[Test] Add L4 diffusion feature test for GLM-Image (#3451)
Signed-off-by: herotai214 <herotai214@gmail.com>
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@@ -801,6 +801,45 @@ steps:
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type: DirectoryOrCreate
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- label: ":full_moon: Diffusion X2I(&A&T) · GLM-Image Function Test with H100"
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timeout_in_minutes: 120
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commands:
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- pytest -sv tests/e2e/online_serving/test_glm_image_expansion.py -m "full_model and diffusion and H100" --run-level "full_model"
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agents:
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queue: "mithril-h100-pool"
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plugins:
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- kubernetes:
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podSpec:
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containers:
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- image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
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resources:
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limits:
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nvidia.com/gpu: 2
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volumeMounts:
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- name: devshm
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mountPath: /dev/shm
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- name: hf-cache
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mountPath: /root/.cache/huggingface
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env:
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- name: HF_HOME
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value: /root/.cache/huggingface
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- name: HF_TOKEN
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valueFrom:
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secretKeyRef:
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name: hf-token-secret
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key: token
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nodeSelector:
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node.kubernetes.io/instance-type: gpu-h100-sxm
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volumes:
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- name: devshm
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emptyDir:
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medium: Memory
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- name: hf-cache
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hostPath:
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path: /mnt/hf-cache
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type: DirectoryOrCreate
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- label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
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timeout_in_minutes: 60
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commands:
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@@ -0,0 +1,183 @@
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"""
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Comprehensive tests of diffusion features that are available in online serving mode
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and are supported by the following model:
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- zai-org/GLM-Image (t2i and i2i)
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Coverage (per mode):
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- Baseline
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- Cache-DiT
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- Tensor-Parallel
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- HSDP
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- Sequence parallel
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Topology for multi-GPU cases uses runtime deploy YAML (except TP)
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so stage-1 parallel fields reach the diffusion worker without relying on CLI
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assert_diffusion_response validates successful generation and the expected resolution.
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"""
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import os
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import pytest
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from tests.helpers.mark import hardware_marks
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from tests.helpers.media import generate_synthetic_image
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from tests.helpers.runtime import (
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OmniServer,
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OmniServerParams,
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OpenAIClientHandler,
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dummy_messages_from_mix_data,
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)
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from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
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pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
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MODEL = os.environ.get("GLM_IMAGE_MODEL_PATH", "zai-org/GLM-Image")
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T2I_PROMPT = "A Vincent van Gogh style impressionist painting."
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I2I_PROMPT = "Transform this modern, geometric image into a Vincent van Gogh style impressionist painting."
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NEGATIVE_PROMPT = "low quality, blurry, distorted, unnatural colors"
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TWO_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
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GLM_DEPLOY = get_deploy_config_path("glm_image.yaml")
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GLM_CACHEDIT_DEPLOY = modify_stage_config(
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GLM_DEPLOY,
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updates={
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"stages": {
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1: {"cache_backend": "cache_dit"},
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},
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},
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)
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GLM_TP_2_DEPLOY = modify_stage_config(
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GLM_DEPLOY,
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updates={
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"stages": {
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0: {"devices": "0", "tensor_parallel_size": 1},
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1: {"devices": "0,1", "tensor_parallel_size": 2},
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},
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},
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)
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GLM_HSDP_2_DEPLOY = modify_stage_config(
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GLM_DEPLOY,
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updates={
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"stages": {
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0: {"devices": "0"},
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1: {
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"devices": "0,1",
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"parallel_config": {
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"use_hsdp": True,
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"hsdp_shard_size": 2,
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},
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},
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},
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},
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)
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GLM_SP_2_DEPLOY = modify_stage_config(
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GLM_DEPLOY,
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updates={
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"stages": {
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0: {"devices": "0"},
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1: {
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"devices": "0,1",
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"parallel_config": {
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"ulysses_degree": 2,
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"ring_degree": 1,
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},
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},
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},
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},
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)
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def _get_diffusion_feature_cases(model: str):
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return [
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# Baseline: default deploy (stage 0 -> GPU 0, stage 1 -> GPU 1)
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pytest.param(
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OmniServerParams(
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model=model,
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stage_config_path=GLM_DEPLOY,
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),
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id="baseline",
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marks=TWO_CARD_FEATURE_MARKS,
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),
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# Cache-DiT via runtime deploy patch (cache_backend -> engine_extras on stage 1)
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pytest.param(
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OmniServerParams(
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model=model,
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stage_config_path=GLM_CACHEDIT_DEPLOY,
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),
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id="cachedit",
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marks=TWO_CARD_FEATURE_MARKS,
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),
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# Tensor-Parallel (2 GPUs on diffusion stage)
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pytest.param(
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OmniServerParams(
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model=model,
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stage_config_path=GLM_TP_2_DEPLOY,
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),
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id="tensor_parallel_2",
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marks=TWO_CARD_FEATURE_MARKS,
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),
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# HSDP (2 GPUs on diffusion stage)
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pytest.param(
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OmniServerParams(
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model=model,
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stage_config_path=GLM_HSDP_2_DEPLOY,
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),
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id="hsdp_2",
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marks=TWO_CARD_FEATURE_MARKS,
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),
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# Ulysses sequence parallel degree=2 (2 GPUs)
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pytest.param(
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OmniServerParams(
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model=model,
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stage_config_path=GLM_SP_2_DEPLOY,
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),
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id="sequence_parallel_2",
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marks=TWO_CARD_FEATURE_MARKS,
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),
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]
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MODES = ["t2i", "i2i"]
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@pytest.mark.parametrize("mode", MODES)
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_glm_image(
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omni_server: OmniServer,
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mode: str,
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openai_client: OpenAIClientHandler,
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):
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"""GLM-Image diffusion feature coverage for t2i and i2i on two H100s."""
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if mode == "t2i":
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messages = dummy_messages_from_mix_data(content_text=T2I_PROMPT)
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else:
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image_size = 1024
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image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(image_size, image_size)['base64']}"
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messages = dummy_messages_from_mix_data(
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image_data_url=image_data_url,
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content_text=I2I_PROMPT,
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)
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request_config = {
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"model": omni_server.model,
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"messages": messages,
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"extra_body": {
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"height": 1024,
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"width": 1024,
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"num_inference_steps": 2,
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"guidance_scale": 1.5,
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"true_cfg_scale": 4.0,
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"negative_prompt": NEGATIVE_PROMPT,
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"seed": 42,
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},
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}
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openai_client.send_diffusion_request(request_config)
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