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[CI] Reduce Qwen-Image Function and share step-execution perf server (#6613)

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Signed-off-by: NumberWan <wantszkin2003@gmail.com>
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NumberWan authored and GitHub committed 2026-08-25 22:55:15 +08:00
1 parent 0d5d1115d6
commit dd527b4245
2 files changed
+125 -177
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tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+24 -50
View File
@@ -74,13 +74,14 @@
"full_model",
"diffusion"
],
"description": "Single-device baseline (no parallelism) with step execution",
"description": "Single-device step execution: sequential 512/1536 plus 512 concurrency sweep. One server (max-num-seqs 8).",
"server_type": "vllm-omni",
"server_params": {
"model": "Qwen/Qwen-Image",
"serve_args": {
"enable-diffusion-pipeline-profiler": true,
"step-execution": true
"step-execution": true,
"max-num-seqs": 8
}
},
"benchmark_params": [
@@ -119,6 +120,27 @@
"peak_memory_mb_mean": 64500.0
}
}
},
{
"name": "512x512_steps20_high_concurrency",
"dataset": "random",
"task": "t2i",
"width": 512,
"height": 512,
"num-inference-steps": 20,
"num-prompts": [20, 40, 80, 160],
"max-concurrency": [1, 2, 4, 8],
"warmup-requests": 8,
"warmup-concurrency": 8,
"warmup-num-inference-steps": 20,
"enable-negative-prompt": true,
"baseline": {
"H100": {
"throughput_qps": [0.3937, 0.6606, 0.6188, 0.7752],
"latency_mean": [2.5466, 3.0235, 6.3948, 10.1944],
"peak_memory_mb_mean": [55149.0, 55150.0, 55153.1458, 55156.325]
}
}
}
]
},
@@ -243,53 +265,5 @@
}
}
]
},
{
"test_name": "test_qwen_image_single_device_step_execution_high_concurrency",
"mark": [
{
"hardware_marks": {
"res": {
"cuda": "H100"
},
"num_cards": 1
}
},
"full_model",
"diffusion"
],
"description": "Single-device Qwen-Image step-level execution benchmark at 512x512, 20 steps, concurrency sweep",
"server_type": "vllm-omni",
"server_params": {
"model": "Qwen/Qwen-Image",
"serve_args": {
"enable-diffusion-pipeline-profiler": true,
"step-execution": true,
"max-num-seqs": 8
}
},
"benchmark_params": [
{
"name": "512x512_steps20_high_concurrency",
"dataset": "random",
"task": "t2i",
"width": 512,
"height": 512,
"num-inference-steps": 20,
"num-prompts": [20, 40, 80, 160],
"max-concurrency": [1, 2, 4, 8],
"warmup-requests": 8,
"warmup-concurrency": 8,
"warmup-num-inference-steps": 20,
"enable-negative-prompt": true,
"baseline": {
"H100": {
"throughput_qps": [0.3937, 0.6606, 0.6188, 0.7752],
"latency_mean": [2.5466, 3.0235, 6.3948, 10.1944],
"peak_memory_mb_mean": [55149.0, 55150.0, 55153.1458, 55156.325]
}
}
}
]
}
]
tests/e2e/online_serving/test_qwen_image_expansion.py
+101 -127
View File
@@ -8,9 +8,8 @@ and are supported by the following text-to-image models:
- Qwen-Image-2512
One feature per test case, matching the Test Plan in PR #1682 (Qwen-Image-Edit).
Supported features for Qwen-Image series: TeaCache, Cache-DiT, Ulysses-SP, Ring-Attention,
CFG-Parallel, Tensor-Parallel, VAE-Patch-Parallel, CPU offload (layerwise).
See docs/user_guide/diffusion_acceleration.md.
Nightly covers each feature once, alternating models (5× Qwen-Image, 4× Qwen-Image-2512).
Ulysses stays on Qwen-Image. See docs/user_guide/diffusion_acceleration.md.
"""
import pytest
@@ -25,141 +24,116 @@ 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)
MODEL_IMAGE = "Qwen/Qwen-Image"
MODEL_2512 = "Qwen/Qwen-Image-2512"
def _get_diffusion_feature_cases(model: str):
return [
pytest.param(
OmniServerParams(
model=model,
server_args=["--enable-cpu-offload"],
),
id="cpu_offload",
marks=SINGLE_CARD_FEATURE_MARKS,
# One server per feature. Alternate models; keep feature pytest ids unchanged.
FEATURE_CASES = [
pytest.param(
OmniServerParams(
model=MODEL_IMAGE,
server_args=["--enable-cpu-offload"],
),
pytest.param(
OmniServerParams(model=model, server_args=["--step-execution"]),
id="step_execution",
marks=SINGLE_CARD_FEATURE_MARKS,
id="cpu_offload",
marks=SINGLE_CARD_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(model=MODEL_2512, server_args=["--step-execution"]),
id="step_execution",
marks=SINGLE_CARD_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(model=MODEL_IMAGE, server_args=["--cache-backend", "tea_cache"]),
id="cache_tea_cache",
marks=SINGLE_CARD_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=MODEL_2512,
server_args=[
"--cache-backend",
"cache_dit",
"--enable-layerwise-offload",
],
),
pytest.param(
OmniServerParams(model=model, server_args=["--cache-backend", "tea_cache"]),
id="cache_tea_cache",
marks=SINGLE_CARD_FEATURE_MARKS,
id="layerwise_offload",
marks=SINGLE_CARD_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=MODEL_IMAGE,
server_args=[
"--cache-backend",
"cache_dit",
"--ulysses-degree",
"2",
],
),
pytest.param(
OmniServerParams(
model=model,
server_args=[
"--cache-backend",
"cache_dit",
"--enable-layerwise-offload",
],
),
id="layerwise_offload",
marks=SINGLE_CARD_FEATURE_MARKS,
id="ulysses_2",
marks=PARALLEL_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=MODEL_2512,
server_args=[
"--cache-backend",
"cache_dit",
"--ring",
"2",
],
),
pytest.param(
OmniServerParams(
model=model,
server_args=[
"--cache-backend",
"cache_dit",
"--ulysses-degree",
"2",
],
),
id="ulysses_2",
marks=PARALLEL_FEATURE_MARKS,
id="ring_2",
marks=PARALLEL_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=MODEL_IMAGE,
server_args=[
"--cache-backend",
"tea_cache",
"--cfg-parallel-size",
"2",
],
),
pytest.param(
OmniServerParams(
model=model,
server_args=[
"--cache-backend",
"cache_dit",
"--ring",
"2",
],
),
id="ring_2",
marks=PARALLEL_FEATURE_MARKS,
id="cfg_parallel_2",
marks=PARALLEL_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=MODEL_2512,
server_args=[
"--cache-backend",
"cache_dit",
"--tensor-parallel-size",
"2",
"--vae-patch-parallel-size",
"2",
"--vae-use-tiling",
"--diffusion-quantization-config",
'{"method":"fp8"}',
],
),
pytest.param(
OmniServerParams(
model=model,
server_args=[
"--cache-backend",
"tea_cache",
"--cfg-parallel-size",
"2",
],
),
id="cfg_parallel_2",
marks=PARALLEL_FEATURE_MARKS,
id="vae_patch_parallel_2",
marks=PARALLEL_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=MODEL_IMAGE,
server_args=[
"--use-hsdp",
"--hsdp-shard-size",
"2",
],
),
pytest.param(
OmniServerParams(
model=model,
server_args=[
"--cache-backend",
"cache_dit",
"--tensor-parallel-size",
"2",
"--vae-patch-parallel-size",
"2",
"--vae-use-tiling",
"--diffusion-quantization-config",
'{"method":"fp8"}',
],
),
id="vae_patch_parallel_2",
marks=PARALLEL_FEATURE_MARKS,
),
pytest.param(
OmniServerParams(
model=model,
server_args=[
"--use-hsdp",
"--hsdp-shard-size",
"2",
],
),
id="parallel_hsdp",
marks=PARALLEL_FEATURE_MARKS,
),
]
id="parallel_hsdp",
marks=PARALLEL_FEATURE_MARKS,
),
]
@pytest.mark.parametrize(
"omni_server",
_get_diffusion_feature_cases("Qwen/Qwen-Image"),
indirect=True,
)
@pytest.mark.parametrize("omni_server", FEATURE_CASES, indirect=True)
def test_qwen_image(omni_server: OmniServer, online_client: OnlineOmniClient):
"""Test each diffusion feature with Qwen-Image (text-to-image), one feature per case."""
messages = dummy_messages_from_mix_data(content_text=T2I_PROMPT)
request_config = {
"model": omni_server.model,
"messages": messages,
"extra_body": {
"height": 512,
"width": 512,
"num_inference_steps": 2,
"negative_prompt": NEGATIVE_PROMPT,
"true_cfg_scale": 4.0,
"seed": 42,
},
}
online_client.send_diffusion_request(request_config)
@pytest.mark.parametrize(
"omni_server",
_get_diffusion_feature_cases("Qwen/Qwen-Image-2512"),
indirect=True,
)
def test_qwen_image_2512(omni_server: OmniServer, online_client: OnlineOmniClient):
"""Test each diffusion feature with Qwen-Image-2512 (text-to-image), one feature per case."""
"""One diffusion feature per case; model is chosen in FEATURE_CASES."""
messages = dummy_messages_from_mix_data(content_text=T2I_PROMPT)
request_config = {
"model": omni_server.model,