[ROCm] [CI] Add More Tests (#542)

Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>
This commit is contained in:
TJian authored and GitHub committed 2026-01-12 14:06:31 +08:00
1 parent d0836d8459
commit f25cd37f93
8 files changed
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@@ -0,0 +1,73 @@
#!/bin/bash
# Helper function to safely login to ECR Public with per-job config isolation
# Uses DOCKER_CONFIG environment variable to prevent race conditions
#
# This script prevents the "device or resource busy" error by giving each
# Buildkite job its own isolated Docker config directory.
#
# Usage:
# source docker_login_ecr_public.sh && safe_docker_login_ecr_public
set -euo pipefail
# Configuration
ECR_REGISTRY="public.ecr.aws"
setup_isolated_docker_config() {
# Use BUILDKITE_JOB_ID for job-specific isolation
# Fallback to PID if running outside Buildkite
local job_id="${BUILDKITE_JOB_ID:-$$}"
# Set Docker config to job-specific directory
export DOCKER_CONFIG="/tmp/docker-config-${job_id}"
# Create directory if it doesn't exist
mkdir -p "$DOCKER_CONFIG"
echo "[docker-config] Using isolated Docker config: $DOCKER_CONFIG"
}
check_docker_auth() {
# Check if already authenticated to the given registry
# Returns 0 if authenticated, 1 if not
local registry="$1"
# Check if credentials exist in the isolated config
if [[ -f "$DOCKER_CONFIG/config.json" ]]; then
# Check if registry is present in config
if grep -q "$registry" "$DOCKER_CONFIG/config.json" 2>/dev/null; then
return 0
fi
fi
return 1
}
safe_docker_login_ecr_public() {
# Setup isolated config first
setup_isolated_docker_config
local registry="$ECR_REGISTRY"
# Check if already authenticated (within this job)
if check_docker_auth "$registry"; then
echo "[docker-login] Already authenticated to $registry in this job"
return 0
fi
# Perform login to isolated config directory
echo "[docker-login] Logging in to $ECR_REGISTRY (isolated config)..."
if aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$ECR_REGISTRY"; then
echo "[docker-login] Login successful (config: $DOCKER_CONFIG)"
return 0
else
local exit_code=$?
echo "[docker-login] ERROR: Login failed with exit code $exit_code" >&2
return $exit_code
fi
}
# Execute if run as script (not sourced)
if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
safe_docker_login_ecr_public
fi
@@ -66,6 +66,14 @@ done
echo "--- Pulling container"
image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-rocm-omni"
container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
# Install AWS CLI to authenticate to ECR Public Gallery to get higher rate limit for pulling images
sudo apt-get update && sudo apt-get install -y awscli
# Use safe docker login helper to prevent race conditions
source "$(dirname "${BASH_SOURCE[0]}")/../docker_login_ecr_public.sh"
safe_docker_login_ecr_public
# Pull the container from ECR Public Gallery
docker pull "${image_name}"
remove_docker_container() {
+57 -4
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@@ -1,10 +1,10 @@
steps:
- label: "Diffusion Model Test"
timeout_in_minutes: 15
timeout_in_minutes: 20
agent_pool: mi325_2
depends_on: amd-build
mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export GPU_ARCHS=gfx942
@@ -20,7 +20,7 @@ steps:
timeout_in_minutes: 15
agent_pool: mi325_1
depends_on: amd-build
mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export GPU_ARCHS=gfx942
@@ -34,11 +34,33 @@ steps:
- export VLLM_ROCM_USE_AITER_RMSNORM=0
- pytest -s -v tests/e2e/offline_inference/test_cache_dit.py tests/e2e/offline_inference/test_teacache.py
- label: "Diffusion Parallelism Test"
timeout_in_minutes: 15
agent_pool: mi325_2
depends_on: amd-build
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export MIOPEN_DEBUG_CONV_DIRECT=0
- export MIOPEN_DEBUG_CONV_GEMM=0
- pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py
- label: "Diffusion GPU Worker Test"
timeout_in_minutes: 20
agent_pool: mi325_2
depends_on: amd-build
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export MIOPEN_DEBUG_CONV_DIRECT=0
- export MIOPEN_DEBUG_CONV_GEMM=0
- pytest -s -v tests/diffusion/test_gpu_worker.py
- label: "Omni Model Test Qwen2-5-Omni"
timeout_in_minutes: 15
agent_pool: mi325_2
depends_on: amd-build
mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export GPU_ARCHS=gfx942
@@ -51,3 +73,34 @@ steps:
- export VLLM_ROCM_USE_AITER_LINEAR=0
- export VLLM_ROCM_USE_AITER_RMSNORM=0
- pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
- label: "Omni Model Test Qwen3-Omni"
timeout_in_minutes: 15
agent_pool: mi325_2
depends_on: amd-build
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export VLLM_LOGGING_LEVEL=DEBUG
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- export MIOPEN_DEBUG_CONV_DIRECT=0
- export MIOPEN_DEBUG_CONV_GEMM=0
- pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py
- label: "Diffusion Image Edit Test"
timeout_in_minutes: 15
agent_pool: mi325_1
depends_on: amd-build
mirror_hardwares: [amdproduction]
grade: Blocking
commands:
- export GPU_ARCHS=gfx942
- export VLLM_LOGGING_LEVEL=DEBUG
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- export MIOPEN_DEBUG_CONV_DIRECT=0
- export MIOPEN_DEBUG_CONV_GEMM=0
- export VLLM_ROCM_USE_AITER=1
- export VLLM_ROCM_USE_AITER_MHA=1
- export VLLM_ROCM_USE_AITER_LINEAR=0
- export VLLM_ROCM_USE_AITER_RMSNORM=0
- pytest -s -v tests/e2e/online_serving/test_i2i_multi_image_input.py
+1 -1
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@@ -13,7 +13,7 @@
depends_on: ~
soft_fail: false
commands:
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
- "source .buildkite/scripts/docker_login_ecr_public.sh && safe_docker_login_ecr_public"
- "docker build -f docker/Dockerfile.rocm -t {{ docker_image_amd }} --progress plain ."
- "docker push {{ docker_image_amd }}"
key: "amd-build"
@@ -0,0 +1,98 @@
# Stage config for running Qwen3-Omni-MoE with 3-stage architecture
# Stage 0: Thinker (multimodal understanding + text generation)
# Stage 1: Talker (text embeddings → 16-layer RVQ codec codes)
# Stage 2: Code2Wav (8-layer RVQ codes → audio waveform)
# The following config has been verified on 2x H100-80G GPUs.
stage_args:
- stage_id: 0
runtime:
devices: "0,1"
max_batch_size: 1
engine_args:
model_stage: thinker
model_arch: Qwen3OmniMoeForConditionalGeneration
worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
gpu_memory_utilization: 0.6
enforce_eager: false
trust_remote_code: true
engine_output_type: latent # Output hidden states for talker
distributed_executor_backend: "mp"
enable_prefix_caching: false
hf_config_name: thinker_config
tensor_parallel_size: 2
load_format: dummy
final_output: true
final_output_type: text
is_comprehension: true
default_sampling_params:
temperature: 0.4
top_p: 0.9
top_k: 1
max_tokens: 100
seed: 42
detokenize: True
repetition_penalty: 1.05
- stage_id: 1
runtime:
devices: "1"
max_batch_size: 1
engine_args:
model_stage: talker
model_arch: Qwen3OmniMoeForConditionalGeneration
worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
gpu_memory_utilization: 0.3
enforce_eager: true
trust_remote_code: true
engine_output_type: latent # Output codec codes for code2wav
# tensor_parallel_size: 2
enable_prefix_caching: false
distributed_executor_backend: "mp"
hf_config_name: talker_config
load_format: dummy
engine_input_source: [0]
custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
# final_output: true
# final_output_type: text
default_sampling_params:
temperature: 0.9
top_k: 50
max_tokens: 100
seed: 42
detokenize: False
repetition_penalty: 1.05
stop_token_ids: [2150]
- stage_id: 2
runtime:
devices: "0"
max_batch_size: 1
engine_args:
model_stage: code2wav
model_arch: Qwen3OmniMoeForConditionalGeneration
worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
enforce_eager: true
trust_remote_code: true
enable_prefix_caching: false
engine_output_type: audio # Final output: audio waveform
gpu_memory_utilization: 0.1
distributed_executor_backend: "mp"
max_num_batched_tokens: 1000000
hf_config_name: thinker_config
load_format: dummy
engine_input_source: [1]
custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
final_output: true
final_output_type: audio
default_sampling_params:
temperature: 0.0
top_p: 1.0
top_k: -1
max_tokens: 200
seed: 42
detokenize: True
repetition_penalty: 1.1
@@ -10,14 +10,20 @@ from pathlib import Path
import pytest
from vllm.assets.video import VideoAsset
from vllm_omni.utils import is_rocm
from .conftest import OmniRunner
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
# CI stage config for 2xH100-80G GPUs
stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")]
# CI stage config for 2xH100-80G GPUs or AMD GPU MI325
if is_rocm():
# ROCm stage config optimized for MI325 GPU
stage_configs = [str(Path(__file__).parent / "stage_configs" / "rocm" / "qwen3_omni_ci.yaml")]
else:
stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_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]
@@ -0,0 +1,98 @@
# Stage config for running Qwen3-Omni-MoE with 3-stage architecture
# Stage 0: Thinker (multimodal understanding + text generation)
# Stage 1: Talker (text embeddings → 16-layer RVQ codec codes)
# Stage 2: Code2Wav (8-layer RVQ codes → audio waveform)
# The following config has been verified on 2x H100-80G GPUs.
stage_args:
- stage_id: 0
runtime:
devices: "0,1"
max_batch_size: 5
engine_args:
model_stage: thinker
model_arch: Qwen3OmniMoeForConditionalGeneration
worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
gpu_memory_utilization: 0.6
enforce_eager: false
trust_remote_code: true
engine_output_type: latent # Output hidden states for talker
distributed_executor_backend: "mp"
enable_prefix_caching: false
hf_config_name: thinker_config
tensor_parallel_size: 2
load_format: dummy
final_output: true
final_output_type: text
is_comprehension: true
default_sampling_params:
temperature: 0.4
top_p: 0.9
top_k: 1
max_tokens: 100
seed: 42
detokenize: True
repetition_penalty: 1.05
- stage_id: 1
runtime:
devices: "1"
max_batch_size: 5
engine_args:
model_stage: talker
model_arch: Qwen3OmniMoeForConditionalGeneration
worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
gpu_memory_utilization: 0.3
enforce_eager: true
trust_remote_code: true
engine_output_type: latent # Output codec codes for code2wav
# tensor_parallel_size: 2
enable_prefix_caching: false
distributed_executor_backend: "mp"
hf_config_name: talker_config
load_format: dummy
engine_input_source: [0]
custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
# final_output: true
# final_output_type: text
default_sampling_params:
temperature: 0.9
top_k: 50
max_tokens: 100
seed: 42
detokenize: False
repetition_penalty: 1.05
stop_token_ids: [2150]
- stage_id: 2
runtime:
devices: "0"
max_batch_size: 1
engine_args:
model_stage: code2wav
model_arch: Qwen3OmniMoeForConditionalGeneration
worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
enforce_eager: true
trust_remote_code: true
enable_prefix_caching: false
engine_output_type: audio # Final output: audio waveform
gpu_memory_utilization: 0.1
distributed_executor_backend: "mp"
max_num_batched_tokens: 1000000
hf_config_name: thinker_config
load_format: dummy
engine_input_source: [1]
custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
final_output: true
final_output_type: audio
default_sampling_params:
temperature: 0.0
top_p: 1.0
top_k: -1
max_tokens: 200
seed: 42
detokenize: True
repetition_penalty: 1.1
+8 -2
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@@ -20,12 +20,18 @@ import pytest
from vllm.assets.video import VideoAsset
from vllm.utils import get_open_port
from vllm_omni.utils import is_rocm
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
# CI stage config for 2*H100-80G GPUs
stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_omni_ci.yaml")]
# CI stage config for 2xH100-80G GPUs or AMD GPU MI325
if is_rocm():
# ROCm stage config optimized for MI325 GPU
stage_configs = [str(Path(__file__).parent / "stage_configs" / "rocm" / "qwen3_omni_ci.yaml")]
else:
stage_configs = [str(Path(__file__).parent / "stage_configs" / "qwen3_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]