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[NPU][CI] Add the CI workflow on NPU (#231)

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Signed-off-by: gcanlin <canlinguosdu@gmail.com>
This commit is contained in:
Canlin Guo authored and GitHub committed 2025-12-17 17:07:12 +08:00
1 parent 85f0c1177f
commit 2b6d8f17ba
6 files changed
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.buildkite/pipeline.yml
+8
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@@ -9,6 +9,14 @@ steps:
agents:
queue: "cpu_queue_premerge_us_east_1"
- label: "Test on NPU"
depends_on: ~
key: npu-test
commands:
- ".buildkite/scripts/hardware_ci/run_npu_test.sh"
agents:
queue: "ascend"
- label: "Simple Unit Test"
commands:
- ".buildkite/scripts/simple_test.sh"
.buildkite/scripts/hardware_ci/run_npu_test.sh
+145
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@@ -0,0 +1,145 @@
#!/bin/bash
# This script build the Ascend NPU docker image and run the offline inference inside the container.
# It serves a sanity check for compilation and basic model usage.
set -ex
# Base ubuntu image with basic ascend development libraries and python installed
VLLM_OMNI_REPO="https://github.com/vllm-project/vllm-omni.git"
BASE_IMAGE_NAME="quay.nju.edu.cn/ascend/vllm-ascend:v0.11.0rc2"
image_name="npu/vllm-omni-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}"
# image_name="npu/vllm-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}"
container_name="npu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
# BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards
agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
echo "agent_idx: ${agent_idx}"
builder_name="cachebuilder${agent_idx}"
builder_cache_dir="/mnt/docker-cache${agent_idx}"
mkdir -p ${builder_cache_dir}
# Try building the docker image
cat <<EOF | DOCKER_BUILDKIT=1 docker build \
--add-host pypi-cache:${PYPI_CACHE_HOST} \
--builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
--cache-to type=local,dest=${builder_cache_dir},mode=max \
--build-arg BUILDKITE_PULL_REQUEST="${BUILDKITE_PULL_REQUEST}" \
--build-arg BUILDKITE_PULL_REQUEST_REPO="${BUILDKITE_PULL_REQUEST_REPO}" \
--progress=plain --load -t ${image_name} -f - .
FROM ${BASE_IMAGE_NAME}
# Define environments
ENV DEBIAN_FRONTEND=noninteractive
RUN pip config set global.index-url http://pypi-cache:${PYPI_CACHE_PORT}/pypi/simple && \
pip config set global.trusted-host pypi-cache && \
apt-get update -y && \
apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev && \
rm -rf /var/cache/apt/* && \
rm -rf /var/lib/apt/lists/*
# Install for pytest to make the docker build cache layer always valid
RUN --mount=type=cache,target=/root/.cache/pip \
pip install pytest>=6.0 pytest-cov modelscope
COPY . .
# Install vllm-omni
WORKDIR /workspace
ARG VLLM_OMNI_REPO=https://github.com/vllm-project/vllm-omni.git
ARG VLLM_OMNI_TAG=main
ARG BUILDKITE_PULL_REQUEST
ARG BUILDKITE_PULL_REQUEST_REPO
RUN git config --global url."https://gh-proxy.test.osinfra.cn/https://github.com/".insteadOf "https://github.com/" && \
if [ "\$BUILDKITE_PULL_REQUEST" != "false" ] && [ -n "\$BUILDKITE_PULL_REQUEST" ]; then \
echo "Cloning and checking out PR #\$BUILDKITE_PULL_REQUEST..." && \
git clone \$VLLM_OMNI_REPO /workspace/vllm-omni && \
cd /workspace/vllm-omni && \
git fetch origin pull/\$BUILDKITE_PULL_REQUEST/head:pr-\$BUILDKITE_PULL_REQUEST && \
git checkout pr-\$BUILDKITE_PULL_REQUEST; \
else \
echo "Not a PR build, using main branch" && \
git clone --depth 1 \$VLLM_OMNI_REPO /workspace/vllm-omni; \
fi
RUN --mount=type=cache,target=/root/.cache/pip \
export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
source /usr/local/Ascend/nnal/atb/set_env.sh && \
export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
python3 -m pip install -v -e /workspace/vllm-omni/
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
WORKDIR /workspace/vllm-omni
CMD ["/bin/bash"]
EOF
# Setup cleanup
remove_docker_container() {
docker rm -f "${container_name}" || true;
docker image rm -f "${image_name}" || true;
docker system prune -f || true;
}
trap remove_docker_container EXIT
# Generate corresponding --device args based on BUILDKITE_AGENT_NAME
# Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
# e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
# returns --device /dev/davinci0 --device /dev/davinci1
parse_and_gen_devices() {
local input="$1"
local index cards_num
if [[ "$input" =~ ([0-9]+)-([0-9]+)cards$ ]]; then
index="${BASH_REMATCH[1]}"
cards_num="${BASH_REMATCH[2]}"
else
echo "parse error" >&2
return 1
fi
local devices=""
local i=0
while (( i < cards_num )); do
local dev_idx=$(((index - 1)*cards_num + i ))
devices="$devices --device /dev/davinci${dev_idx}"
((i++))
done
# trim leading space
devices="${devices#"${devices%%[![:space:]]*}"}"
# Output devices: assigned to the caller variable
printf '%s' "$devices"
}
devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
# Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
# This test checks whether the OOT platform interface is functioning properly in conjunction with
# the hardware plugin vllm-ascend.
hf_model_cache_dir=/mnt/hf_cache${agent_idx}
ms_model_cache_dir=/mnt/modelscope${agent_idx}
mkdir -p ${hf_model_cache_dir}
mkdir -p ${ms_model_cache_dir}
docker run \
--init \
${devices} \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v ${hf_model_cache_dir}:/root/.cache/huggingface \
-v ${ms_model_cache_dir}:/root/.cache/modelscope \
--network host \
--entrypoint="" \
--name "${container_name}" \
"${image_name}" \
bash -c '
set -e
VLLM_USE_MODELSCOPE=True pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
'
docker/Dockerfile.ci.npu
+15
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@@ -0,0 +1,15 @@
ARG VLLM_ASCEND_IMAGE=quay.nju.edu.cn/ascend/vllm-ascend
ARG VLLM_ASCEND_TAG=v0.11.0rc2
FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
ARG APP_DIR=/vllm-workspace/vllm-omni
WORKDIR ${APP_DIR}
COPY . .
# Install vllm-omni with dev dependencies
RUN pip install --no-cache-dir -e ".[dev]"
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
ENTRYPOINT []
tests/e2e/offline_inference/stage_configs/npu/qwen2_5_omni_ci.yaml
+104
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@@ -0,0 +1,104 @@
# stage config for running qwen2.5-omni with architecture of OmniLLM.
# This config is optimized for CI e2e tests.
stage_args:
- stage_id: 0
runtime:
process: true # Run this stage in a separate process
devices: "0"
max_batch_size: 1
engine_args:
model_stage: thinker
model_arch: Qwen2_5OmniForConditionalGeneration
worker_cls: vllm_omni.worker.npu.npu_ar_worker.NPUARWorker
scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
max_model_len: 896
max_num_batched_tokens: 896
max_num_seqs: 1
gpu_memory_utilization: 0.8
skip_mm_profiling: true
enforce_eager: true # Now we only support eager mode
trust_remote_code: true
engine_output_type: latent
enable_prefix_caching: false
is_comprehension: true
final_output: true
final_output_type: text
default_sampling_params:
temperature: 0.0
top_p: 1.0
top_k: -1
max_tokens: 128
seed: 42
detokenize: True
repetition_penalty: 1.1
- stage_id: 1
runtime:
process: true
devices: "1"
max_batch_size: 1
engine_args:
model_stage: talker
model_arch: Qwen2_5OmniForConditionalGeneration
worker_cls: vllm_omni.worker.npu.npu_ar_worker.NPUARWorker
scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
max_model_len: 896
max_num_batched_tokens: 896
max_num_seqs: 1
gpu_memory_utilization: 0.8
skip_mm_profiling: true
enforce_eager: true
trust_remote_code: true
enable_prefix_caching: false
engine_output_type: latent
engine_input_source: [0]
custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen2_5_omni.thinker2talker
default_sampling_params:
temperature: 0.9
top_p: 0.8
top_k: 40
max_tokens: 128
seed: 42
detokenize: True
repetition_penalty: 1.05
stop_token_ids: [8294]
- stage_id: 2
runtime:
process: true
devices: "0" # Example: use a different GPU than the previous stage; use "0" if single GPU
max_batch_size: 1
engine_args:
model_stage: code2wav
model_arch: Qwen2_5OmniForConditionalGeneration
worker_cls: vllm_omni.worker.npu.npu_generation_worker.NPUGenerationWorker
scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
gpu_memory_utilization: 0.15
enforce_eager: true
trust_remote_code: true
enable_prefix_caching: false
engine_output_type: audio
engine_input_source: [1]
final_output: true
final_output_type: audio
default_sampling_params:
temperature: 0.0
top_p: 1.0
top_k: -1
max_tokens: 128
seed: 42
detokenize: True
repetition_penalty: 1.1
# Top-level runtime config (concise): default windows and stage edges
runtime:
enabled: true
defaults:
window_size: -1 # Simplified: trigger downstream only after full upstream completion
max_inflight: 1 # Simplified: process serially within each stage
edges:
- from: 0 # thinker → talker: trigger only after receiving full input (-1)
to: 1
window_size: -1
- from: 1 # talker → code2wav: trigger only after receiving full input (-1)
to: 2
window_size: -1
tests/e2e/offline_inference/test_qwen2_5_omni.py
+14 -4
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@@ -10,18 +10,24 @@ import pytest
from vllm.assets.audio import AudioAsset
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
from vllm.envs import VLLM_USE_MODELSCOPE
from vllm.multimodal.image import convert_image_mode
from vllm_omni.utils import is_npu
from .conftest import OmniRunner
from .utils import create_new_process_for_each_test
models = ["Qwen/Qwen2.5-Omni-3B"]
# CI stage config optimized for 24GB GPU (L4/RTX3090)
stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen2_5_omni_ci.yaml")]
# CI stage config optimized for 24GB GPU (L4/RTX3090) or NPU
if is_npu():
stage_config = str(Path(__file__).parent / "stage_configs" / "npu" / "qwen2_5_omni_ci.yaml")
else:
stage_config = str(Path(__file__).parent / "stage_configs" / "qwen2_5_omni_ci.yaml")
# Create parameter combinations for model and stage config
test_params = [(model, stage_config) for model in models for stage_config in stage_configs]
test_params = [(model, stage_config) for model in models]
@pytest.mark.core_model
@@ -36,7 +42,11 @@ def test_mixed_modalities_to_audio(omni_runner: type[OmniRunner], test_config: t
audio = AudioAsset("mary_had_lamb").audio_and_sample_rate
audio = (audio[0][: 16000 * 5], audio[1]) # Trim to first 5 seconds
image = convert_image_mode(ImageAsset("cherry_blossom").pil_image.resize((128, 128)), "RGB")
video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
if not VLLM_USE_MODELSCOPE:
video = VideoAsset(name="baby_reading", num_frames=4).np_ndarrays
else:
# modelscope can't access raushan-testing-hf/videos-test, skip video input temporarily
video = None
outputs = runner.generate_multimodal(
prompts=question,
tests/e2e/offline_inference/test_t2i_model.py
+9
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@@ -5,6 +5,8 @@ from pathlib import Path
import pytest
import torch
from vllm_omni.utils.platform_utils import is_npu
# ruff: noqa: E402
REPO_ROOT = Path(__file__).resolve().parents[2]
if str(REPO_ROOT) not in sys.path:
@@ -14,8 +16,15 @@ from vllm_omni import Omni
os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
models = ["Tongyi-MAI/Z-Image-Turbo", "riverclouds/qwen_image_random"]
# NPU still can't run Tongyi-MAI/Z-Image-Turbo properly
# Modelscope can't find riverclouds/qwen_image_random
# TODO: When NPU support is ready, remove this branch.
if is_npu():
models = ["Qwen/Qwen-Image"]
@pytest.mark.parametrize("model_name", models)
def test_diffusion_model(model_name: str):