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[CI/Build] Share nightly CUDA YAML between H100/L4 and B200 (#7028)

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Signed-off-by: wangyu <410167048@qq.com>
Signed-off-by: [Your Name] [Your Email]
Signed-off-by: [Your Name] <your.email@example.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>
This commit is contained in:
wangyuCursoramy-why-3459
 authored and GitHub committed 2026-09-14 14:27:10 +08:00
1 parent 58adeec05f
commit 64903b8e30
78 files changed
+437 -321
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.buildkite/common/ci_source_file_dependencies.yml
+6
View File
@@ -329,6 +329,7 @@ source_file_dependencies:
- *qwen_image
- tests/e2e/online_serving/test_qwen_image.py
- tests/e2e/online_serving/test_qwen_image_expansion.py
- tests/e2e/offline_inference/test_qwen_image_autoround_w4a16_expansion.py
diffusion_qwen_image_accuracy:
- *qwen_image
@@ -417,6 +418,11 @@ source_file_dependencies:
- tests/e2e/online_serving/test_z_image.py
- tests/e2e/online_serving/test_zimage_expansion.py
diffusion_sana_video_function:
- *sana_video
- tests/e2e/online_serving/test_sana_video_expansion.py
- tests/e2e/accuracy/sana_video/
diffusion_stable_audio_function:
- *stable_audio
- tests/e2e/offline_inference/test_stable_audio_expansion.py
.buildkite/cuda/test-nightly.yml
+58 -109
View File
@@ -35,23 +35,20 @@ steps:
source_file_dependencies: omni_minicpmo_4_5_duplex_function
timeout_in_minutes: 50
commands:
- pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni and cards_1" --run-level "full_model"
mirror_hardwares: h100_1
- pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and B200 and omni and cards_1" --run-level "full_model"
- label: ":full_moon: Omni · Doc Test with L4 · 4-GPU"
source_file_dependencies: omni_qwen2_5_omni_doc
timeout_in_minutes: 90
commands:
- export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
- pytest -s -v tests/examples/ -m "full_model and omni and L4 and cards_4" --run-level "full_model"
mirror_hardwares: l4_4
- pytest -s -v tests/examples/ -m "full_model and omni and L4 and B200 and cards_4" --run-level "full_model"
- label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
source_file_dependencies: omni_qwen3_omni_doc
timeout_in_minutes: 90
commands:
- pytest -s -v tests/examples/ -m "full_model and omni and H100 and cards_2" --run-level "full_model"
mirror_hardwares: h100_2
- pytest -s -v tests/examples/ -m "full_model and omni and H100 and B200 and cards_2" --run-level "full_model"
- label: ":full_moon: Omni · Accuracy Test"
source_file_dependencies: omni_qwen3_omni_accuracy
@@ -61,11 +58,10 @@ steps:
- export SEED_TTS_EVAL_DEVICE=cuda:1
- |
set +e
pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model" --run-level full_model
pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model and H100 and B200 and cards_2" --run-level full_model
EXIT=$$?
buildkite-agent artifact upload "tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json"
exit $$EXIT
mirror_hardwares: h100_2
- label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
source_file_dependencies: omni_minicpmo_4_5_accuracy
@@ -75,11 +71,10 @@ steps:
- export SEED_TTS_EVAL_DEVICE=cuda:1
- |
set +e
pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and cuda" --run-level full_model
pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and H100 and B200 and cuda and (cards_1 or cards_2)" --run-level full_model
EXIT=$$?
buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
exit $$EXIT
mirror_hardwares: h100_2
- label: ":full_moon: Omni · Perf Test · No Async Chunk"
source_file_dependencies: omni_qwen3_omni_perf
@@ -89,11 +84,10 @@ steps:
- export BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json -m "H100 and B200 and cards_2"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
exit $$EXIT
mirror_hardwares: h100_2
- label: ":full_moon: Omni · Perf Test · Async Chunk"
source_file_dependencies: omni_qwen3_omni_perf
@@ -103,11 +97,10 @@ steps:
- export BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json -m "H100 and full_model"
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json -m "H100 and B200 and full_model and cards_2"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
exit $$EXIT
mirror_hardwares: h100_2
- label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
source_file_dependencies: omni_minicpmo_4_5_perf
@@ -117,11 +110,10 @@ steps:
- export BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json -m "H100 and cards_1"
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json -m "H100 and B200 and cards_1"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
exit $$EXIT
mirror_hardwares: h100_1
- label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
source_file_dependencies: omni_minicpmo_4_5_duplex_perf
@@ -131,18 +123,16 @@ steps:
- export BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json -m "H100 and B200 and cards_1"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
exit $$EXIT
mirror_hardwares: h100_1
- label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
source_file_dependencies: omni_qwen3_omni_function
timeout_in_minutes: 45
commands:
- pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model" --run-level "core_model"
mirror_hardwares: h100_4
- pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model and H100 and B200 and cards_4" --run-level "core_model"
- group: ":card_index_dividers: TTS Model Test"
key: nightly-tts-test-group
@@ -157,8 +147,7 @@ steps:
- export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
- export VLLM_USE_DEEP_GEMM="0"
- export VLLM_MOE_USE_DEEP_GEMM="0"
- pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
mirror_hardwares: l4_1
- pytest -s -v tests/e2e/ -m "full_model and L4 and B200 and tts and cards_1" --run-level "full_model" --ignore=tests/e2e/accuracy
- label: ":full_moon: TTS · Perf Test · Single-GPU"
source_file_dependencies: tts_qwen3_tts_perf
@@ -169,11 +158,10 @@ steps:
- export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_1"
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and B200 and cards_1"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
exit $$EXIT
mirror_hardwares: h100_1
- label: ":full_moon: TTS · Perf Test · 2-GPU"
source_file_dependencies: tts_qwen3_tts_perf
@@ -184,11 +172,10 @@ steps:
- export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_2"
pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and B200 and cards_2"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
exit $$EXIT
mirror_hardwares: h100_2
- group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
key: nightly-tiny-model-test-group
@@ -209,16 +196,14 @@ steps:
# for the CI, since these tests should pass irrespective of whether or not we use
# the full weights or not.
commands:
- pytest -sv tests/model_tests/diffusion -m 'full_model and cuda and cards_2' --run-level "core_model"
mirror_hardwares: l4_2
- pytest -sv tests/model_tests/diffusion -m 'full_model and L4 and B200 and cuda and cards_2' --run-level "core_model"
- label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations) · 4-GPU"
source_file_dependencies: diffusion_tiny_model
timeout_in_minutes: 30
# CFG+TP extra_test_groups request 2×2=4 devices (see get_required_device_count).
commands:
- pytest -sv tests/model_tests/diffusion -m 'full_model and cuda and cards_4' --run-level "core_model"
mirror_hardwares: l4_4
- pytest -sv tests/model_tests/diffusion -m 'full_model and L4 and B200 and cuda and cards_4' --run-level "core_model"
- group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
key: nightly-diffusion-x2iat-group
@@ -235,9 +220,8 @@ steps:
- >-
pytest -sv
tests/e2e/online_serving/test_qwen_image_expansion.py
-m "full_model and diffusion and H100 and cards_1"
-m "full_model and diffusion and H100 and B200 and cards_1"
--run-level "full_model"
mirror_hardwares: h100_1
- label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · 2-GPU"
source_file_dependencies: diffusion_qwen_image_function
@@ -246,36 +230,29 @@ steps:
- >-
pytest -sv
tests/e2e/online_serving/test_qwen_image_expansion.py
-m "full_model and diffusion and H100 and cards_2"
-m "full_model and diffusion and H100 and B200 and cards_2"
--run-level "full_model"
mirror_hardwares: h100_2
- label: ":full_moon: Diffusion X2I(&A&T) · Z-Image Function Test · L4 · 4-GPU"
source_file_dependencies: diffusion_z_image_function
- label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4 · Single-GPU"
source_file_dependencies: diffusion_qwen_image_function
timeout_in_minutes: 120
commands:
- >-
pytest -sv
tests/e2e/online_serving/test_zimage_expansion.py
-m "full_model and diffusion and L4 and cards_4"
--run-level "full_model"
mirror_hardwares: l4_4
- pytest -sv tests/e2e/offline_inference/test_qwen_image_autoround_w4a16_expansion.py -m "full_model and diffusion and L4 and B200 and cards_1" --run-level "full_model"
- label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
source_file_dependencies: diffusion_text_to_image_doc
timeout_in_minutes: 60
commands:
- pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100" --run-level "full_model"
mirror_hardwares: h100_2
- pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100 and B200 and (cards_1 or cards_2)" --run-level "full_model"
- label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
source_file_dependencies: diffusion_qwen_image_accuracy
timeout_in_minutes: 180
commands:
- export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
- pytest -s -v tests/e2e/accuracy/test_qwen_image.py --run-level full_model
- pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
mirror_hardwares: h100_1
- pytest -s -v tests/e2e/accuracy/test_qwen_image.py -m "H100 and B200 and cards_1" --run-level full_model
- pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m "full_model and H100 and B200 and cards_1" --run-level full_model
- label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
source_file_dependencies: diffusion_hunyuan_image3_accuracy
@@ -283,8 +260,7 @@ steps:
commands:
- export HUNYUAN_IMAGE3_MODEL="tencent/HunyuanImage-3.0-Instruct"
- export HUNYUAN_IMAGE3_DEVICES="0,1,2,3"
- pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m full_model --run-level full_model
mirror_hardwares: h100_4
- pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m "full_model and H100 and B200 and cards_4" --run-level full_model
- label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · Single-GPU"
source_file_dependencies: diffusion_qwen_image_perf
@@ -295,12 +271,11 @@ steps:
- export CACHE_DIT_VERSION=1.5.0
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_1"
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and B200 and cards_1"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
exit $$EXIT
mirror_hardwares: h100_1
- label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · 4-GPU"
source_file_dependencies: diffusion_qwen_image_perf
@@ -308,16 +283,16 @@ steps:
timeout_in_minutes: 180
commands:
- export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
- export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
- export CACHE_DIT_VERSION=1.5.0
# Do not pin DIFFUSION_ATTENTION_BACKEND: H100 auto-selects FLASH_ATTN;
# B200 rejects explicit FLASH_ATTN without FA4 and uses CUDNN/TRTLLM.
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_4"
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and B200 and cards_4"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
exit $$EXIT
mirror_hardwares: h100_4
- group: ":card_index_dividers: Diffusion X2V Model Test"
key: nightly-diffusion-x2v-group
@@ -331,36 +306,31 @@ steps:
source_file_dependencies: diffusion_wan22_function
timeout_in_minutes: 90
commands:
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
mirror_hardwares: h100_1
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and B200 and cards_1" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
source_file_dependencies: diffusion_wan22_function
timeout_in_minutes: 90
commands:
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
mirror_hardwares: h100_2
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and B200 and cards_2" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
source_file_dependencies: diffusion_wan22_function
timeout_in_minutes: 90
commands:
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
mirror_hardwares: h100_1
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and B200 and cards_1" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
source_file_dependencies: diffusion_wan22_function
timeout_in_minutes: 90
commands:
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
mirror_hardwares: h100_2
- pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and B200 and cards_2" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
source_file_dependencies: diffusion_hunyuan_video_function
timeout_in_minutes: 90
commands:
- pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and cuda" --run-level "full_model"
mirror_hardwares: h100_2
- pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and H100 and B200 and cuda and (cards_1 or cards_2)" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · LingBot Function Test"
source_file_dependencies: diffusion_lingbot_function
@@ -371,23 +341,20 @@ steps:
tests/e2e/online_serving/test_lingbot_video.py
tests/e2e/online_serving/test_lingbot_video_moe.py
tests/e2e/offline_inference/test_lingbot_world_v2.py
-m "full_model and diffusion and H100 and cards_1"
-m "full_model and diffusion and H100 and B200 and cards_1"
--run-level "full_model"
mirror_hardwares: h100_1
- label: ":full_moon: Diffusion X2V · Doc Test"
source_file_dependencies: diffusion_image_to_video_doc
timeout_in_minutes: 60
commands:
- pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
mirror_hardwares: h100_1
- pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100 and B200 and cards_1" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · Wan2.2 Autoround Function Test"
source_file_dependencies: diffusion_wan22_function
timeout_in_minutes: 90
commands:
- pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
mirror_hardwares: h100_2
- pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and H100 and B200 and cuda and (cards_1 or cards_2)" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · Wan2.2 Device-Postprocess Equivalence Test"
source_file_dependencies: diffusion_wan22_function
@@ -395,31 +362,27 @@ steps:
commands:
- |
VLLM_OMNI_DEVICE_POSTPROCESS_MODEL=Wan-AI/Wan2.2-TI2V-5B-Diffusers \
pytest -s -v tests/e2e/offline_inference/test_device_postprocess_equivalence.py -m "full_model and cuda" --run-level "full_model"
mirror_hardwares: h100_1
pytest -s -v tests/e2e/offline_inference/test_device_postprocess_equivalence.py -m "full_model and H100 and B200 and cuda and cards_1" --run-level "full_model"
- label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
source_file_dependencies: diffusion_wan22_accuracy
timeout_in_minutes: 180
commands:
- pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
- pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
mirror_hardwares: h100_2
- pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m "full_model and H100 and B200 and (cards_1 or cards_2)" --run-level full_model
- pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m "full_model and H100 and B200 and cards_1" --run-level full_model
- label: ":full_moon: Diffusion X2V · MiniMax H3 I2VA/Ref2VA Accuracy Test"
source_file_dependencies: diffusion_minimax_h3_accuracy
timeout_in_minutes: 180
commands:
- pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m full_model --run-level full_model
mirror_hardwares: h100_4
- pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m "full_model and H100 and B200 and cards_4" --run-level full_model
- label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Accuracy Test"
source_file_dependencies: diffusion_hunyuan_video_accuracy
timeout_in_minutes: 180
commands:
- pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
- pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
mirror_hardwares: h100_2
- pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m "full_model and H100 and B200 and (cards_1 or cards_2)" --run-level full_model
- pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m "full_model and H100 and B200 and (cards_1 or cards_2)" --run-level full_model
- label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
source_file_dependencies: diffusion_wan22_perf
@@ -429,12 +392,11 @@ steps:
- export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_1"
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and B200 and cards_1"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
exit $$EXIT
mirror_hardwares: h100_1
- label: ":full_moon: Diffusion X2V · Perf Test · 2-GPU"
source_file_dependencies:
@@ -444,18 +406,16 @@ steps:
timeout_in_minutes: 180
commands:
- export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
- export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_2"
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and B200 and cards_2"
EXIT1=$$?
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and cards_2"
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and B200 and cards_2"
EXIT2=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
exit $$EXIT2
mirror_hardwares: h100_2
- label: ":full_moon: Diffusion X2V · Perf Test · HunyuanVideo-1.5"
source_file_dependencies: diffusion_hunyuan_video_perf
@@ -465,15 +425,14 @@ steps:
- export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json -m "H100 and B200 and (cards_1 or cards_2)"
EXIT1=$$?
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_i2v_vllm_omni.json
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_i2v_vllm_omni.json -m "H100 and B200 and (cards_1 or cards_2)"
EXIT2=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
exit $$EXIT2
mirror_hardwares: h100_2
- label: ":full_moon: Diffusion X2V · Perf Test · MiniMax-H3"
source_file_dependencies: diffusion_minimax_h3_perf
@@ -483,12 +442,11 @@ steps:
- export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
- |
set +e
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json
pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json -m "H100 and B200 and cards_4"
EXIT=$$?
buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
exit $$EXIT
mirror_hardwares: h100_4
- group: ":card_index_dividers: Diffusion Test"
key: nightly-diffusion-test-group
@@ -499,65 +457,56 @@ steps:
source_file_dependencies: diffusion_distributed_attention
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and cards_2' --run-level "full_model"
mirror_hardwares: l4_2
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and B200 and cards_2' --run-level "full_model"
- label: ":full_moon: Diffusion · Distributed Test with L4 · 3-GPU"
source_file_dependencies: diffusion_distributed_attention
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and cards_3' --run-level "full_model"
mirror_hardwares: l4_3
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and B200 and cards_3' --run-level "full_model"
- label: ":full_moon: Diffusion · Distributed Test with L4 · 4-GPU"
source_file_dependencies: diffusion_distributed_attention
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and cards_4' --run-level "full_model"
mirror_hardwares: l4_4
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and B200 and cards_4' --run-level "full_model"
- label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
source_file_dependencies: diffusion_distributed_attention
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_2' --run-level "full_model"
mirror_hardwares: h100_2
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and B200 and cards_2' --run-level "full_model"
- label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
source_file_dependencies: diffusion_distributed_attention
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_4' --run-level "full_model"
mirror_hardwares: h100_4
- pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and B200 and cards_4' --run-level "full_model"
- label: ":full_moon: Diffusion · Offloader Test"
source_file_dependencies: diffusion_offloader
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/offloader -m "full_model and cuda"
mirror_hardwares: l4_1
- pytest -sv tests/diffusion/offloader -m "full_model and L4 and B200 and cuda and cards_1"
- label: ":full_moon: Diffusion · Quantization Test with H100 · Single-GPU"
source_file_dependencies: diffusion_quantization
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
mirror_hardwares: h100_1
- pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100 and B200 and cards_1' --run-level "full_model"
- label: ":full_moon: Diffusion · MiniMax H3 FP8 Quality Test with H100 · 2-GPU"
source_file_dependencies: diffusion_minimax_h3_function
timeout_in_minutes: 60
commands:
- pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100' --run-level "full_model"
mirror_hardwares: h100_2
- pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100 and B200 and cards_2' --run-level "full_model"
- label: ":full_moon: Diffusion · Quantization Test with L4"
source_file_dependencies: diffusion_quantization
timeout_in_minutes: 60
commands:
- pip install "vllm-gguf-plugin==0.0.4"
- pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
mirror_hardwares: l4_1
- pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4 and B200 and cards_1' --run-level "full_model"
# NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
# Do not add new depends_on entries or extend this step.
.buildkite/cuda/test-weekly.yml
+13 -1
View File
@@ -339,11 +339,14 @@ steps:
mirror_hardwares: h100_1
- label: "Diffusion · SANA-Video · H100 · Single-GPU"
timeout_in_minutes: 90
source_file_dependencies: diffusion_sana_video_function
timeout_in_minutes: 180
commands:
- export SANA_VIDEO_GOLDEN_BASE_URL=https://vllm-public-assets.s3.us-west-2.amazonaws.com/omni-assets/sana-video/v2
- >-
pytest -sv
tests/e2e/online_serving/test_sana_video_expansion.py
tests/e2e/accuracy/sana_video/
-m "slow and diffusion and H100 and cards_1"
--run-level "full_model"
mirror_hardwares: h100_1
@@ -488,6 +491,15 @@ steps:
--run-level "full_model"
mirror_hardwares: h100_2
- label: "Diffusion · H100 · 3-GPU"
timeout_in_minutes: 120
commands:
- >-
pytest -sv
tests/e2e/ -m "slow and diffusion and H100 and cards_3"
--run-level "full_model"
mirror_hardwares: h100_3
- label: "Diffusion · H100 · 4-GPU"
timeout_in_minutes: 90
commands:
tests/buildkite/test_upload_pipeline.py
+47
View File
@@ -27,6 +27,7 @@ from upload_pipeline import ( # noqa: E402
pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
CUDA_BOOTSTRAP_STEPS = Path(".buildkite/cuda/bootstrap-upload-steps.yml")
NIGHTLY_YAML = Path(".buildkite/cuda/test-nightly.yml")
BOOTSTRAP_STEPS_TEMPLATE = """steps:
- key: image-build
- key: upload-ready-pipeline
@@ -368,6 +369,52 @@ def test_cpu_step_without_mirror_hardwares_is_unchanged() -> None:
assert _expand_mirror_hardwares(step) is step
def _leaf_steps(steps: list) -> list[dict]:
leaves: list[dict] = []
for step in steps:
if not isinstance(step, dict):
continue
nested = step.get("steps")
if nested is not None:
leaves.extend(_leaf_steps(nested))
else:
leaves.append(step)
return leaves
def test_nightly_yaml_infers_h100_l4_and_b200(monkeypatch: pytest.MonkeyPatch) -> None:
"""Nightly inferred jobs share card counts across H100/L4 and B200 mirrors."""
src = yaml.safe_load(NIGHTLY_YAML.read_text(encoding="utf-8"))
pytest_leaves = [step for step in _leaf_steps(src["steps"]) if "pytest" in str(step.get("commands"))]
inferred_leaves = [step for step in pytest_leaves if "mirror_hardwares" not in step]
pinned_leaves = [step for step in pytest_leaves if "mirror_hardwares" in step]
assert inferred_leaves
monkeypatch.setattr("upload_pipeline._get_mirror_hw_selector", lambda: "")
default_by_label = {
step["label"]: step for step in _leaf_steps(_render_test_pipeline(src, changed_files=None)["steps"])
}
monkeypatch.setattr("upload_pipeline._get_mirror_hw_selector", lambda: "b200")
b200_src = yaml.safe_load(NIGHTLY_YAML.read_text(encoding="utf-8"))
b200_by_label = {
step["label"]: step for step in _leaf_steps(_render_test_pipeline(b200_src, changed_files=None)["steps"])
}
pytest_labels = {step["label"] for step in inferred_leaves}
pinned_labels = {step["label"] for step in pinned_leaves}
assert pinned_labels <= set(default_by_label)
assert pinned_labels.isdisjoint(b200_by_label)
assert pytest_labels <= set(default_by_label)
assert pytest_labels <= set(b200_by_label)
for label in pytest_labels:
default_step = default_by_label[label]
b200_step = b200_by_label[label]
assert default_step["agents"]["queue"] in {"mithril-h100-pool", "l4-k8s"}
assert b200_step["agents"]["queue"] == "b200-k8s"
assert _gpu_limit(default_step) == _gpu_limit(b200_step)
@pytest.mark.parametrize(("raw", "expected"), [("", ""), (" ", ""), ("b200", "b200"), ("B200", "b200")])
def test_mirror_hw_selector_empty_or_b200(monkeypatch: pytest.MonkeyPatch, raw: str, expected: str) -> None:
monkeypatch.setenv("MIRROR_HW", raw)
tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+4 -4
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
@@ -63,7 +63,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
@@ -132,7 +132,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
@@ -200,7 +200,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
tests/dfx/perf/tests/test_hunyuanvideo15_i2v_vllm_omni.json
+2 -2
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 1
}
@@ -55,7 +55,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
+2 -2
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 1
}
@@ -54,7 +54,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
tests/dfx/perf/tests/test_minicpmo_4_5.json
+1 -1
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100",
"cuda": ["H100", "B200"],
"npu": "A3"
},
"num_cards": 1
tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
+1 -1
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100",
"cuda": ["H100", "B200"],
"npu": "A3"
},
"num_cards": 1
tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json
+4 -4
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 4
}
@@ -76,7 +76,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 4
}
@@ -148,7 +148,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 4
}
@@ -219,7 +219,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 4
}
tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+2 -2
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100",
"cuda": ["H100", "B200"],
"npu": "A3"
},
"num_cards": 2
@@ -242,7 +242,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+1 -1
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100",
"cuda": ["H100", "B200"],
"npu": "A3"
},
"num_cards": 2
tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+4 -4
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 1
}
@@ -66,7 +66,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 1
}
@@ -150,7 +150,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 4
}
@@ -197,7 +197,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 4
}
tests/dfx/perf/tests/test_tts.json
+4 -4
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100",
"cuda": ["H100", "B200"],
"npu": "A3"
},
"num_cards": 2
@@ -121,7 +121,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
@@ -285,7 +285,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 2
}
@@ -416,7 +416,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 1
}
tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+2 -2
View File
@@ -5,7 +5,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100"
"cuda": ["H100", "B200"]
},
"num_cards": 1
}
@@ -57,7 +57,7 @@
{
"hardware_marks": {
"res": {
"cuda": "H100",
"cuda": ["H100", "B200"],
"npu": "A3"
},
"num_cards": 2
tests/diffusion/distributed/test_cfg_parallel.py
+4 -4
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""Tests for CFG (Classifier-Free Guidance) parallel functionality.
CPU tests mock the CFG process group and validate mixin logic without GPUs.
@@ -32,8 +32,8 @@ from vllm_omni.diffusion.distributed.parallel_state import (
)
from vllm_omni.platforms import current_omni_platform
_L4_TWO_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=2)
_L4_THREE_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=3)
_L4_TWO_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=2)
_L4_THREE_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=3)
def _set_random_seeds(seed: int) -> None:
@@ -1009,7 +1009,7 @@ def _run_multi_branch_gpu_parity(
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "L4"}, num_cards=2)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=2)
@pytest.mark.parametrize("batch_size", [2])
@pytest.mark.parametrize("cfg_normalize", [False, True])
@pytest.mark.parametrize("extra_kwargs", [None, {"step_i": 1}])
tests/diffusion/distributed/test_comm.py
+3 -3
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""Tests for SeqAllToAll4D, SeqAllToAll5D, and RingComm communication primitives.
CPU tests use gloo on CPU tensors (no GPU). Nightly parity tests run the same
@@ -24,8 +24,8 @@ from vllm_omni.diffusion.distributed.parallel_state import (
)
from vllm_omni.platforms import current_omni_platform
_L4_TWO_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=2)
_L4_FOUR_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=4)
_L4_TWO_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=2)
_L4_FOUR_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=4)
DeviceKind = Literal["cpu", "cuda"]
tests/diffusion/distributed/test_expert_parallel.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
System test for Expert Parallel (EP).
tests/diffusion/distributed/test_flux2_vae_patch_parallel.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""FLUX.2-dev VAE patch-parallel correctness tests.
tests/diffusion/distributed/test_pipeline_parallel.py
+3 -3
View File
@@ -31,9 +31,9 @@ from vllm_omni.diffusion.distributed.parallel_state import (
from vllm_omni.diffusion.distributed.pipeline_parallel import AsyncLatents, PipelineParallelMixin
from vllm_omni.platforms import current_omni_platform
_L4_TWO_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=2)
_L4_THREE_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=3)
_L4_FOUR_GPU = hardware_marks(res={"cuda": "L4"}, num_cards=4)
_L4_TWO_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=2)
_L4_THREE_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=3)
_L4_FOUR_GPU = hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=4)
DeviceKind = Literal["cpu", "cuda"]
_UNIT_MARKS = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
tests/diffusion/distributed/test_sana_video_sp_numeric.py
+6 -6
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""Real multi-process numeric equivalence gate for SANA-Video sequence parallel.
Runs the native transformer directly (no pipeline, no topology validation) with
@@ -273,7 +273,7 @@ def _baseline_worker(
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "L4"}, num_cards=2)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=2)
@pytest.mark.parametrize("task", ["t2v", "i2v"])
@pytest.mark.parametrize("num_frames", [21, 5])
def test_sp2_matches_sp1_fp32(task: str, num_frames: int) -> None:
@@ -290,7 +290,7 @@ def test_sp2_matches_sp1_fp32(task: str, num_frames: int) -> None:
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "L4"}, num_cards=4)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=4)
@pytest.mark.parametrize("task", ["t2v", "i2v"])
@pytest.mark.parametrize("num_frames", [21, 5])
def test_sp4_matches_sp1_fp32(task: str, num_frames: int) -> None:
@@ -307,7 +307,7 @@ def test_sp4_matches_sp1_fp32(task: str, num_frames: int) -> None:
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "L4"}, num_cards=4)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=4)
def test_sp2_cfg2_matches_sp1_fp32() -> None:
"""Four processes: CFG branches with distinct inputs each run their own SP
group; catches CFG/SP group crossover and deadlocks."""
@@ -324,7 +324,7 @@ def test_sp2_cfg2_matches_sp1_fp32() -> None:
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "L4"}, num_cards=4)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=4)
def test_tp2_sp2_matches_sp1_fp32() -> None:
"""Four processes: TP shards heads and channels while SP shards frames; the
combined mesh must still reproduce the serial output."""
@@ -342,7 +342,7 @@ def test_tp2_sp2_matches_sp1_fp32() -> None:
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "L4"}, num_cards=2)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=2)
def test_sp2_bf16_drift_informational() -> None:
"""Records the bf16 reduction-reorder drift magnitude; the load-bearing
numeric gate is the fp32 suite above."""
tests/diffusion/distributed/test_sequence_parallel.py
+1 -1
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@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
System test for Sequence Parallel (SP) backends: Ulysses and Ring attention.
tests/diffusion/distributed/test_vae_decode_parallelism.py
+2 -2
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""VAE patch-parallel decode parity tests for diffusion models.
@@ -145,7 +145,7 @@ def _run_generate(
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "H100"}, num_cards={"cuda": 4})
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards={"cuda": 4})
@pytest.mark.parametrize("model_case", MODEL_CASES)
def test_vae_patch_parallel_tp2(model_case: dict[str, Any], tmp_path: Path):
if current_omni_platform.is_npu():
tests/diffusion/distributed/test_wan_spatial_shard.py
+4 -1
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
import os
from types import SimpleNamespace
@@ -538,7 +541,7 @@ def _spatial_shard_decode_worker(rank: int, split_dim: str, return_dict, master_
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@hardware_test(res={"cuda": "H100"}, num_cards=_SPATIAL_SHARD_WORLD_SIZE)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=_SPATIAL_SHARD_WORLD_SIZE)
@pytest.mark.parametrize("split_dim", ["height", "width"])
def test_spatial_shard_decode_matches_reference(split_dim: str):
manager = mp.get_context("spawn").Manager()
tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py
+1 -1
View File
@@ -175,7 +175,7 @@ def _generate_joint_output(omni, config: QualityTestConfig):
pytest.param(
_QUALITY_CONFIG,
id=_QUALITY_CONFIG.id,
marks=hardware_marks(res={"cuda": _QUALITY_CONFIG.gpu}, num_cards=2),
marks=hardware_marks(res={"cuda": ["H100", "B200"]}, num_cards=2),
)
],
)
tests/diffusion/offloader/test_diffusion_cpu_offload.py
+5 -2
View File
@@ -9,7 +9,7 @@ import pytest
import torch
from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
from tests.helpers.mark import hardware_test
from tests.helpers.mark import hardware_marks, hardware_test
from tests.helpers.monitor import DeviceMemoryMonitor
from tests.helpers.runtime import OmniRunner
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -27,7 +27,10 @@ MODELS: dict[str, dict[str, int | None]] = {**AUDIO_MODEL, **IMAGE_MODELS}
MODEL_MARKS = {
"riverclouds/qwen_image_random": pytest.mark.core_model,
"stabilityai/stable-audio-open-1.0": pytest.mark.full_model,
"stabilityai/stable-audio-open-1.0": [
pytest.mark.full_model,
*hardware_marks(res={"cuda": ["L4", "B200"], "rocm": "MI325"}),
],
}
_GATED_MODELS = {"stabilityai/stable-audio-open-1.0"}
tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+9 -4
View File
@@ -12,7 +12,7 @@ import pytest
import torch
from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
from tests.helpers.mark import hardware_test
from tests.helpers.mark import hardware_marks
from tests.helpers.monitor import DeviceMemoryMonitor
from tests.helpers.runtime import OmniRunner
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -34,8 +34,14 @@ IMAGE_VIDEO_MODELS: dict[str, dict[str, int | None]] = {
MODELS: dict[str, dict[str, int | None]] = {**AUDIO_MODEL, **IMAGE_VIDEO_MODELS}
MODEL_MARKS = {
"riverclouds/qwen_image_random": pytest.mark.core_model,
"stabilityai/stable-audio-open-1.0": pytest.mark.full_model,
"riverclouds/qwen_image_random": [
pytest.mark.core_model,
*hardware_marks(res={"cuda": "L4", "rocm": "MI325"}),
],
"stabilityai/stable-audio-open-1.0": [
pytest.mark.full_model,
*hardware_marks(res={"cuda": ["L4", "B200"], "rocm": "MI325"}),
],
}
AUDIO_MODEL_PARAMS: dict[str, dict[str, Any]] = {
@@ -309,7 +315,6 @@ def test_stable_audio_requires_both_outputs(audio_no_offload, audio_offload, mes
@pytest.mark.diffusion
@hardware_test(res={"cuda": "L4", "rocm": "MI325"})
@pytest.mark.parametrize(
"model_name",
[pytest.param(name, marks=MODEL_MARKS[name]) for name in MODELS],
tests/diffusion/quantization/test_gguf_diffusion.py
+1 -1
View File
@@ -109,7 +109,7 @@ def _generate_single_stage_image(
return images, peak_mem
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parametrize("model", [Z_IMAGE_CONFIG, FLUX_CONFIG], ids=["Z-Image-Turbo", "FLUX.2-klein"])
tests/diffusion/quantization/test_quantization_fp8.py
+12 -12
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
End-to-end tests for the unified quantization framework (PR #1764).
@@ -270,7 +270,7 @@ def _generate_bagel_image(
# ─── Single-stage diffusion model tests ──────────────────────────────────────
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_single_stage_zimage_fp8():
"""Z-Image-Turbo with FP8 generates valid images."""
images, _ = _generate_single_stage_image(
@@ -281,7 +281,7 @@ def test_single_stage_zimage_fp8():
images[0].save("test_zimage_fp8.png")
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_single_stage_zimage_fp8_uses_less_memory():
"""FP8 should use less peak memory than BF16 for Z-Image-Turbo."""
_, mem_bf16 = _generate_single_stage_image(
@@ -300,7 +300,7 @@ def test_single_stage_zimage_fp8_uses_less_memory():
assert mem_fp8 < mem_bf16, f"FP8 ({mem_fp8:.2f} GiB) should use less memory than BF16 ({mem_bf16:.2f} GiB)"
@hardware_test(res={"cuda": "L4", "xpu": "B60"})
@hardware_test(res={"cuda": ["L4", "B200"], "xpu": "B60"})
@pytest.mark.advanced_model
def test_single_stage_qwen_image_fp8():
"""Qwen-Image (random weights) with FP8 generates valid images."""
@@ -316,7 +316,7 @@ def test_single_stage_qwen_image_fp8():
images[0].save("test_qwen_image_fp8.png")
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.skip(reason="This model is not authorized on Hugging Face Hub yet")
def test_single_stage_flux_fp8():
"""FLUX.1-dev with FP8 generates valid images."""
@@ -331,7 +331,7 @@ def test_single_stage_flux_fp8():
images[0].save("test_flux_fp8.png")
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.skip(reason="This model is not authorized on Hugging Face Hub yet")
def test_single_stage_flux_fp8_uses_less_memory():
"""FP8 should use less peak memory than BF16 for FLUX.1-dev."""
@@ -357,7 +357,7 @@ def test_single_stage_flux_fp8_uses_less_memory():
assert mem_fp8 < mem_bf16, f"FP8 ({mem_fp8:.2f} GiB) should use less memory than BF16 ({mem_bf16:.2f} GiB)"
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
def test_single_stage_ltx2_fp8_uses_less_memory():
"""FP8 should use less peak memory than BF16 for LTX-2."""
_, mem_bf16 = _generate_single_stage_video(
@@ -379,7 +379,7 @@ def test_single_stage_ltx2_fp8_uses_less_memory():
# ─── Multi-stage model tests (BAGEL) ─────────────────────────────────────────
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
def test_bagel_fp8_generates_image():
"""BAGEL with diffusion-stage FP8 generates a valid image.
@@ -392,7 +392,7 @@ def test_bagel_fp8_generates_image():
image.save("test_bagel_fp8.png")
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
def test_bagel_bf16_generates_image():
"""BAGEL without quantization generates a valid image (baseline)."""
image, _ = _generate_bagel_image(diffusion_quantization_config=None)
@@ -402,7 +402,7 @@ def test_bagel_bf16_generates_image():
# ─── Quantization config routing tests ────────────────────────────────────────
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_quantization_key_maps_to_quantization_config():
"""The old 'quantization' kwarg should map to 'quantization_config'
in OmniDiffusionConfig.from_kwargs for backwards compatibility."""
@@ -413,7 +413,7 @@ def test_quantization_key_maps_to_quantization_config():
assert config.quantization_config.get_name() == "fp8"
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_quantization_config_key_takes_priority():
"""When both 'quantization' and 'quantization_config' are set,
'quantization_config' takes priority."""
@@ -428,7 +428,7 @@ def test_quantization_config_key_takes_priority():
assert config.quantization_config.activation_scheme == "static"
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_single_stage_quantization_config_key():
"""Single-stage model using quantization_config (dict) key generates images."""
images, _ = _generate_single_stage_image(
tests/diffusion/quantization/test_quantization_quality.py
+9 -8
View File
@@ -45,6 +45,7 @@ import pytest
import torch
from PIL import Image
from tests.e2e.accuracy.helpers import resolve_device_threshold
from tests.helpers.mark import hardware_marks
_REPO_ROOT = Path(__file__).resolve().parents[3]
@@ -70,7 +71,7 @@ class QualityTestConfig:
id: str # pytest ID, e.g. "fp8_z_image"
task: str # "t2i" or "t2v"
prompt: str # generation prompt
max_lpips: float # fail threshold — higher = more lenient
max_lpips: float | dict[str, float] # threshold, or {"H100": 0.15, "B200": 0.17}
model: str | None = None # HF model name
quantization: str | dict[str, object] | None = None # quantization method/config, e.g. "fp8"
baseline_model: str | None = None # explicit BF16/local baseline path
@@ -80,7 +81,6 @@ class QualityTestConfig:
num_inference_steps: int = 20 # keep low for CI speed
num_frames: int = 5 # only for t2v
seed: int = 42
gpu: str = "H100" # minimum GPU requirement
negative_prompt: str | None = ""
guidance_scale: float | None = None
sigmas: list[float] | None = None
@@ -190,7 +190,7 @@ QUALITY_CONFIGS = [
quantization={"text_encoder": "fp8", "transformer": None, "vae": None},
task="t2i",
prompt="a cup of coffee on a wooden table, morning light",
max_lpips=0.15,
max_lpips={"H100": 0.15, "B200": 0.17},
num_inference_steps=10,
enable_cpu_offload=True,
height=1024,
@@ -528,7 +528,7 @@ def test_ltx_quality_gate_uses_official_eager_defaults(monkeypatch):
assert captured.sampling.guidance_scale is None
_marks = hardware_marks(res={"cuda": "H100"})
_marks = hardware_marks(res={"cuda": ["H100", "B200"]})
_OUTPUT_DIR = Path(os.environ["VLLM_OMNI_QUALITY_OUTPUT_DIR"]) if "VLLM_OMNI_QUALITY_OUTPUT_DIR" in os.environ else None
@@ -588,8 +588,9 @@ def test_quantization_quality(config: QualityTestConfig):
# --- Similarity metrics ---
lpips_score = _compute_lpips(baseline_out, quant_out, config.task)
psnr_score, mae_score = _compute_psnr_and_mae(baseline_out, quant_out, config.task)
assert lpips_score <= config.max_lpips, (
f"LPIPS {lpips_score:.4f} exceeds threshold {config.max_lpips} "
gpu_key, max_lpips = resolve_device_threshold(config.max_lpips, label=f"{config.id} max_lpips")
assert lpips_score <= max_lpips, (
f"LPIPS {lpips_score:.4f} exceeds threshold {max_lpips} ({gpu_key}) "
f"for {config.quantization_ref() or 'pre-quantized checkpoint'} on {config.quantized_ref()}"
)
@@ -601,12 +602,12 @@ def test_quantization_quality(config: QualityTestConfig):
print(f" Baseline: {config.baseline_ref()}")
print(f" Quantized: {config.quantized_ref()}")
print(f" Method: {config.quantization_ref() or 'pre-quantized checkpoint'}")
print(f" LPIPS: {lpips_score:.4f} (threshold: {config.max_lpips})")
print(f" LPIPS: {lpips_score:.4f} (threshold: {max_lpips}, gpu: {gpu_key})")
print(f" PSNR: {psnr_score:.4f} dB (higher is better)")
print(f" MAE: {mae_score:.6f} (lower is better)")
print(f" BF16 memory: {bl_mem:.2f} GiB")
print(f" Quant memory: {qt_mem:.2f} GiB ({mem_reduction:.0f}% reduction)")
print(f" Result: {'PASS' if lpips_score <= config.max_lpips else 'FAIL'}")
print(f" Result: {'PASS' if lpips_score <= max_lpips else 'FAIL'}")
print(f"{'=' * 60}\n")
assert np.isfinite(psnr_score) or np.isinf(psnr_score), f"PSNR is invalid for {config.id}: {psnr_score}"
tests/e2e/accuracy/helpers.py
+26
View File
@@ -135,6 +135,32 @@ def resolve_similarity_thresholds(
return table.get(profile, table[DEFAULT_DEVICE_PROFILE])
def resolve_device_threshold(
thresholds: float | int | Mapping[str, float],
*,
device_name: str | None = None,
label: str = "threshold",
) -> tuple[str, float]:
"""Resolve a scalar or per-GPU threshold for the current CUDA device.
Returns ``(profile_key, threshold)``. A bare number uses profile ``default``.
A mapping matches keys as substrings of the device name via
``resolve_device_profile`` (for example ``{"H100": 0.3, "B200": 0.36}``).
Prefer an explicit ``default`` entry when unmatched GPUs should fall back;
otherwise unmatched devices raise.
"""
if isinstance(thresholds, float | int):
return DEFAULT_DEVICE_PROFILE, float(thresholds)
profile = resolve_device_profile(device_name, profiles=thresholds)
if profile in thresholds:
return profile, float(thresholds[profile])
if DEFAULT_DEVICE_PROFILE in thresholds:
return DEFAULT_DEVICE_PROFILE, float(thresholds[DEFAULT_DEVICE_PROFILE])
known = ", ".join(str(key) for key in thresholds)
raise RuntimeError(f"no {label} entry for device profile {profile!r}; known: {known}")
_SSIM_RE = re.compile(r"All:(?P<score>[0-9.]+)")
_PSNR_RE = re.compile(r"average:(?P<score>[0-9.]+)")
tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py
+3 -3
View File
@@ -183,7 +183,7 @@ def _generate_online_video(
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_hunyuanvideo15_i2v_diffusers_offline_generates_video(
hunyuanvideo15_i2v_image_source: str | None,
) -> None:
@@ -203,7 +203,7 @@ def test_hunyuanvideo15_i2v_diffusers_offline_generates_video(
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", SERVER_CASES, indirect=True)
def test_hunyuanvideo15_i2v_online_serving_generates_video(
omni_server,
@@ -229,7 +229,7 @@ def test_hunyuanvideo15_i2v_online_serving_generates_video(
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_hunyuanvideo15_i2v_serving_matches_offline_video_similarity(
hunyuanvideo15_i2v_image_source: str | None,
) -> None:
tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py
+3 -3
View File
@@ -144,7 +144,7 @@ def _generate_online_video(*, omni_server, online_client, timeout_seconds: int)
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_hunyuanvideo15_t2v_diffusers_offline_generates_video() -> None:
if not torch.cuda.is_available():
pytest.skip("HunyuanVideo-1.5 T2V offline accuracy test requires CUDA.")
@@ -160,7 +160,7 @@ def test_hunyuanvideo15_t2v_diffusers_offline_generates_video() -> None:
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", SERVER_CASES, indirect=True)
def test_hunyuanvideo15_t2v_online_serving_generates_video(
omni_server,
@@ -182,7 +182,7 @@ def test_hunyuanvideo15_t2v_online_serving_generates_video(
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_hunyuanvideo15_t2v_serving_matches_offline_video_similarity() -> None:
if not torch.cuda.is_available():
pytest.skip("HunyuanVideo-1.5 T2V video similarity test requires CUDA.")
tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py
+3 -3
View File
@@ -141,7 +141,7 @@ def _inline_daily_omni_media(monkeypatch: pytest.MonkeyPatch) -> None:
@requires_daily_omni_deps
@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"], "npu": "A3"}, num_cards=1)
@pytest.mark.parametrize("omni_server", daily_test_params, indirect=True)
def test_minicpmo_4_5_daily_omni_accuracy_bench(omni_server) -> None:
_require_vllm_cli()
@@ -174,7 +174,7 @@ def test_minicpmo_4_5_daily_omni_accuracy_bench(omni_server) -> None:
assert _acc_bench.run_acc_benchmark(_acc_bench.parse_acc_benchmark_args(argv)) == 0
@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"], "npu": "A3"}, num_cards=1)
@pytest.mark.parametrize("omni_server", seed_test_params, indirect=True)
def test_minicpmo_4_5_seed_tts_wer_bench(omni_server) -> None:
_require_vllm_cli()
@@ -200,7 +200,7 @@ def test_minicpmo_4_5_seed_tts_wer_bench(omni_server) -> None:
assert _acc_bench.run_acc_benchmark(_acc_bench.parse_acc_benchmark_args(argv)) == 0
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", duplex_accuracy_test_params, indirect=True)
def test_minicpmo_4_5_duplex_seed_tts_wer_bench(omni_server) -> None:
"""Gate Seed-TTS WER through the explicit Realtime TTS contract."""
tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py
+2 -4
View File
@@ -138,8 +138,6 @@ def _server_args() -> list[str]:
"--vae-parallel-mode",
"tile",
"--vae-use-tiling",
"--diffusion-attention-backend",
"FLASH_ATTN",
"--stage-init-timeout",
"1800",
"--init-timeout",
@@ -228,7 +226,7 @@ def _assert_official_video(
)
@hardware_test(res={"cuda": "H100"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=4)
def test_minimax_h3_i2va_matches_official_reference(
accuracy_artifact_root: Path,
) -> None:
@@ -304,7 +302,7 @@ def test_minimax_h3_i2va_matches_official_reference(
)
@hardware_test(res={"cuda": "H100"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=4)
def test_minimax_h3_ref2va_matches_official_reference(
accuracy_artifact_root: Path,
) -> None:
tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py
+2 -2
View File
@@ -110,7 +110,7 @@ def _daily_omni_hub_inline_media(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_qwen3_omni_daily_omni_accuracy_bench(omni_server) -> None:
_require_vllm_cli()
@@ -122,7 +122,7 @@ def test_qwen3_omni_daily_omni_accuracy_bench(omni_server) -> None:
assert _acc_bench.run_acc_benchmark(ns) == 0
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_qwen3_omni_seed_tts_wer_bench(omni_server) -> None:
_require_vllm_cli()
tests/e2e/accuracy/sana_video/test_sana_video_golden.py
+3 -2
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from __future__ import annotations
@@ -44,7 +44,7 @@ SSIM_THRESHOLDS = {
}
pytestmark = [
pytest.mark.full_model,
pytest.mark.slow,
pytest.mark.diffusion,
pytest.mark.skipif(
not GOLDEN_BASE_URL,
@@ -169,6 +169,7 @@ def _validate_metadata(payload: Any, manifest: dict[str, Any], variant: str, tas
def _download_case(variant: str, task: str, output_dir: Path, filenames: set[str]) -> dict[str, Any]:
output_dir.mkdir(parents=True, exist_ok=True)
assert GOLDEN_BASE_URL is not None
base_url = f"{GOLDEN_BASE_URL.rstrip('/')}/{variant}/{task}"
response = requests.get(f"{base_url}/manifest.json", timeout=60)
response.raise_for_status()
tests/e2e/accuracy/sana_video/test_sana_video_i2v_alignment.py
+2 -2
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from __future__ import annotations
@@ -11,7 +11,7 @@ import torch
from tests.helpers.mark import hardware_marks
pytestmark = [
pytest.mark.full_model,
pytest.mark.slow,
pytest.mark.diffusion,
pytest.mark.benchmark,
]
tests/e2e/accuracy/sana_video/test_sana_video_pipeline_alignment.py
+20 -4
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from __future__ import annotations
@@ -7,7 +7,7 @@ import gc
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from typing import Any, TypedDict
import pytest
import torch
@@ -16,7 +16,7 @@ import torch.nn.functional as F
from tests.helpers.mark import hardware_marks
pytestmark = [
pytest.mark.full_model,
pytest.mark.slow,
pytest.mark.diffusion,
pytest.mark.benchmark,
]
@@ -30,7 +30,23 @@ HEIGHT = 192
WIDTH = 320
NUM_FRAMES = 9
VARIANTS = {
class _VariantConfig(TypedDict):
model: str
revision: str
model_env: str
max_abs: float
relative_l2: float
cosine: float
cfg_max_abs: float
cfg_relative_l2: float
cfg_cosine: float
step_max_abs: float
step_relative_l2: float
step_cosine: float
VARIANTS: dict[str, _VariantConfig] = {
"480p": {
"model": "Efficient-Large-Model/SANA-Video_2B_480p_diffusers",
"revision": "fed3bce411c58a0f688a31afe8f52e61acc2b15f",
tests/e2e/accuracy/test_cosmos3_similarity.py
+4 -3
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from __future__ import annotations
@@ -35,6 +35,7 @@ def _model_name() -> str:
pytest.skip(f"Set {MODEL_ENV_VAR} to run Cosmos3 full-model smoke tests.")
if not torch.cuda.is_available():
pytest.skip("Cosmos3 full-model smoke tests require CUDA.")
assert model
return model
@@ -56,7 +57,7 @@ def _image_data_url(image: Image.Image) -> str:
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_cosmos3_t2i_serving_smoke(accuracy_artifact_root: Path) -> None:
output_dir = model_output_dir(accuracy_artifact_root, MODEL_ID)
with OmniServer(_model_name(), _server_args(), use_omni=True) as server:
@@ -97,7 +98,7 @@ def test_cosmos3_t2i_serving_smoke(accuracy_artifact_root: Path) -> None:
],
)
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_cosmos3_video_serving_smoke(
accuracy_artifact_root: Path,
name: str,
tests/e2e/accuracy/test_diffusers_backend_similarity.py
+27 -12
View File
@@ -29,6 +29,7 @@ from tests.e2e.accuracy.helpers import (
assert_similarity,
env_to_apply_ftfy_mock_in_subproc,
model_output_dir,
resolve_device_threshold,
)
from tests.e2e.accuracy.helpers import (
parse_psnr_score as _parse_psnr_score,
@@ -49,16 +50,25 @@ from vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter imp
pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
def _set_matched_attention_backend(pipe: DiffusionPipeline) -> None:
"""Walk the same backend chain the omni server's diffusers adapter walks.
def _flash_attempt_backends() -> list[str]:
"""Backends in ``CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS`` that can actually launch.
The server side (--diffusion-load-format diffusers) resolves its attention
backend through the adapter's preference chain, skipping backends that are
unavailable on the image (e.g. the FA3 hub kernel has no build variant for
the image's torch — build 2953). The reference run must resolve to the
same backend or the similarity comparison measures kernel differences.
Those names are Hopper FA2/FA3. ``set_attention_backend`` only checks import,
so a newer GPU still "selects" them and then dies with "no kernel image".
Do not probe by launching: a missing cubin can leave the CUDA context dead.
Capability >= 10 is outside that wheel, including future SKUs in that family.
"""
for backend in CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS:
if not torch.cuda.is_available():
return []
major, _ = torch.cuda.get_device_capability()
if major >= 10:
return []
return list(CUDA_FLASH_ATTENTION_BACKEND_ATTEMPTS)
def _set_matched_attention_backend(pipe: DiffusionPipeline) -> None:
"""Pick a Diffusers attention backend the reference run can actually execute."""
for backend in _flash_attempt_backends():
try:
pipe.transformer.set_attention_backend(backend)
return
@@ -297,7 +307,7 @@ def _run_diffusers_qwen_image(*, model: str, output_path: Path) -> tuple[Image.I
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize("model_id", ["Qwen/Qwen-Image"])
def test_diffusers_backend_t2i_matches_diffusers(model_id: str, accuracy_artifact_root: Path) -> None:
output_dir = model_output_dir(accuracy_artifact_root, model_id + "-diffusers-backend")
@@ -336,7 +346,7 @@ def test_diffusers_backend_t2i_matches_diffusers(model_id: str, accuracy_artifac
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize(
"model_id",
[
@@ -363,7 +373,11 @@ def test_diffusers_backend_i2v_matches_diffusers(
model=model_id, output_path=diffusers_path, conditioning_image=resized_image
)
diffusers_latency = diffusers_latency * 1000
latency_threshold_factor = 0.3
# H100 keeps the historical 30% slack. B200 measured ~35.8% (6008 vs 4425 ms).
gpu_key, latency_threshold_factor = resolve_device_threshold(
{"H100": 0.3, "B200": 0.36},
label="latency threshold factor",
)
latency_threshold = diffusers_latency * (1 + latency_threshold_factor)
ssim_output = _run_ffmpeg_similarity("ssim", vllm_path, diffusers_path)
@@ -372,7 +386,8 @@ def test_diffusers_backend_i2v_matches_diffusers(
psnr_score = _parse_psnr_score(psnr_output)
print(f"{model_id} latency metrics:")
print(
f" Latency={vllm_latency:.2f}ms, threshold<={latency_threshold:.2f}ms, diffusers latency={diffusers_latency:.2f}ms, lower is better"
f" Latency={vllm_latency:.2f}ms, threshold<={latency_threshold:.2f}ms, "
f"diffusers latency={diffusers_latency:.2f}ms, slack={latency_threshold_factor:.0%} ({gpu_key}), lower is better"
)
print(f"{model_id} similarity metrics:")
print(f" SSIM: value={ssim_score:.6f}, threshold>={VIDEO_SSIM_THRESHOLD:.6f}, range=[-1, 1], higher is better")
tests/e2e/accuracy/test_hunyuan_image3.py
+7 -6
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from __future__ import annotations
@@ -12,6 +12,7 @@ import tempfile
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Any, cast
import pytest
import requests
@@ -288,7 +289,7 @@ def _quant_accuracy_cases() -> list[pytest.ParameterSet]:
def _make_config(enable_kv_reuse: bool, path: Path) -> None:
config = copy.deepcopy(_DEPLOY_CONFIG)
config = cast(dict[str, Any], copy.deepcopy(_DEPLOY_CONFIG))
config["stages"][0]["omni_kv_config"] = {"need_send_cache": enable_kv_reuse}
config["stages"][1]["omni_kv_config"] = {"need_recv_cache": enable_kv_reuse}
path.write_text(yaml.dump(config, default_flow_style=False, sort_keys=False))
@@ -303,7 +304,7 @@ def _quant_tensor_parallel_size() -> int:
def _make_quant_dit_config(path: Path) -> None:
config = copy.deepcopy(_QUANT_DIT_CONFIG)
config = cast(dict[str, Any], copy.deepcopy(_QUANT_DIT_CONFIG))
config["stages"][0]["devices"] = _quant_devices()
config["stages"][0]["parallel_config"]["tensor_parallel_size"] = _quant_tensor_parallel_size()
path.write_text(yaml.dump(config, default_flow_style=False, sort_keys=False))
@@ -448,7 +449,7 @@ def test_image_to_image_alignment_online(accuracy_artifact_root: Path) -> None:
"""Online API test: same pipeline, same seed as offline → PSNR >= 10 dB."""
if importlib.util.find_spec("FlagEmbedding") is None:
raise ImportError("Missing dependency: FlagEmbedding\nInstall with: pip install FlagEmbedding")
from tabulate import tabulate
from tabulate import tabulate # type: ignore[import-untyped]
output_dir = model_output_dir(accuracy_artifact_root, MODEL_NAME + "-online")
@@ -556,7 +557,7 @@ def _run_dit_model(
torch.accelerator.empty_cache()
@hardware_test(res={"cuda": "H100"}, num_cards=8)
@hardware_test(res={"cuda": "H200"}, num_cards=8)
@pytest.mark.skipif(
torch.accelerator.device_count() < AR_TP_SIZE + DIT_TP_SIZE,
reason=f"Needs {AR_TP_SIZE + DIT_TP_SIZE}+ GPUs ({AR_TP_SIZE} AR + {DIT_TP_SIZE} DiT)",
@@ -564,7 +565,7 @@ def _run_dit_model(
def test_image_to_image_alignment(accuracy_artifact_root: Path) -> None:
if importlib.util.find_spec("FlagEmbedding") is None:
raise ImportError("Missing dependency: FlagEmbedding\nInstall with: pip install FlagEmbedding")
from tabulate import tabulate # lazy import
from tabulate import tabulate # type: ignore[import-untyped]
"""KV reuse ON vs OFF: same pipeline, same seed → PSNR >= 10 dB."""
output_dir = model_output_dir(accuracy_artifact_root, MODEL_NAME + "-offline-kv-reuse")
tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py
+2 -2
View File
@@ -299,7 +299,7 @@ def _assert_against_baseline(image: Image.Image, label: str) -> None:
@pytest.mark.full_model
@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"], "npu": "A3"}, num_cards=4)
def test_hunyuan_image3_pixel_accuracy_online(accuracy_artifact_root: Path) -> None:
model = _model_name()
output_dir = model_output_dir(accuracy_artifact_root, MODEL_NAME)
@@ -314,7 +314,7 @@ def test_hunyuan_image3_pixel_accuracy_online(accuracy_artifact_root: Path) -> N
@pytest.mark.full_model
@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"], "npu": "A3"}, num_cards=4)
def test_hunyuan_image3_pixel_accuracy_offline(accuracy_artifact_root: Path) -> None:
model = _model_name()
output_dir = model_output_dir(accuracy_artifact_root, MODEL_NAME)
tests/e2e/accuracy/test_qwen_image.py
+2 -2
View File
@@ -233,7 +233,7 @@ def _run_diffusers_qwen_image_2512(*, model: str, output_path: Path) -> Image.Im
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_qwen_image_matches_diffusers(accuracy_artifact_root: Path) -> None:
model = _model_name()
output_dir = model_output_dir(accuracy_artifact_root, MODEL_ID)
@@ -253,7 +253,7 @@ def test_qwen_image_matches_diffusers(accuracy_artifact_root: Path) -> None:
@pytest.mark.benchmark
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_qwen_image_2512_matches_diffusers_pixelwise(accuracy_artifact_root: Path) -> None:
model = _model_2512_name()
output_dir = model_output_dir(accuracy_artifact_root, MODEL_2512_ID)
tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py
+3 -3
View File
@@ -195,7 +195,7 @@ def _generate_offline_video(*, image_source: str) -> tuple[Path, Path]:
@pytest.mark.benchmark
@pytest.mark.full_model
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_wan22_i2v_diffusers_offline_generates_video(
wan22_i2v_image_source: str | None,
) -> None:
@@ -217,7 +217,7 @@ def test_wan22_i2v_diffusers_offline_generates_video(
@pytest.mark.benchmark
@pytest.mark.full_model
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=2)
@pytest.mark.parametrize("omni_server", SERVER_CASES, indirect=True)
def test_wan22_i2v_online_serving_generates_video(
omni_server,
@@ -244,7 +244,7 @@ def test_wan22_i2v_online_serving_generates_video(
@pytest.mark.benchmark
@pytest.mark.full_model
@hardware_test(res={"cuda": "H100"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=2)
def test_wan22_i2v_serving_matches_diffusers_video_similarity(
wan22_i2v_image_source: str | None,
) -> None:
tests/e2e/features/custom_pipeline/test_async_omni_collective_rpc.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Regression tests for AsyncOmni collective_rpc in inline diffusion mode.
tests/e2e/features/custom_pipeline/test_async_omni_qwen_image_generate.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""E2E tests for AsyncOmni Qwen-Image generation with trajectory_* fields.
tests/e2e/features/custom_pipeline/test_worker_extension.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from __future__ import annotations
tests/e2e/offline_inference/test_device_postprocess_equivalence.py
+1 -1
View File
@@ -83,7 +83,7 @@ def _as_uint8_frames(video: np.ndarray) -> np.ndarray:
_MAX_DIFFERING_FRACTION = 0.35
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_device_postprocess_produces_equivalent_frames() -> None:
float_path = _generate(False)
device_path = _generate(True)
tests/e2e/offline_inference/test_dots_tts_expansion.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""E2E tests for dots.tts native AR offline inference."""
from collections.abc import Mapping
tests/e2e/offline_inference/test_gepard_tts.py
+2 -2
View File
@@ -246,7 +246,7 @@ def _transcribe_for(wav: torch.Tensor, tmp_dir: str, name: str, accepts) -> str:
pytest.param(
"The quick brown fox jumps over the lazy dog.",
None,
marks=pytest.mark.full_model,
marks=pytest.mark.slow,
id="second_text",
),
],
@@ -274,7 +274,7 @@ def test_gepard_offline_zero_shot(omni_runner, run_level: str, text: str, keywor
assert keyword in transcript, f"expected {keyword!r} in transcript, got {transcript!r}"
@pytest.mark.full_model
@pytest.mark.slow
@hardware_test(res={"cuda": "L4"}, num_cards=1)
def test_gepard_offline_long_text_skips_repetition(omni_runner, run_level: str) -> None:
"""The other branch of the prompt layout, end to end.
tests/e2e/offline_inference/test_hunyuanimage3_i2t_expansion.py
+1 -1
View File
@@ -57,7 +57,7 @@ def omni() -> Generator[Omni, None, None]:
yield runner.omni
@hardware_test(res={"cuda": "H100"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=4)
def test_i2t_generates_text(omni: Omni) -> None:
"""Verify I2T output's first 20 token IDs match the HF greedy baseline."""
# Solid-color image keeps the input self-contained and reproducible.
tests/e2e/offline_inference/test_lingbot_world_v2.py
+1 -1
View File
@@ -47,7 +47,7 @@ pytestmark = [
]
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
def test_lingbot_world_v2_tp1_one_block(omni_runner_handler: OmniRunnerHandler) -> None:
"""Load the real checkpoint and generate one causal block on one GPU."""
tests/e2e/offline_inference/test_longcat_video_avatar.py
+9 -6
View File
@@ -1,7 +1,8 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
from collections.abc import Generator
from typing import Any
import numpy as np
import pytest
@@ -105,15 +106,17 @@ def _generate_avatar_frames(
num_frames: int = 5,
sampling_extra_args: dict | None = None,
):
extra_info: dict[str, Any] = dict(additional_information or {})
extra_info.update({"stage": stage, "resolution": "480p"})
multi_modal_data: dict[str, Any] = {"audio": audio_path}
if image_path is not None:
multi_modal_data["image"] = image_path
prompt = {
"prompt": PROMPT,
"negative_prompt": NEGATIVE_PROMPT,
"multi_modal_data": {"audio": audio_path},
"additional_information": additional_information or {},
"multi_modal_data": multi_modal_data,
"additional_information": extra_info,
}
prompt["additional_information"].update({"stage": stage, "resolution": "480p"})
if image_path is not None:
prompt["multi_modal_data"]["image"] = image_path
outputs = omni_runner.generate(
[prompt],
tests/e2e/offline_inference/test_qwen_image_autoround_w4a16_expansion.py
+5 -3
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""E2E test for Qwen-Image AutoRound W4A16 quantized inference.
Verifies that the W4A16 quantized Qwen-Image checkpoint loads end-to-end
@@ -47,8 +47,9 @@ def _first_request_images(outputs) -> list:
return req_out.images
@pytest.mark.full_model
@pytest.mark.diffusion
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_qwen_image_autoround_w4a16_load():
"""Load the W4A16 quantized Qwen-Image model and run a minimal generation.
@@ -68,8 +69,9 @@ def test_qwen_image_autoround_w4a16_load():
assert arr.std() > 1.0, "Generated image appears blank (std ≈ 0)"
@pytest.mark.full_model
@pytest.mark.diffusion
@hardware_test(res={"cuda": "L4"})
@hardware_test(res={"cuda": ["L4", "B200"]})
def test_qwen_image_autoround_w4a16_generate():
"""Full generation: 512×512, 20 steps, CFG=5.0.
tests/e2e/offline_inference/test_voxcpm2_tts.py
+3
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""E2E test for VoxCPM2 native AR offline inference."""
import os
tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py
+8 -8
View File
@@ -156,7 +156,7 @@ def _generate_t2v_video(offline_client, prompt: str = "A cat sitting on a table"
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_runner", quant_i2v_params, indirect=True)
def test_wan22_i2v_autoround_w4a16_generates_video(omni_runner, offline_client):
"""Load the W4A16 quantized Wan2.2 I2V model and verify it produces a valid video."""
@@ -180,7 +180,7 @@ def test_wan22_i2v_autoround_w4a16_generates_video(omni_runner, offline_client):
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_runner", quant_t2v_params, indirect=True)
def test_wan22_t2v_autoround_w4a16_generates_video(omni_runner, offline_client):
"""Load the W4A16 quantized Wan2.2 T2V model and verify it produces a valid video."""
@@ -202,7 +202,7 @@ def test_wan22_t2v_autoround_w4a16_generates_video(omni_runner, offline_client):
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_runner", quant_i2v_params, indirect=True)
def test_wan22_i2v_autoround_w4a16_quant_peak(omni_runner, offline_client):
"""Measure peak GPU memory of W4A16 quantized I2V model."""
@@ -218,7 +218,7 @@ def test_wan22_i2v_autoround_w4a16_quant_peak(omni_runner, offline_client):
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_runner", baseline_i2v_params, indirect=True)
def test_wan22_i2v_autoround_w4a16_baseline_peak(omni_runner, offline_client):
"""Measure peak GPU memory of BF16 baseline I2V model."""
@@ -234,7 +234,7 @@ def test_wan22_i2v_autoround_w4a16_baseline_peak(omni_runner, offline_client):
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
def test_wan22_i2v_autoround_w4a16_memory_savings():
"""Assert quantized I2V model uses meaningfully less memory than BF16 baseline."""
quant_peak = _memory_results["quant_i2v"]
@@ -259,7 +259,7 @@ def test_wan22_i2v_autoround_w4a16_memory_savings():
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_runner", quant_t2v_params, indirect=True)
def test_wan22_t2v_autoround_w4a16_quant_peak(omni_runner, offline_client):
"""Measure peak GPU memory of W4A16 quantized T2V model."""
@@ -275,7 +275,7 @@ def test_wan22_t2v_autoround_w4a16_quant_peak(omni_runner, offline_client):
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_runner", baseline_t2v_params, indirect=True)
def test_wan22_t2v_autoround_w4a16_baseline_peak(omni_runner, offline_client):
"""Measure peak GPU memory of BF16 baseline T2V model."""
@@ -291,7 +291,7 @@ def test_wan22_t2v_autoround_w4a16_baseline_peak(omni_runner, offline_client):
# ------------------------------------------------------------------
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
def test_wan22_t2v_autoround_w4a16_memory_savings():
"""Assert quantized T2V model uses meaningfully less memory than BF16 baseline."""
quant_peak = _memory_results["quant_t2v"]
tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
+15 -11
View File
@@ -33,19 +33,13 @@ NEGATIVE_PROMPT = "low quality, blurry, distorted"
MODEL = "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
PARALLEL_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
# Only the most basic single-card deployment row is cheap enough for PR (L2) / merge (L3)
# CI; the CacheDiT / parallel feature combinations run nightly (L4).
_CORE_MARKS = [pytest.mark.core_model, pytest.mark.advanced_model]
_FULL_MARKS = [pytest.mark.full_model]
def _get_diffusion_feature_cases(model: str):
"""Return diffusion feature cases for HunyuanVideo-1.5.
Designed for 2x H100 environment per issue #1832.
Only the CPU-offload row is cheap enough for PR (L2) / merge (L3);
CacheDiT / parallel combinations run nightly (L4).
"""
return [
# (1 GPU) CPU offload
@@ -57,7 +51,11 @@ def _get_diffusion_feature_cases(model: str):
],
),
id="single_card_cpu_offload",
marks=SINGLE_CARD_MARKS + _CORE_MARKS,
marks=[
*hardware_marks(res={"cuda": "H100"}),
pytest.mark.core_model,
pytest.mark.advanced_model,
],
),
# (1 GPU) CacheDiT + Layerwise CPU offloading
pytest.param(
@@ -70,7 +68,10 @@ def _get_diffusion_feature_cases(model: str):
],
),
id="single_card_cachedit_layerwise",
marks=SINGLE_CARD_MARKS + _FULL_MARKS,
marks=[
*hardware_marks(res={"cuda": ["H100", "B200"]}),
pytest.mark.full_model,
],
),
# (2 GPUs) CacheDiT + TP=2 + VAE patch parallel=2
pytest.param(
@@ -87,7 +88,10 @@ def _get_diffusion_feature_cases(model: str):
],
),
id="parallel_cachedit_tp2_vae2",
marks=PARALLEL_MARKS + _FULL_MARKS,
marks=[
*hardware_marks(res={"cuda": ["H100", "B200"]}, num_cards=2),
pytest.mark.full_model,
],
),
]
tests/e2e/online_serving/test_lingbot_video.py
+1 -1
View File
@@ -20,7 +20,7 @@ PROMPT = "a robotic arm picks up a red block"
NEGATIVE_PROMPT = "low quality, blurry, watermark, text"
DEFAULT_SAMPLING_PARAMS = '{"0":{"num_frames":81,"num_inference_steps":40,"guidance_scale":6.0}}'
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]})
def _get_diffusion_feature_cases(model: str):
tests/e2e/online_serving/test_lingbot_video_expansion.py
+2 -2
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
L4 expansion coverage for ``robbyant/lingbot-video-dense-1.3b``.
@@ -26,7 +26,7 @@ MODEL = "robbyant/lingbot-video-dense-1.3b"
PROMPT = "a robotic arm picks up a red block"
NEGATIVE_PROMPT = "low quality, blurry, watermark, text"
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]})
def _get_diffusion_feature_cases(model: str):
tests/e2e/online_serving/test_lingbot_video_moe.py
+1 -1
View File
@@ -20,7 +20,7 @@ PROMPT = "a robotic arm picks up a red block"
NEGATIVE_PROMPT = "low quality, blurry, watermark, text"
SMOKE_DEFAULT_SAMPLING_PARAMS = '{"0":{"num_frames":81,"num_inference_steps":2,"guidance_scale":3.0}}'
SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
SINGLE_CARD_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]})
def _get_server_cases(model: str):
tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+3 -3
View File
@@ -51,7 +51,7 @@ def _cached_server_vad_artifact() -> None:
)
@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"], "npu": "A3"}, num_cards=1)
@pytest.mark.parametrize("omni_server", SERVER_PARAMS, indirect=True)
def test_duplex_admission_and_expiry_reaper(omni_server, tmp_path: Path) -> None:
args = multi_session_args(
@@ -79,7 +79,7 @@ def test_duplex_admission_and_expiry_reaper(omni_server, tmp_path: Path) -> None
# still talking, which only holds when the duplex pipeline sustains real-time
# throughput. The current NPU stack runs several times slower than real time,
# so it never reaches a mid-stream decision point.
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", SERVER_PARAMS, indirect=True)
def test_duplex_soft_interrupt(omni_server, tmp_path: Path) -> None:
input_wav = validated_soft_interrupt_wav()
@@ -112,7 +112,7 @@ def test_duplex_soft_interrupt(omni_server, tmp_path: Path) -> None:
assert result["followup_response_transcript_ok"] is True
@hardware_test(res={"cuda": "H100"}, num_cards=1)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", SERVER_PARAMS, indirect=True)
def test_duplex_server_vad_hard_interrupt(_cached_server_vad_artifact, omni_server) -> None:
result = asyncio.run(
tests/e2e/online_serving/test_pi0_expansion.py
+2 -2
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""E2E online serving test for π0 (Pi-Zero) OpenPI websocket serving.
@@ -58,7 +58,7 @@ test_params = [
@pytest.mark.full_model
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100"})
@hardware_test(res={"cuda": ["H100", "B200"]})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_pi0_openpi_online(omni_server):
try:
tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
+3 -3
View File
@@ -91,7 +91,7 @@ def _assert_audio_outputs(responses: list[OmniResponse], *, expect_text: bool) -
@pytest.mark.full_model
@pytest.mark.omni
@hardware_test(res={"cuda": "H100"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=4)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_text_only_batch_uses_multi_replica_talker(omni_server, online_client) -> None:
request_config = {
@@ -108,7 +108,7 @@ def test_text_only_batch_uses_multi_replica_talker(omni_server, online_client) -
@pytest.mark.full_model
@pytest.mark.omni
@hardware_test(res={"cuda": "H100"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=4)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_text_to_audio_stream_batch_uses_multi_replica_vocoder(omni_server, online_client) -> None:
request_config = {
@@ -125,7 +125,7 @@ def test_text_to_audio_stream_batch_uses_multi_replica_vocoder(omni_server, onli
@pytest.mark.full_model
@pytest.mark.omni
@hardware_test(res={"cuda": "H100"}, num_cards=4)
@hardware_test(res={"cuda": ["H100", "B200"]}, num_cards=4)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_mixed_modal_stream_batch_generates_text_and_audio(omni_server, online_client) -> None:
request_config = {
tests/e2e/online_serving/test_qwen3_tts_base_expansion.py
+3 -3
View File
@@ -69,7 +69,7 @@ tts_no_async_chunk_server_params = [
DEFAULT_AUDIO_SPEECH_TIMEOUT_S = 180.0
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize(
"omni_server",
tts_async_chunk_server_params + tts_no_async_chunk_server_params,
@@ -99,7 +99,7 @@ def test_voice_clone_streaming_001(omni_server, online_client) -> None:
online_client.send_audio_speech_request(request_config, request_num=get_max_batch_size("few"))
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize(
"omni_server",
tts_async_chunk_server_params + tts_no_async_chunk_server_params,
@@ -127,7 +127,7 @@ def test_response_format_001(omni_server, online_client) -> None:
online_client.send_audio_speech_request(request_config)
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", tts_async_chunk_server_params, indirect=True)
def test_xvector_then_icl_same_ref_audio_keeps_engine_alive(omni_server, online_client) -> None:
"""Regression for #5049: x-vector-only then ICL on the same ``ref_audio``.
tests/e2e/online_serving/test_qwen3_tts_customvoice_expansion.py
+5 -5
View File
@@ -60,7 +60,7 @@ tts_server_params = [
]
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
def test_voice_001(omni_server, online_client) -> None:
"""
@@ -92,7 +92,7 @@ def test_voice_001(omni_server, online_client) -> None:
raise
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
def test_voice_002(omni_server, online_client) -> None:
"""
@@ -115,7 +115,7 @@ def test_voice_002(omni_server, online_client) -> None:
online_client.send_audio_speech_request(request_config)
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
def test_voice_003(omni_server, online_client) -> None:
"""
@@ -138,7 +138,7 @@ def test_voice_003(omni_server, online_client) -> None:
online_client.send_audio_speech_request(request_config)
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
def test_sample_rate_001(omni_server, online_client) -> None:
"""The speech API resamples Qwen3-TTS output from native 24 kHz to 8 kHz."""
@@ -155,7 +155,7 @@ def test_sample_rate_001(omni_server, online_client) -> None:
online_client.send_audio_speech_request(request_config)
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@hardware_test(res={"cuda": ["L4", "B200"]}, num_cards=1)
@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
def test_language_001(omni_server, online_client) -> None:
"""
tests/e2e/online_serving/test_qwen_image_expansion.py
+2 -2
View File
@@ -21,8 +21,8 @@ pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
T2I_PROMPT = "A photo of a cat sitting on a laptop keyboard, digital art style."
NEGATIVE_PROMPT = "blurry, low quality"
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
PARALLEL_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]})
PARALLEL_FEATURE_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]}, num_cards=2)
MODEL_IMAGE = "Qwen/Qwen-Image"
MODEL_2512 = "Qwen/Qwen-Image-2512"
tests/e2e/online_serving/test_sana_wm.py
+1 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Online serving smoke for SANA-WM image-to-video (first-frame I2V via ``/v1/videos``).
tests/e2e/online_serving/test_wan22_expansion.py
+2 -2
View File
@@ -30,8 +30,8 @@ PROMPT = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight
NEGATIVE_PROMPT = "low quality, blurry, distorted face, extra limbs, bad anatomy, watermark, logo, text, ugly, deformed, mutated, jpeg artifacts"
# CUDA marks (original matrix, unchanged)
CUDA_SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
CUDA_PARALLEL_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
CUDA_SINGLE_CARD_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]})
CUDA_PARALLEL_MARKS = hardware_marks(res={"cuda": ["H100", "B200"]}, num_cards=2)
# NPU marks
NPU_TWO_CARD_MARKS = hardware_marks(res={"npu": "A2"}, num_cards=2)
tests/e2e/online_serving/test_zimage_expansion.py
+1 -1
View File
@@ -19,7 +19,7 @@ import pytest
from tests.helpers.mark import hardware_marks
from tests.helpers.runtime import OmniServer, OmniServerParams, OnlineOmniClient
pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
MODEL = "Tongyi-MAI/Z-Image-Turbo"
PROMPT = "A high-detail studio photo of an orange tabby cat sitting on a laptop keyboard."
tests/examples/offline_inference/test_image_to_video.py
+4 -1
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Offline inference tests: image-to-video.
See examples/offline_inference/image_to_video/README.md
@@ -17,7 +20,7 @@ pytestmark = [
pytest.mark.usefixtures("clean_gpu_memory_between_tests"),
pytest.mark.full_model,
pytest.mark.example,
*hardware_marks(res={"cuda": "H100"}),
*hardware_marks(res={"cuda": ["H100", "B200"]}),
]
I2V_SCRIPT = EXAMPLES / "offline_inference" / "image_to_video" / "image_to_video.py"
tests/examples/offline_inference/test_internvla_a1.py
+2 -1
View File
@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""Offline E2E test for InternVLA-A1 open-loop inference."""
@@ -26,6 +26,7 @@ def _required_env(name: str) -> str:
value = os.getenv(name)
if not value:
pytest.skip(f"{name} is not set")
assert value
return value
tests/examples/offline_inference/test_text_to_image.py
+1 -1
View File
@@ -18,7 +18,7 @@ pytestmark = [
pytest.mark.usefixtures("clean_gpu_memory_between_tests"),
pytest.mark.full_model,
pytest.mark.example,
*hardware_marks(res={"cuda": "H100"}),
*hardware_marks(res={"cuda": ["H100", "B200"]}),
]
T2I_SCRIPT = EXAMPLES / "offline_inference" / "text_to_image" / "text_to_image.py"
tests/examples/offline_inference/test_vace_video_generation.py
+4 -1
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""VACE coverage through the shared text-to-video and image-to-video examples."""
from pathlib import Path
@@ -14,7 +17,7 @@ pytestmark = [
pytest.mark.full_model,
pytest.mark.diffusion,
pytest.mark.example,
*hardware_marks(res={"cuda": "H100"}),
*hardware_marks(res={"cuda": ["H100", "B200"]}),
]
TEXT_TO_VIDEO_README = EXAMPLES / "offline_inference" / "text_to_video" / "text_to_video.md"
tests/examples/online_serving/test_qwen2_5_omni.py
+10 -7
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Online serving tests: Qwen2.5-Omni-7B.
See examples/online_serving/qwen2_5_omni/README.md
@@ -42,7 +45,7 @@ test_params = [
common_args = ["python", os.path.join(example_dir, "openai_chat_completion_client_for_multimodal_generation.py")]
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_send_multimodal_request_001(omni_server) -> None:
command = common_args + [
@@ -77,7 +80,7 @@ def test_send_multimodal_request_001(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_send_multimodal_request_002(omni_server) -> None:
command = common_args + [
@@ -112,7 +115,7 @@ def test_send_multimodal_request_002(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_send_multimodal_request_003(omni_server) -> None:
command = [
@@ -137,7 +140,7 @@ def test_send_multimodal_request_003(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_modality_control_001(omni_server) -> None:
command = common_args + [
@@ -165,7 +168,7 @@ def test_modality_control_001(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_modality_control_002(omni_server) -> None:
command = common_args + [
@@ -192,7 +195,7 @@ def test_modality_control_002(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_modality_control_003(omni_server) -> None:
command = common_args + [
@@ -228,7 +231,7 @@ def test_modality_control_003(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@hardware_test(res={"cuda": ["L4", "B200"], "rocm": "MI325"}, num_cards={"cuda": 4, "rocm": 2})
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_stream_001(omni_server) -> None:
command = common_args + [
tests/examples/online_serving/test_qwen3_omni.py
+10 -7
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Online serving tests: Qwen3-Omni.
See examples/online_serving/qwen3_omni/README.md
@@ -41,7 +44,7 @@ test_params = [
common_args = ["python", os.path.join(example_dir, "openai_chat_completion_client_for_multimodal_generation.py")]
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_send_multimodal_request_001(omni_server) -> None:
command = common_args + [
@@ -69,7 +72,7 @@ def test_send_multimodal_request_001(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_send_multimodal_request_002(omni_server) -> None:
command = common_args + [
@@ -100,7 +103,7 @@ def test_send_multimodal_request_002(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_send_multimodal_request_003(omni_server) -> None:
command = ["bash", os.path.join(example_dir, "qwen3_omni/run_curl_multimodal_generation.sh"), "use_image"]
@@ -115,7 +118,7 @@ def test_send_multimodal_request_003(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_modality_control_001(omni_server) -> None:
command = common_args + [
@@ -137,7 +140,7 @@ def test_modality_control_001(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_modality_control_002(omni_server) -> None:
command = common_args + [
@@ -159,7 +162,7 @@ def test_modality_control_002(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_modality_control_003(omni_server) -> None:
command = common_args + [
@@ -189,7 +192,7 @@ def test_modality_control_003(omni_server) -> None:
# TODO: Verify the E2E latency after confirmation baseline.
@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
@hardware_test(res={"cuda": ["H100", "B200"], "rocm": "MI325"}, num_cards=2)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
def test_stream_001(omni_server) -> None:
command = common_args + [
tests/examples/online_serving/test_text_to_image.py
+4 -1
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Online serving tests: text-to-image.
See examples/online_serving/text_to_image/README.md
@@ -18,7 +21,7 @@ from tests.helpers.assertions import assert_image_valid
from tests.helpers.mark import hardware_marks
from tests.helpers.runtime import OmniServer, OmniServerParams
pytestmark = [pytest.mark.full_model, pytest.mark.example, *hardware_marks(res={"cuda": "H100"})]
pytestmark = [pytest.mark.full_model, pytest.mark.example, *hardware_marks(res={"cuda": ["H100", "B200"]})]
T2I_ONLINE_CLIENT = EXAMPLES / "online_serving" / "text_to_image" / "openai_chat_client.py"
EXAMPLE_OUTPUT_SUBFOLDER = "example_online_t2i"
tests/model_tests/diffusion/case_filtering.py
+5 -1
View File
@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Analogous to: https://github.com/vllm-project/vllm/blob/v0.23.0/tests/models/multimodal/generation/vlm_utils/case_filtering.py
@@ -45,7 +48,6 @@ def get_test_group_marks(model_name: str, test_group: list[DiffusionAccs] | None
f"but the max CI device count is {MAX_CI_DEVICES}. "
)
marks.extend(hardware_marks(res={"cuda": "L4"}, num_cards=required_devices))
# hardware_marks only adds skipif for num_cards > 1, so we currently handle the single-device case
# directly here. This should probably be handled in a more common way later on.
assert current_omni_platform is not None and current_omni_platform.device_count is not None
@@ -53,8 +55,10 @@ def get_test_group_marks(model_name: str, test_group: list[DiffusionAccs] | None
if current_omni_platform.is_cuda() and device_count < required_devices:
marks.append(pytest.mark.skip(reason=f"Need {required_devices} devices, got {device_count}"))
if required_devices > 1:
marks.extend(hardware_marks(res={"cuda": ["L4", "B200"]}, num_cards=required_devices))
marks.append(pytest.mark.full_model)
else:
marks.extend(hardware_marks(res={"cuda": "L4"}, num_cards=1))
marks.append(pytest.mark.core_model)
return marks