py
  - pytest -v -s compile/fullgraph/test_basic_correctness.py
  - pytest -v -s distributed/test_pynccl.py
  - pytest -v -s distributed/test_events.py
  - pytest -v -s distributed/test_symm_mem_allreduce.py
  # TODO: create a dedicated test section for multi-GPU example tests
  # when we have multiple distributed example tests
  - pushd ../examples/offline_inference
  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
  - popd

- label: Distributed Tests (8 GPUs) # 4min
  timeout_in_minutes: 10
  gpu: h100
  num_gpus: 8
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - 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
  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/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep

- label: EPLB Algorithm Test # 5min
  timeout_in_minutes: 15
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/distributed/eplb
  - tests/distributed/test_eplb_algo.py
  commands:
  - pytest -v -s distributed/test_eplb_algo.py

- label: EPLB Execution Test # 10min
  timeout_in_minutes: 20
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
  - vllm/distributed/eplb
  - tests/distributed/test_eplb_execute.py
  commands:
  - pytest -v -s distributed/test_eplb_execute.py
  - pytest -v -s distributed/test_eplb_spec_decode.py

- label: Metrics, Tracing Test # 12min
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  num_gpus: 2
  source_file_dependencies:
  - vllm/
  - tests/v1/tracing
  commands:
  - "pip install \
      'opentelemetry-sdk>=1.26.0' \
      'opentelemetry-api>=1.26.0' \
      'opentelemetry-exporter-otlp>=1.26.0' \
      'opentelemetry-semantic-conventions-ai>=0.4.1'"
  - pytest -v -s v1/tracing

##### fast check tests  #####
#####  1 GPU test  #####

- label: Regression Test # 7min
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/test_regression
  commands:
  - pip install modelscope
  - pytest -v -s test_regression.py
  working_dir: "/vllm-workspace/tests" # optional

- label: Engine Test # 9min
  timeout_in_minutes: 15
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/engine
  - tests/test_sequence
  - tests/test_config
  - tests/test_logger
  - tests/test_vllm_port
  commands:
  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py

- label: V1 Test e2e + engine # 30min
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
    - vllm/
    - tests/v1
  commands:
    # TODO: accuracy does not match, whether setting
    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
    - pytest -v -s v1/e2e
    - pytest -v -s v1/engine

- label: V1 Test entrypoints # 35min
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
    - vllm/
    - tests/v1
  commands:
    - pytest -v -s v1/entrypoints

- label: V1 Test others # 42min
  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
    - vllm/
    - tests/v1
  commands:
    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
    # split the test to avoid interference
    - pytest -v -s -m 'not cpu_test' v1/core
    - pytest -v -s v1/executor
    - pytest -v -s v1/kv_offload
    - pytest -v -s v1/sample
    - pytest -v -s v1/logits_processors
    - pytest -v -s v1/worker
    - pytest -v -s v1/spec_decode
    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/metrics
    - pytest -v -s v1/test_oracle.py
    - pytest -v -s v1/test_request.py
    - pytest -v -s v1/test_outputs.py
    # 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

- label: V1 Test attention (H100) # 10min
  timeout_in_minutes: 30
  gpu: h100
  source_file_dependencies:
    - vllm/v1/attention
    - tests/v1/attention
  commands:
    - pytest -v -s v1/attention

- label: Batch Invariance Tests (H100) # 10min
  timeout_in_minutes: 25
  gpu: h100
  source_file_dependencies:
    - vllm/
    - 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
    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py

- label: V1 Test attention (B200) # 10min
  timeout_in_minutes: 30
  gpu: b200
  source_file_dependencies:
    - vllm/v1/attention
    - tests/v1/attention
  commands:
    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this

- label: V1 Test others (CPU) # 5 mins
  source_file_dependencies:
    - vllm/
    - tests/v1
  no_gpu: true
  commands:
    # split the test to avoid interference
    - pytest -v -s -m 'cpu_test' v1/core
    - pytest -v -s v1/structured_output
    - pytest -v -s v1/test_serial_utils.py
    - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'cpu_test' v1/metrics


- label: Examples Test # 30min
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/examples"
  source_file_dependencies:
  - vllm/entrypoints
  - examples/
  commands:
    - pip install tensorizer # for tensorizer test
    - python3 offline_inference/basic/generate.py --model facebook/opt-125m
    - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
    - python3 offline_inference/basic/chat.py
    - python3 offline_inference/prefix_caching.py
    - python3 offline_inference/llm_engine_example.py
    - python3 offline_inference/audio_language.py --seed 0
    - python3 offline_inference/vision_language.py --seed 0
    - python3 offline_inference/vision_language_pooling.py --seed 0
    - python3 offline_inference/vision_language_multi_image.py --seed 0
    - 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/encoder_decoder_multimodal.py --model-type whisper --seed 0
    - python3 offline_inference/basic/classify.py
    - python3 offline_inference/basic/embed.py
    - python3 offline_inference/basic/score.py
    - 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 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: Platform Tests (CUDA) # 4min
  timeout_in_minutes: 15
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/cuda
  commands:
    - pytest -v -s cuda/test_cuda_context.py

- label: Samplers Test # 56min
  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/model_executor/layers
  - vllm/sampling_metadata.py
  - tests/samplers
  - tests/conftest.py
  commands:
    - pytest -v -s samplers
    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers

- label: LoRA Test %N # 20min each
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  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_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

  parallelism: 4

- label: PyTorch Compilation Unit Tests # 15min
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
    - vllm/
    - tests/compile
  commands:
  # Run unit tests defined directly under compile/,
  # not including subdirectories, which are usually heavier
  # tests covered elsewhere.
  # Use `find` to launch multiple instances of pytest so that
  # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
  - "find compile/ -maxdepth 1 -name 'test_*.py' -exec pytest -s -v {} \\\\;"

- label: PyTorch Fullgraph Smoke Test # 15min
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/compile
  commands:
  # Run smoke tests under fullgraph directory, except test_full_graph.py
  # as it is a heavy test that is covered in other steps.
  # Use `find` to launch multiple instances of pytest so that
  # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -exec pytest -s -v {} \\\\;"

- label: PyTorch Fullgraph Test # 27min
  timeout_in_minutes: 40
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - 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'
    # Limit to no custom ops to reduce running time
    # Wrap with quotes to escape yaml and avoid starting -k string with a -
  - "pytest -v -s compile/distributed/test_fusions_e2e.py -k 'TRITON and not +quant_fp8 and not Llama-4'"

- label: Cudagraph test
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - tests/v1/cudagraph
  - vllm/v1/cudagraph_dispatcher.py
  - vllm/config/compilation.py
  - vllm/compilation
  commands:
    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py

- label: Kernels Core Operation Test # 48min
  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
  - tests/kernels/core
  - tests/kernels/test_top_k_per_row.py
  commands:
    - pytest -v -s kernels/core kernels/test_top_k_per_row.py

- label: Kernels Attention Test %N # 23min
  timeout_in_minutes: 35
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/attention/
  - vllm/attention
  - vllm/v1/attention
  - tests/kernels/attention
  commands:
    - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels Quantization Test %N # 64min
  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/quantization/
  - vllm/model_executor/layers/quantization
  - tests/kernels/quantization
  commands:
    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels MoE Test %N # 40min
  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/quantization/cutlass_w8a8/moe/
  - csrc/moe/
  - tests/kernels/moe
  - vllm/model_executor/layers/fused_moe/
  - vllm/distributed/device_communicators/
  - vllm/envs.py
  - vllm/config
  commands:
    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels Mamba Test # 31min
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/mamba/
  - tests/kernels/mamba
  - vllm/model_executor/layers/mamba/ops
  commands:
    - pytest -v -s kernels/mamba

- label: Kernels DeepGEMM Test (H100)
  timeout_in_minutes: 45
  gpu: h100
  num_gpus: 1
  source_file_dependencies:
  - tools/install_deepgemm.sh
  - vllm/utils/deep_gemm.py
  - vllm/model_executor/layers/fused_moe
  - vllm/model_executor/layers/quantization
  - tests/kernels/quantization/test_block_fp8.py
  - tests/kernels/moe/test_deepgemm.py
  - tests/kernels/moe/test_batched_deepgemm.py
  - tests/kernels/attention/test_deepgemm_attention.py
  commands:
    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
    - 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

- label: Model Executor Test # 23min
  timeout_in_minutes: 35
  torch_nightly: true
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/engine/arg_utils.py
  - vllm/config/model.py
  - vllm/model_executor
  - tests/model_executor
  - tests/entrypoints/openai/test_tensorizer_entrypoint.py
  commands:
    - apt-get update && apt-get install -y curl libsodium23
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - pytest -v -s model_executor
    - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py

- label: Benchmarks # 11min
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/.buildkite"
  source_file_dependencies:
  - benchmarks/
  commands:
  - bash scripts/run-benchmarks.sh

- label: Benchmarks CLI Test # 7min
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/
  - tests/benchmarks/
  commands:
  - pytest -v -s benchmarks/

- label: Quantization Test # 70min
  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  - tests/quantization
  commands:
  # temporary install here since we need nightly, will move to requirements/test.in
  # after torchao 0.12 release, and pin a working version of torchao nightly here

  # since torchao nightly is only compatible with torch nightly currently
  # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
  # we can only upgrade after this is resolved
  # TODO(jerryzh168): resolve the above comment
  - uv pip install --system torchao==0.13.0 --index-url https://download.pytorch.org/whl/cu129
  - uv pip install --system conch-triton-kernels
  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py

- label: LM Eval Small Models # 53min
  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  autorun_on_main: true
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1

- label: OpenAI API correctness # 22min
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - csrc/
  - vllm/entrypoints/openai/
  - vllm/model_executor/models/whisper.py
  commands: # LMEval+Transcription WER check
  - pytest -s entrypoints/openai/correctness/

- label: OpenAI-Compatible Tool Use # 23 min
  timeout_in_minutes: 35
  mirror_hardwares: [amdexperimental]
  fast_check: false
  source_file_dependencies:
    - vllm/
    - tests/tool_use
  commands:
    - pytest -v -s -m 'not cpu_test' tool_use

- label: OpenAI-Compatible Tool Use (CPU) # 5 mins
  timeout_in_minutes: 10
  source_file_dependencies:
    - vllm/
    - tests/tool_use
  no_gpu: true
  commands:
    - pytest -v -s -m 'cpu_test' tool_use

#####  models test  #####

- label: Basic Models Tests (Initialization)
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/test_initialization.py
  commands:
    # Run a subset of model initialization tests
    - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset

- label: Basic Models Tests (Extra Initialization) %N
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/model_executor/models/
  - vllm/transformers_utils/
  - tests/models/test_initialization.py
  commands:
    # Only when vLLM model source is modified - test initialization of a large
    # subset of supported models (the complement of the small subset in the above
    # test.) Also run if model initialization test file is modified
    - pytest -v -s models/test_initialization.py \
             -k 'not test_can_initialize_small_subset' \
             --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
             --shard-id=$$BUILDKITE_PARALLEL_JOB
  parallelism: 2

- label: Basic Models Tests (Other)
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/test_transformers.py
  - tests/models/test_registry.py
  commands:
    - pytest -v -s models/test_transformers.py models/test_registry.py

- label: Basic Models Test (Other CPU) # 5min
  timeout_in_minutes: 10
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/test_utils.py
  - tests/models/test_vision.py
  no_gpu: true
  commands:
    - pytest -v -s models/test_utils.py models/test_vision.py

- label: Language Models Tests (Standard)
  timeout_in_minutes: 25
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/language
  commands:
    # Test standard language models, excluding a subset of slow tests
    - pip freeze | grep -E 'torch'
    - pytest -v -s models/language -m 'core_model and (not slow_test)'

- label: Language Models Tests (Extra Standard) %N
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/model_executor/models/
  - tests/models/language/pooling/test_embedding.py
  - tests/models/language/generation/test_common.py
  - tests/models/language/pooling/test_classification.py
  commands:
    # Shard slow subset of standard language models tests. Only run when model
    # source is modified, or when specified test files are modified
    - pip freeze | grep -E 'torch'
    - pytest -v -s models/language -m 'core_model and slow_test' \
             --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
             --shard-id=$$BUILDKITE_PARALLEL_JOB
  parallelism: 2

- label: Language Models Tests (Hybrid) %N
  timeout_in_minutes: 75
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/language/generation
  commands:
    # Install fast path packages for testing against transformers
    # Note: also needed to run plamo2 model in vLLM
    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
    # Shard hybrid language model tests
    - pytest -v -s models/language/generation \
                   -m hybrid_model \
                   --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
                   --shard-id=$$BUILDKITE_PARALLEL_JOB
  parallelism: 2

- label: Language Models Test (Extended Generation) # 80min
  timeout_in_minutes: 110
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/language/generation
  commands:
    # Install fast path packages for testing against transformers
    # Note: also needed to run plamo2 model in vLLM
    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
    - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'

- label: Language Models Test (PPL)
  timeout_in_minutes: 110
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/language/generation_ppl_test
  commands:
    - pytest -v -s models/language/generation_ppl_test

- label: Language Models Test (Extended Pooling)  # 36min
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/language/pooling
  commands:
    - pytest -v -s models/language/pooling -m 'not core_model'

- label: Language Models Test (MTEB)
  timeout_in_minutes: 110
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/language/pooling_mteb_test
  commands:
    - pytest -v -s models/language/pooling_mteb_test

- label: Multi-Modal Processor Test (CPU)
  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  no_gpu: true
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py

- label: Multi-Modal Processor Test
  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal/processing/test_tensor_schema.py

- label: Multi-Modal Models Test (Standard) # 60min
  timeout_in_minutes: 80
  mirror_hardwares: [amdexperimental]
  torch_nightly: true
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pip freeze | grep -E 'torch'
    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
    - 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

- label: Multi-Modal Accuracy Eval (Small Models) # 50min
  timeout_in_minutes: 70
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - vllm/multimodal/
  - vllm/inputs/
  - vllm/v1/core/
  commands:
  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1

- label: Multi-Modal Models Test (Extended) 1
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal -m 'not core_model' --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing

- label: Multi-Modal Models Test (Extended) 2
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'

- label: Multi-Modal Models Test (Extended) 3
  mirror_hardwares: [amdexperimental]
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'

- label: Quantized Models Test # 45 min
  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  source_file_dependencies:
  - vllm/model_executor/layers/quantization
  - tests/models/quantization
  commands:
    - pytest -v -s models/quantization

# This test is used only in PR development phase to test individual models and should never run on main
- label: Custom Models Test
  mirror_hardwares: [amdexperimental]
  optional: true
  commands:
    - echo 'Testing custom models...'
    # PR authors can temporarily add commands below to test individual models
    # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
    # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*

- label: Transformers Nightly Models Test
  working_dir: "/vllm-workspace/"
  optional: true
  soft_fail: true
  commands:
    - pip install --upgrade git+https://github.com/huggingface/transformers
    - pytest -v -s tests/models/test_initialization.py
    - pytest -v -s tests/models/test_transformers.py
    - pytest -v -s tests/models/multimodal/processing/
    - pytest -v -s tests/models/multimodal/test_mapping.py
    - python3 examples/offline_inference/basic/chat.py
    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
    # Whisper needs spawn method to avoid deadlock
    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

- label: Blackwell Test # 21 min
  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/"
  gpu: b200
  # optional: true
  source_file_dependencies:
  - csrc/quantization/fp4/
  - csrc/attention/mla/
  - csrc/quantization/cutlass_w8a8/moe/
  - 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_cutlass_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
  - vllm/v1/attention/backends/mla/flashinfer_mla.py
  - vllm/platforms/cuda.py
  - vllm/attention/selector.py
  commands:
    - nvidia-smi
    - python3 examples/offline_inference/basic/chat.py
    # Attention
    # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
    - pytest -v -s tests/kernels/attention/test_attention_selector.py
    - pytest -v -s tests/kernels/attention/test_flashinfer.py -k 'not num_heads2'
    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
    - pytest -v -s tests/kernels/attention/test_cutlass_mla_decode.py
    - pytest -v -s tests/kernels/attention/test_flashinfer_mla_decode.py
    # Quantization
    - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
    - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
    - pytest -v -s tests/kernels/quantization/test_silu_mul_nvfp4_quant.py
    - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
    - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
    - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
    - 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_ocp_mx_moe.py
    - pytest -v -s tests/kernels/moe/test_flashinfer.py
    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py

- label: Blackwell Fusion and Compile Tests # 30 min
  timeout_in_minutes: 40
  working_dir: "/vllm-workspace/"
  gpu: b200
  source_file_dependencies:
  - csrc/quantization/fp4/
  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
  - vllm/v1/attention/backends/flashinfer.py
  - vllm/v1/worker/
  - vllm/v1/cudagraph_dispatcher.py
  - vllm/compilation/
  # can affect pattern matching
  - vllm/model_executor/layers/layernorm.py
  - vllm/model_executor/layers/activation.py
  - vllm/model_executor/layers/quantization/input_quant_fp8.py
  - tests/compile/test_fusion_attn.py
  - tests/compile/test_silu_mul_quant_fusion.py
  - tests/compile/distributed/test_fusion_all_reduce.py
  - tests/compile/distributed/test_fusions_e2e.py
  - tests/compile/fullgraph/test_full_graph.py
  commands:
    - nvidia-smi
    - pytest -v -s tests/compile/test_fusion_attn.py
    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
    # this runner has 2 GPUs available even though num_gpus=2 is not set
    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
    # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
    # Wrap with quotes to escape yaml
    - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile

- label: Blackwell Fusion E2E Tests # 30 min
  timeout_in_minutes: 40
  working_dir: "/vllm-workspace/"
  gpu: b200
  optional: true
  num_gpus: 2
  source_file_dependencies:
  - csrc/quantization/fp4/
  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
  - vllm/v1/attention/backends/flashinfer.py
  - vllm/compilation/
  # can affect pattern matching
  - vllm/model_executor/layers/layernorm.py
  - vllm/model_executor/layers/activation.py
  - vllm/model_executor/layers/quantization/input_quant_fp8.py
  - tests/compile/distributed/test_fusions_e2e.py
  commands:
    - nvidia-smi
    # Run all e2e fusion tests
    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py

- label: Blackwell GPT-OSS Eval
  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/"
  gpu: b200
  optional: true # run on nightlies
  source_file_dependencies:
  - tests/evals/gpt_oss
  - vllm/model_executor/models/gpt_oss.py
  - vllm/model_executor/layers/quantization/mxfp4.py
  - vllm/v1/attention/backends/flashinfer.py
  commands:
    - uv pip install --system 'gpt-oss[eval]==0.0.5'
    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58

- label: Blackwell Quantized MoE Test
  timeout_in_minutes: 60
  working_dir: "/vllm-workspace/"
  gpu: b200
  source_file_dependencies:
  - tests/quantization/test_blackwell_moe.py
  - vllm/model_executor/models/deepseek_v2.py
  - vllm/model_executor/models/gpt_oss.py
  - vllm/model_executor/models/llama4.py
  - vllm/model_executor/layers/fused_moe
  - vllm/model_executor/layers/quantization/compressed_tensors
  - vllm/model_executor/layers/quantization/modelopt.py
  - vllm/model_executor/layers/quantization/mxfp4.py
  - vllm/v1/attention/backends/flashinfer.py
  commands:
    - pytest -s -v tests/quantization/test_blackwell_moe.py

- label: Blackwell LM Eval Small Models
  timeout_in_minutes: 120
  gpu: b200
  optional: true # run on nightlies
  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.txt --tp-size=1

#####  1 GPU test  #####
#####  multi gpus test  #####

- label: Distributed Comm Ops Test # 7min
  timeout_in_minutes: 20
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/distributed
  - tests/distributed
  commands:
  - pytest -v -s distributed/test_comm_ops.py
  - pytest -v -s distributed/test_shm_broadcast.py
  - pytest -v -s distributed/test_shm_buffer.py
  - pytest -v -s distributed/test_shm_storage.py

- label: 2 Node Tests (4 GPUs in total) # 16min
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  num_nodes: 2
  source_file_dependencies:
  - vllm/distributed/
  - vllm/engine/
  - vllm/executor/
  - vllm/model_executor/models/
  - tests/distributed/
  - tests/examples/offline_inference/data_parallel.py
  commands:
  - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
    - 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-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --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
  - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
    - 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-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code

- label: Distributed Tests (2 GPUs) # 68min
  timeout_in_minutes: 90
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/compilation/
  - vllm/distributed/
  - vllm/engine/
  - vllm/executor/
  - vllm/worker/worker_base.py
  - vllm/v1/engine/
  - vllm/v1/worker/
  - tests/compile/fullgraph/test_basic_correctness.py
  - tests/compile/test_wrapper.py
  - tests/distributed/
  - tests/entrypoints/llm/test_collective_rpc.py
  - tests/v1/distributed
  - tests/v1/entrypoints/openai/test_multi_api_servers.py
  - tests/v1/shutdown
  - tests/v1/worker/test_worker_memory_snapshot.py
  commands:
  # https://github.com/NVIDIA/nccl/issues/1838
  - export NCCL_CUMEM_HOST_ENABLE=0
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
  - pytest -v -s entrypoints/llm/test_collective_rpc.py
  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
  - pytest -v -s ./compile/test_wrapper.py
  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
  - pytest -v -s distributed/test_sequence_parallel.py
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
  - pytest -v -s v1/worker/test_worker_memory_snapshot.py

- label: Distributed Model Tests (2 GPUs) # 37min
  timeout_in_minutes: 50
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/model_executor/model_loader/sharded_state_loader.py
  - vllm/model_executor/models/
  - tests/basic_correctness/
  - tests/model_executor/model_loader/test_sharded_state_loader.py
  - tests/models/
  commands:
  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py
  # Avoid importing model tests that cause CUDA reinitialization error
  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
  - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'

- label: Plugin Tests (2 GPUs) # 40min
  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/plugins/
  - tests/plugins/
  commands:
  # begin platform plugin and general plugin tests, all the code in-between runs on dummy platform
  - pip install -e ./plugins/vllm_add_dummy_platform
  - pytest -v -s plugins_tests/test_platform_plugins.py
  - pip uninstall vllm_add_dummy_platform -y
  # end platform plugin tests
  # begin io_processor plugins test, all the code in between uses the prithvi_io_processor plugin
  - pip install -e ./plugins/prithvi_io_processor_plugin
  - pytest -v -s plugins_tests/test_io_processor_plugins.py
  - pip uninstall prithvi_io_processor_plugin -y
  # end io_processor plugins test
  # begin stat_logger plugins test
  - pip install -e ./plugins/vllm_add_dummy_stat_logger
  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
  - pip uninstall dummy_stat_logger -y
  # end stat_logger plugins test
  # other tests continue here:
  - pytest -v -s plugins_tests/test_scheduler_plugins.py
  - pip install -e ./plugins/vllm_add_dummy_model
  - pytest -v -s distributed/test_distributed_oot.py
  - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
  - pytest -v -s models/test_oot_registration.py # it needs a clean process
  - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins

- label: Pipeline + Context Parallelism Test # 45min
  timeout_in_minutes: 60
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
  - vllm/distributed/
  - vllm/engine/
  - vllm/executor/
  - vllm/model_executor/models/
  - tests/distributed/
  commands:
  - pytest -v -s distributed/test_pp_cudagraph.py
  - pytest -v -s distributed/test_pipeline_parallel.py

- label: LoRA TP Test (Distributed) # 17 min
  timeout_in_minutes: 30
  mirror_hardwares: [amdexperimental]
  num_gpus: 4
  source_file_dependencies:
  - vllm/lora
  - tests/lora
  commands:
    # FIXIT: find out which code initialize cuda before running the test
    # before the fix, we need to use spawn to test it
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    # There is some Tensor Parallelism related processing logic in LoRA that
    # 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_llm_with_multi_loras.py
    - pytest -v -s -x lora/test_olmoe_tp.py
    - pytest -v -s -x lora/test_gptoss_tp.py


- label: Weight Loading Multiple GPU Test  # 33min
  timeout_in_minutes: 45
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/weight_loading
  commands:
    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt

- label: Weight Loading Multiple GPU Test - Large Models # optional
  mirror_hardwares: [amdexperimental]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  gpu: a100
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/weight_loading
  commands:
    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt

- label: NixlConnector PD accuracy tests (Distributed) # 30min
  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
  num_gpus: 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/tp_config_sweep_accuracy_test.sh


##### multi gpus test #####
##### A100 test #####

- label: Distributed Tests (A100) # optional
  gpu: a100
  optional: true
  num_gpus: 4
  source_file_dependencies:
  - vllm/
  commands:
  # NOTE: don't test llama model here, it seems hf implementation is buggy
  # see https://github.com/vllm-project/vllm/pull/5689 for details
  - pytest -v -s distributed/test_custom_all_reduce.py
  - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py
  - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
  - pytest -v -s -x lora/test_mixtral.py

- label: LM Eval Large Models # optional
  gpu: a100
  optional: true
  num_gpus: 4
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4

##### H100 test #####
- label: LM Eval Large Models (H100) # optional
  gpu: h100
  optional: true
  num_gpus: 4
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
    - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
    - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4

##### H200 test #####
- label: Distributed Tests (H200) # optional
  gpu: h200
  optional: true
  working_dir: "/vllm-workspace/"
  num_gpus: 2
  commands:
    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_async_tp.py
    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
    - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
    - pytest -v -s tests/distributed/test_context_parallel.py
    - CUDA_VISIBLE_DEVICES=1,2 VLLM_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-size=1  --dp-size=2 --max-model-len 2048
    - pytest -v -s tests/v1/distributed/test_dbo.py

##### B200 test #####
- label: Distributed Tests (B200) # optional
  gpu: b200
  optional: true
  working_dir: "/vllm-workspace/"
  num_gpus: 2
  commands:
    - 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

##### RL Integration Tests #####
- label: Prime-RL Integration Test # 15min
  timeout_in_minutes: 30
  optional: true
  num_gpus: 2
  working_dir: "/vllm-workspace"
  source_file_dependencies:
  - vllm/
  - .buildkite/scripts/run-prime-rl-test.sh
  commands:
    - bash .buildkite/scripts/run-prime-rl-test.sh

- label: DeepSeek V2-Lite Accuracy
  timeout_in_minutes: 60
  gpu: h100
  optional: true
  num_gpus: 4
  working_dir: "/vllm-workspace"
  commands:
  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010

- label: Qwen3-30B-A3B-FP8-block Accuracy (H100)
  timeout_in_minutes: 60
  gpu: h100
  optional: true
  num_gpus: 4
  working_dir: "/vllm-workspace"
  commands:
  - 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)
  timeout_in_minutes: 60
  gpu: b200
  optional: true
  num_gpus: 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