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. emoji allowed.
# fast_check(bool): whether to run this on each commit on fastcheck pipeline.
# fast_check_only(bool): run this test on fastcheck pipeline only
# optional(bool): never run this test by default (i.e. need to unblock manually) unless it's scheduled nightly run.
# command(str): the single command to run for tests. incompatible with commands.
# commands(list): the list of commands to run for test. incompatbile with command.
# mirror_hardwares(list): the list of hardwares to run the test on as well. currently only supports [amd]
# gpu(str): override the GPU selection for the test. default is on L4 GPUs. currently only supports a100
# num_gpus(int): override the number of GPUs for the test. default to 1 GPU. currently support 2,4.
# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host,
#     in this case, commands must be specified. the first command runs on first host, the second
#     command runs on the second host.
# working_dir(str): specify the place where command should execute, default to /vllm-workspace/tests
# source_file_dependencies(list): the list of prefix to opt-in the test for, if empty, the test will always run.

# When adding a test
# - If the test belong 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: Documentation Build # 2min
  working_dir: "/vllm-workspace/test_docs/docs"
  fast_check: true
  no_gpu: True
  commands:
  - pip install -r ../../requirements/docs.txt
  - SPHINXOPTS=\"-W\" make html
  # Check API reference (if it fails, you may have missing mock imports)
  - grep \"sig sig-object py\" build/html/api/inference_params.html

- label: Async Engine, Inputs, Utils, Worker Test # 24min
  source_file_dependencies:
  - vllm/
  - tests/mq_llm_engine
  - tests/async_engine
  - tests/test_inputs
  - tests/multimodal
  - tests/test_utils
  - tests/worker
  - tests/standalone_tests/lazy_imports.py
  commands:
  - python3 standalone_tests/lazy_imports.py
  - pytest -v -s mq_llm_engine # MQLLMEngine
  - pytest -v -s async_engine # AsyncLLMEngine
  - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
  - pytest -v -s test_inputs.py
  - pytest -v -s multimodal
  - pytest -v -s test_utils.py # Utils
  - pytest -v -s worker # Worker

- label: Python-only Installation Test
  source_file_dependencies:
  - tests/standalone_tests/python_only_compile.sh
  - setup.py
  commands:
  - bash standalone_tests/python_only_compile.sh

- label: Basic Correctness Test # 30min
  #mirror_hardwares: [amd]
  fast_check: true
  source_file_dependencies:
  - vllm/
  - tests/basic_correctness/test_basic_correctness
  - tests/basic_correctness/test_cpu_offload
  - tests/basic_correctness/test_preemption
  - 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
  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py

- label: Chunked Prefill Test
  source_file_dependencies:
  - vllm/
  - tests/basic_correctness/test_chunked_prefill
  commands:
  - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
  - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py

- label: Core Test # 10min
  mirror_hardwares: [amd]
  fast_check: true
  source_file_dependencies:
  - vllm/core
  - vllm/distributed
  - tests/core
  commands:
  - pytest -v -s core

- label: Entrypoints Test # 40min
  working_dir: "/vllm-workspace/tests"
  fast_check: true
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/entrypoints/llm
  - tests/entrypoints/openai
  - tests/entrypoints/test_chat_utils
  - tests/entrypoints/offline_mode
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
  - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
  - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
  - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py  --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/correctness/
  - pytest -v -s entrypoints/test_chat_utils.py
  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests

- label: Distributed Tests (4 GPUs) # 10min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
  - vllm/distributed/
  - vllm/core/
  - tests/distributed/test_utils
  - tests/distributed/test_pynccl
  - tests/spec_decode/e2e/test_integration_dist_tp4
  - tests/compile/test_basic_correctness
  - examples/offline_inference/rlhf.py
  - examples/offline_inference/rlhf_colocate.py
  - tests/examples/offline_inference/data_parallel.py
  - tests/v1/test_async_llm_dp.py
  commands:
  # test with tp=2 and external_dp=2
  - VLLM_USE_V1=0 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
  # test with internal dp
  - python3 ../examples/offline_inference/data_parallel.py
  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
  - pytest -v -s distributed/test_utils.py
  - pytest -v -s compile/test_basic_correctness.py
  - pytest -v -s distributed/test_pynccl.py
  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
  # TODO: create a dedicated test section for multi-GPU example tests
  # when we have multiple distributed example tests
  - pushd ../examples/offline_inference
  - python3 rlhf.py
  - RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
  - popd

- label: Metrics, Tracing Test # 10min
  mirror_hardwares: [amd]
  num_gpus: 2
  source_file_dependencies:
  - vllm/
  - tests/metrics
  - tests/tracing
  commands:
  - pytest -v -s metrics
  - pytest -v -s tracing

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

- label: Regression Test # 5min
  #mirror_hardwares: [amd]
  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 # 10min
  mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/engine
  - tests/tokenization
  - tests/test_sequence
  - tests/test_config
  - tests/test_logger
  commands:
  - pytest -v -s engine test_sequence.py test_config.py test_logger.py
  # OOM in the CI unless we run this separately
  - pytest -v -s tokenization

- label: V1 Test
  #mirror_hardwares: [amd]
  source_file_dependencies:
    - vllm/
    - tests/v1
  commands:
    # split the test to avoid interference
    - pytest -v -s v1/core
    - pytest -v -s v1/engine
    - pytest -v -s v1/entrypoints
    - pytest -v -s v1/sample
    - pytest -v -s v1/worker
    - pytest -v -s v1/structured_output
    - pytest -v -s v1/test_stats.py
    - pytest -v -s v1/test_utils.py
    - pytest -v -s v1/test_oracle.py
    # TODO: accuracy does not match, whether setting
    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
    - pytest -v -s v1/e2e
    # Integration test for streaming correctness (requires special branch).
    - pip install -U git+https://github.com/robertgshaw2-neuralmagic/lm-evaluation-harness.git@streaming-api
    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine

- label: Examples Test # 25min
  working_dir: "/vllm-workspace/examples"
  #mirror_hardwares: [amd]
  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_embedding.py --seed 0
    - python3 offline_inference/vision_language_multi_image.py --seed 0
    - VLLM_USE_V1=0 python3 other/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 other/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.py
    - 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
    - VLLM_USE_V1=0 python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2

- label: Prefix Caching Test # 9min
  mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/prefix_caching
  commands:
    - pytest -v -s prefix_caching

- label: Samplers Test # 36min
  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: LogitsProcessor Test # 5min
  mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/model_executor/layers
  - vllm/model_executor/guided_decoding
  - tests/test_logits_processor
  - tests/model_executor/test_guided_processors
  commands:
    - pytest -v -s test_logits_processor.py
    - pytest -v -s model_executor/test_guided_processors.py

- label: Speculative decoding tests # 40min
  source_file_dependencies:
  - vllm/spec_decode
  - tests/spec_decode
  - vllm/model_executor/models/eagle.py
  commands:
    - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py --ignore=spec_decode/e2e/test_mtp_correctness.py
    - pytest -v -s spec_decode/e2e/test_eagle_correctness.py

- label: LoRA Test %N # 15min each
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/lora
  - tests/lora
  command: 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
  parallelism: 4

- label: PyTorch Compilation Unit Tests
  source_file_dependencies:
    - vllm/
    - tests/compile
  commands:
    - pytest -v -s compile/test_pass_manager.py
    - pytest -v -s compile/test_fusion.py

- label: PyTorch Fullgraph Smoke Test # 9min
  source_file_dependencies:
  - vllm/
  - tests/compile
  commands:
  - pytest -v -s compile/test_basic_correctness.py
  # these tests need to be separated, cannot combine
  - pytest -v -s compile/piecewise/test_simple.py
  - pytest -v -s compile/piecewise/test_toy_llama.py

- label: PyTorch Fullgraph Test # 18min
  source_file_dependencies:
  - vllm/
  - tests/compile
  commands:
  - pytest -v -s compile/test_full_graph.py

- label: Kernels Test %N # 1h each
  # mirror_hardwares: [amd]
  source_file_dependencies:
  - csrc/
  - vllm/attention
  - tests/kernels
  commands:
    - pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 4

- label: Tensorizer Test # 11min
  # mirror_hardwares: [amd]
  soft_fail: true
  source_file_dependencies:
  - vllm/model_executor/model_loader
  - tests/tensorizer_loader
  commands:
    - apt-get update && apt-get install -y curl libsodium23
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - pytest -v -s tensorizer_loader

- label: Benchmarks # 9min
  working_dir: "/vllm-workspace/.buildkite"
  mirror_hardwares: [amd]
  source_file_dependencies:
  - benchmarks/
  commands:
  - bash scripts/run-benchmarks.sh

- label: Quantization Test # 33min
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  - tests/quantization
  command: VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization

- label: LM Eval Small Models # 53min
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - bash ./run-tests.sh -c configs/models-small.txt -t 1

- label: OpenAI API correctness
  source_file_dependencies:
  - csrc/
  - vllm/entrypoints/openai/
  - vllm/model_executor/models/whisper.py
  commands: # LMEval+Transcription WER check
  - pytest -s entrypoints/openai/correctness/

- label: Encoder Decoder tests # 5min
  source_file_dependencies:
  - vllm/
  - tests/encoder_decoder
  commands:
    - pytest -v -s encoder_decoder

- label: OpenAI-Compatible Tool Use # 20 min
  fast_check: false
  #mirror_hardwares: [ amd ]
  source_file_dependencies:
    - vllm/
    - tests/tool_use
    - tests/mistral_tool_use
  commands:
    - pytest -v -s tool_use
    - pytest -v -s mistral_tool_use

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

- label: Basic Models Test # 24min
  source_file_dependencies:
  - vllm/
  - tests/models
  commands:
    - pytest -v -s models/test_transformers.py
    - pytest -v -s models/test_registry.py
    # V1 Test: https://github.com/vllm-project/vllm/issues/14531
    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4'
    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'

- label: Language Models Test (Standard) # 32min
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/language
  - tests/models/embedding/language
  - tests/models/encoder_decoder/language
  commands:
    - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
    - pytest -v -s models/embedding/language -m core_model

- label: Language Models Test (Extended) # 1h10min
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/language
  - tests/models/embedding/language
  - tests/models/encoder_decoder/language
  commands:
    - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
    - pytest -v -s models/embedding/language -m 'not core_model'

- label: Multi-Modal Models Test (Standard) # 40min
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/audio_language
  - tests/models/decoder_only/vision_language
  - tests/models/embedding/vision_language
  - tests/models/encoder_decoder/audio_language
  - tests/models/encoder_decoder/vision_language
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/multimodal
    - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
    - pytest -v -s models/decoder_only/vision_language -m 'core_model or quant_model'
    - pytest -v -s models/embedding/vision_language -m core_model
    - pytest -v -s models/encoder_decoder/audio_language -m core_model
    - pytest -v -s models/encoder_decoder/language -m core_model
    - pytest -v -s models/encoder_decoder/vision_language -m core_model
    - pytest -v -s models/decoder_only/vision_language/test_interleaved.py

- label: Multi-Modal Models Test (Extended) 1 # 48m
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/audio_language
  - tests/models/decoder_only/vision_language
  - tests/models/embedding/vision_language
  - tests/models/encoder_decoder/vision_language
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model'
    - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
    - pytest -v -s models/embedding/vision_language -m 'not core_model'
    - pytest -v -s models/encoder_decoder/language -m 'not core_model'
    - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'

- label: Multi-Modal Models Test (Extended) 2 # 38m
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/vision_language
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=1) and not core_model and not quant_model'

# 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: [amd]
  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*

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

- label: Distributed Comm Ops Test # 7min
  mirror_hardwares: [amd]
  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

- label: 2 Node Tests (4 GPUs in total) # 16min
  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/
  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'
    - 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'

- label: Distributed Tests (2 GPUs) # 40min
  #mirror_hardwares: [amd]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/distributed/
  - vllm/engine/
  - vllm/executor/
  - vllm/model_executor/models/
  - tests/distributed/
  - vllm/compilation
  - vllm/worker/worker_base.py
  - vllm/worker/worker.py
  - vllm/worker/model_runner.py
  - entrypoints/llm/test_collective_rpc.py
  - tests/v1/test_async_llm_dp.py
  - vllm/v1/engine/
  commands:
  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
  - pytest -v -s entrypoints/llm/test_collective_rpc.py
  - pytest -v -s ./compile/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'
  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
  # Avoid importing model tests that cause CUDA reinitialization error
  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)'
  # this test fails consistently.
  # TODO: investigate and fix
  # - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py

- label: Plugin Tests (2 GPUs) # 40min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/plugins/
  - tests/plugins/
  commands:
  # begin platform 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
  # 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

- label: Multi-step Tests (4 GPUs) # 36min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
  - vllm/model_executor/layers/sampler.py
  - vllm/sequence.py
  - vllm/worker/worker_base.py
  - vllm/worker/worker.py
  - vllm/worker/multi_step_worker.py
  - vllm/worker/model_runner_base.py
  - vllm/worker/model_runner.py
  - vllm/worker/multi_step_model_runner.py
  - vllm/engine
  - tests/multi_step
  commands:
  # this test is quite flaky
  # TODO: investigate and fix.
  # - pytest -v -s multi_step/test_correctness_async_llm.py
  - pytest -v -s multi_step/test_correctness_llm.py

- label: Pipeline Parallelism Test # 45min
  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)
  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


- label: Weight Loading Multiple GPU Test  # 33min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  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
  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


##### 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
  - bash ./run-tests.sh -c configs/models-large.txt -t 4