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Update AMD test definitions (2025-12-08) (#30298)
Signed-off-by: Alexei V. Ivanov <alexei.ivanov@amd.com>
This commit is contained in:
Alexei-V-Ivanov-AMD
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@ -398,7 +398,8 @@ steps:
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timeout_in_minutes: 25
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gpu: h100
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source_file_dependencies:
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- vllm/
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- vllm/v1/attention
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- vllm/model_executor/layers
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- tests/v1/determinism/
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commands:
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- export VLLM_WORKER_MULTIPROC_METHOD=spawn
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@ -440,23 +441,29 @@ steps:
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working_dir: "/vllm-workspace/examples"
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source_file_dependencies:
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- vllm/entrypoints
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- vllm/multimodal
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- examples/
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commands:
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- pip install tensorizer # for tensorizer test
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# for basic
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- python3 offline_inference/basic/chat.py
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- python3 offline_inference/basic/generate.py --model facebook/opt-125m
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- python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
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- python3 offline_inference/basic/chat.py
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- python3 offline_inference/prefix_caching.py
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- python3 offline_inference/llm_engine_example.py
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- python3 offline_inference/basic/classify.py
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- python3 offline_inference/basic/embed.py
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- python3 offline_inference/basic/score.py
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# for multi-modal models
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- python3 offline_inference/audio_language.py --seed 0
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- python3 offline_inference/vision_language.py --seed 0
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- python3 offline_inference/vision_language_pooling.py --seed 0
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- python3 offline_inference/vision_language_multi_image.py --seed 0
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- 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
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- python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
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- python3 offline_inference/basic/classify.py
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- python3 offline_inference/basic/embed.py
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- python3 offline_inference/basic/score.py
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# for pooling models
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- python3 pooling/pooling/vision_language_pooling.py --seed 0
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# for features demo
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- python3 offline_inference/prefix_caching.py
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- python3 offline_inference/llm_engine_example.py
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- 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
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- 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
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# https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
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- 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
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@ -718,6 +725,18 @@ steps:
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- uv pip install --system conch-triton-kernels
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- VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
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- label: LM Eval Small Models # 53min
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timeout_in_minutes: 75
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mirror_hardwares: [amdexperimental]
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agent_pool: mi325_1
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# grade: Blocking
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source_file_dependencies:
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- csrc/
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- vllm/model_executor/layers/quantization
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autorun_on_main: true
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commands:
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- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
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- label: OpenAI API correctness # 10min
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timeout_in_minutes: 15
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mirror_hardwares: [amdexperimental, amdproduction]
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@ -727,7 +746,7 @@ steps:
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- csrc/
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- vllm/entrypoints/openai/
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- vllm/model_executor/models/whisper.py
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commands: # LMEval
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commands: # LMEval+Transcription WER check
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# Transcription WER check is skipped because encoder-decoder models are not supported on ROCm, see https://github.com/vllm-project/vllm/issues/27442
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- pytest -s entrypoints/openai/correctness/
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@ -963,6 +982,19 @@ steps:
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- pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
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- 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
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- label: Multi-Modal Accuracy Eval (Small Models) # 150min - 180min
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timeout_in_minutes: 180
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mirror_hardwares: [amdexperimental, amdproduction]
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agent_pool: mi325_1
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# grade: Blocking
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working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
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source_file_dependencies:
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- vllm/multimodal/
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- vllm/inputs/
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- vllm/v1/core/
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commands:
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- pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
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- label: Multi-Modal Models Test (Extended) 1 # 60min
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timeout_in_minutes: 120
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mirror_hardwares: [amdexperimental]
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@ -1098,7 +1130,6 @@ steps:
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- vllm/model_executor/layers/layernorm.py
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- vllm/model_executor/layers/activation.py
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- vllm/model_executor/layers/quantization/input_quant_fp8.py
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- vllm/model_executor/layers/fused_moe/layer.py
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- tests/compile/test_fusion_attn.py
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- tests/compile/test_silu_mul_quant_fusion.py
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- tests/compile/distributed/test_fusion_all_reduce.py
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@ -1132,12 +1163,25 @@ steps:
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- vllm/model_executor/layers/activation.py
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- vllm/model_executor/layers/quantization/input_quant_fp8.py
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- tests/compile/distributed/test_fusions_e2e.py
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- tests/compile/fullgraph/test_full_graph.py
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commands:
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- nvidia-smi
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# Run all e2e fusion tests
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- pytest -v -s tests/compile/distributed/test_fusions_e2e.py
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- label: Blackwell GPT-OSS Eval
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timeout_in_minutes: 60
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working_dir: "/vllm-workspace/"
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gpu: b200
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optional: true # run on nightlies
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source_file_dependencies:
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- tests/evals/gpt_oss
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- vllm/model_executor/models/gpt_oss.py
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- vllm/model_executor/layers/quantization/mxfp4.py
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- vllm/v1/attention/backends/flashinfer.py
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commands:
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- uv pip install --system 'gpt-oss[eval]==0.0.5'
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- pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
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- label: Blackwell Quantized MoE Test
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timeout_in_minutes: 60
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working_dir: "/vllm-workspace/"
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@ -1155,6 +1199,16 @@ steps:
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commands:
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- pytest -s -v tests/quantization/test_blackwell_moe.py
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- label: Blackwell LM Eval Small Models
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timeout_in_minutes: 120
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gpu: b200
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optional: true # run on nightlies
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source_file_dependencies:
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- csrc/
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- vllm/model_executor/layers/quantization
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commands:
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- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt --tp-size=1
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##### 1 GPU test #####
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##### multi gpus test #####
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@ -1397,6 +1451,39 @@ steps:
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- TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
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- pytest -v -s -x lora/test_mixtral.py
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- label: LM Eval Large Models # optional
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gpu: a100
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optional: true
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mirror_hardwares: [amdexperimental]
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agent_pool: mi325_4
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# grade: Blocking
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num_gpus: 4
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working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
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source_file_dependencies:
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- csrc/
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- vllm/model_executor/layers/quantization
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commands:
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- export VLLM_WORKER_MULTIPROC_METHOD=spawn
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- pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
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##### H100 test #####
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- label: LM Eval Large Models (H100) # optional
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gpu: h100
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optional: true
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mirror_hardwares: [amdexperimental]
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agent_pool: mi325_4
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# grade: Blocking
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num_gpus: 4
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working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
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source_file_dependencies:
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- csrc/
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- vllm/model_executor/layers/quantization
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commands:
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- export VLLM_USE_DEEP_GEMM=0 # We found Triton is faster than DeepGEMM for H100
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- pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
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##### H200 test #####
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- label: Distributed Tests (H200) # optional
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mirror_hardwares: [amdexperimental]
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@ -1440,29 +1527,6 @@ steps:
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commands:
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- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
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- label: Blackwell LM Eval Small Models
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timeout_in_minutes: 120
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gpu: b200
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optional: true # run on nightlies
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source_file_dependencies:
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- csrc/
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- vllm/model_executor/layers/quantization
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commands:
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- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt --tp-size=1
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- label: Multi-Modal Accuracy Eval (Small Models) # 10min
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timeout_in_minutes: 70
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mirror_hardwares: [amdexperimental, amdproduction]
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agent_pool: mi325_1
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# grade: Blocking
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working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
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source_file_dependencies:
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- vllm/multimodal/
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- vllm/inputs/
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- vllm/v1/core/
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commands:
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- pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
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- label: LM Eval Large Models (4 Card)
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mirror_hardwares: [amdexperimental, amdproduction]
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agent_pool: mi325_4
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@ -1478,21 +1542,6 @@ steps:
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- export VLLM_WORKER_MULTIPROC_METHOD=spawn
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- pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
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- label: LM Eval Large Models (H100) # optional
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mirror_hardwares: [amdexperimental, amdproduction]
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agent_pool: mi325_4
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# grade: Blocking
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gpu: h100
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optional: true
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num_gpus: 4
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working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
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source_file_dependencies:
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- csrc/
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- vllm/model_executor/layers/quantization
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commands:
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- export VLLM_USE_DEEP_GEMM=0 # We found Triton is faster than DeepGEMM for H100
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- pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
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- label: ROCm LM Eval Large Models (8 Card)
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mirror_hardwares: [amdproduction]
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agent_pool: mi325_8
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@ -1517,6 +1566,20 @@ steps:
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- uv pip install --system 'gpt-oss[eval]==0.0.5'
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- VLLM_ROCM_USE_AITER_MHA=0 VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
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##### RL Integration Tests #####
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- label: Prime-RL Integration Test # 15min
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mirror_hardwares: [amdexperimental]
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agent_pool: mi325_2
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# grade: Blocking
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timeout_in_minutes: 30
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optional: true
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num_gpus: 2
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working_dir: "/vllm-workspace"
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source_file_dependencies:
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- vllm/
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- .buildkite/scripts/run-prime-rl-test.sh
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commands:
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- bash .buildkite/scripts/run-prime-rl-test.sh
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- label: DeepSeek V2-Lite Accuracy
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mirror_hardwares: [amdexperimental, amdproduction]
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agent_pool: mi325_4
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@ -1550,17 +1613,26 @@ steps:
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commands:
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- bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
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##### RL Integration Tests #####
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- label: Prime-RL Integration Test # 15min
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- label: DeepSeek V2-Lite Async EPLB Accuracy
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timeout_in_minutes: 60
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mirror_hardwares: [amdexperimental]
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agent_pool: mi325_2
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agent_pool: mi325_4
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# grade: Blocking
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timeout_in_minutes: 30
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gpu: h100
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optional: true
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num_gpus: 2
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num_gpus: 4
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working_dir: "/vllm-workspace"
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source_file_dependencies:
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- vllm/
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- .buildkite/scripts/run-prime-rl-test.sh
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commands:
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- bash .buildkite/scripts/run-prime-rl-test.sh
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- bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh 0.25 1319 8030
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- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy
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timeout_in_minutes: 60
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mirror_hardwares: [amdexperimental]
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agent_pool: mi325_4
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# grade: Blocking
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gpu: h100
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optional: true
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num_gpus: 4
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working_dir: "/vllm-workspace"
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commands:
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- bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040
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