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[CI] refect e2e test (#4799)

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### What this PR does / why we need it?
This PR updates the CI configuration and adjusts a set of end-to-end
(e2e) tests under tests/e2e/multicard, in order to refactor the test
suite and ensure compatibility with current codebase and CI workflows.

1. tests/e2e/multicard/test_prefix_caching.py: change model to Qwen3-8B
and rename the test case
2. tests/e2e/multicard/test_quantization.py: rename the test case
3. tests/e2e/multicard/test_qwen3_moe.py: remove duplicate test and
rename test cases
4. tests/e2e/multicard/test_qwen3_next.py: rename test cases and change
the W8A8 pruning model to the W8A8 model and remove the eager parameter
5. tests/e2e/multicard/test_shared_expert_dp.py: rename test case and
remove the eager parameter
6. tests/e2e/multicard/test_single_request_aclgraph.py: rename test case
and change Qwen3-30B to Qwen3-0.6B
7. tests/e2e/multicard/test_torchair_graph_mode.py: delete test cases
about torchair

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
This commit is contained in:
zhangxinyuehfad
2025-12-12 08:42:08 +08:00
committed by GitHub
parent a6ef3ac4e4
commit bfafe30953
8 changed files with 30 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
.github/workflows/_e2e_test.yaml vendored
View File

@@ -197,8 +197,8 @@ jobs:
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_flashcomm_v1
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight
pytest -sv tests/e2e/multicard/test_pipeline_parallel.py
pytest -sv tests/e2e/multicard/test_prefix_caching.py
pytest -sv tests/e2e/multicard/test_pipeline_parallel.py
pytest -sv tests/e2e/multicard/test_qwen3_moe.py
pytest -sv tests/e2e/multicard/test_offline_weight_load.py
@@ -266,9 +266,8 @@ jobs:
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC
pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Kimi_K2_Thinking_W4A16
# pytest -sv tests/e2e/multicard/test_qwen3_moe.py::test_models_distributed_Qwen3_MOE_TP2_WITH_EP
# pytest -sv tests/e2e/multicard/test_qwen3_moe.py::test_models_distributed_Qwen3_MOE_W8A8_WITH_EP
pytest -sv tests/e2e/multicard/test_data_parallel_tp2.py
- name: Install Ascend toolkit & triton_ascend (for Qwen3-Next-80B-A3B-Instruct)
shell: bash -l {0}
run: |

2
tests/e2e/multicard/test_pipeline_parallel.py
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@@ -44,4 +44,4 @@ def test_models_pp2(model: str, tp_size: int, pp_size: int,
pipeline_parallel_size=pp_size,
distributed_executor_backend=distributed_executor_backend,
gpu_memory_utilization=0.7) as vllm_model:
vllm_model.generate_greedy(prompts, 64)
vllm_model.generate_greedy(prompts, 64)

8
tests/e2e/multicard/test_prefix_caching.py
View File

@@ -1,6 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Compare the with and without prefix caching on V1 scheduler or AscendScheduler."""
"""Compare the with and without prefix caching."""
import pytest
@@ -9,7 +9,7 @@ from tests.e2e.model_utils import check_outputs_equal
MODELS = [
# for MHA
"Qwen/Qwen3-8B-Base",
"Qwen/Qwen3-8B",
# for MLA
"deepseek-ai/DeepSeek-V2-Lite-Chat"
]
@@ -60,9 +60,8 @@ INPUT_PROMPTS = [
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_tokens", [50])
def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
def test_models_prefix_cache_tp2(model: str, max_tokens: int) -> None:
with VllmRunner(model,
enforce_eager=False,
max_model_len=2048,
tensor_parallel_size=2,
gpu_memory_utilization=0.7) as vllm_model:
@@ -71,7 +70,6 @@ def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
with VllmRunner(model,
enable_prefix_caching=False,
enforce_eager=False,
max_model_len=2048,
tensor_parallel_size=2,
gpu_memory_utilization=0.7) as vllm_model:

13
tests/e2e/multicard/test_quantization.py
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@@ -25,16 +25,17 @@ from modelscope import snapshot_download # type: ignore
from tests.e2e.conftest import VllmRunner
def test_models_distributed_quantized_W8A8():
def test_qwen2_5_w8a8_external_quantized_tp2():
example_prompts = [
"The president of the United States is",
]
max_tokens = 5
with VllmRunner(snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
tensor_parallel_size=2,
max_model_len=4096,
gpu_memory_utilization=0.8,
enforce_eager=False) as vllm_model:
with VllmRunner(
snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
tensor_parallel_size=2,
max_model_len=4096,
gpu_memory_utilization=0.8,
) as vllm_model:
vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
golden_results = [

40
tests/e2e/multicard/test_qwen3_moe.py
View File

@@ -29,21 +29,8 @@ from modelscope import snapshot_download # type: ignore
from tests.e2e.conftest import VllmRunner
def test_models_distributed_Qwen3_MOE_TP2():
example_prompts = [
"Hello, my name is",
]
max_tokens = 5
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
tensor_parallel_size=2,
distributed_executor_backend="mp",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
def test_qwen3_moe_distributed_mp_tp2_ep():
example_prompts = [
"Hello, my name is",
]
@@ -53,12 +40,11 @@ def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
tensor_parallel_size=2,
enable_expert_parallel=True,
distributed_executor_backend="mp",
enforce_eager=False,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
def test_models_distributed_Qwen3_MOE_W8A8():
def test_qwen3_moe_w8a8_distributed_tp2():
example_prompts = [
"Hello, my name is",
]
@@ -73,7 +59,7 @@ def test_models_distributed_Qwen3_MOE_W8A8():
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
def test_models_distributed_Qwen3_MOE_W8A8_WITH_EP():
def test_qwen3_moe_w8a8_distributed_tp2_ep():
example_prompts = [
"Hello, my name is",
]
@@ -88,7 +74,7 @@ def test_models_distributed_Qwen3_MOE_W8A8_WITH_EP():
vllm_model.generate_greedy(example_prompts, max_tokens)
def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH_AIV():
def test_qwen3_moe_distributed_aiv_tp2():
os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
example_prompts = [
"Hello, my name is",
@@ -99,23 +85,5 @@ def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH_AIV():
"Qwen/Qwen3-30B-A3B",
dtype=dtype,
tensor_parallel_size=2,
enforce_eager=False,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH():
if 'HCCL_OP_EXPANSION_MODE' in os.environ:
del os.environ['HCCL_OP_EXPANSION_MODE']
example_prompts = [
"Hello, my name is",
]
dtype = "auto"
max_tokens = 5
with VllmRunner(
"Qwen/Qwen3-30B-A3B",
dtype=dtype,
tensor_parallel_size=2,
enforce_eager=False,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)

17
tests/e2e/multicard/test_qwen3_next.py
View File

@@ -29,7 +29,7 @@ from modelscope import snapshot_download # type: ignore
from tests.e2e.conftest import VllmRunner
def test_models_distributed_Qwen3_NEXT_TP4():
def test_qwen3_next_distributed_mp_tp4():
example_prompts = [
"Hello, my name is",
] * 4
@@ -38,13 +38,12 @@ def test_models_distributed_Qwen3_NEXT_TP4():
tensor_parallel_size=4,
max_model_len=4096,
gpu_memory_utilization=0.8,
distributed_executor_backend="mp",
enforce_eager=True) as vllm_model:
distributed_executor_backend="mp") as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
del vllm_model
def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
def test_qwen3_next_distributed_mp_full_decode_only_tp4():
example_prompts = [
"Hello, my name is",
] * 4
@@ -54,7 +53,6 @@ def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
max_model_len=4096,
gpu_memory_utilization=0.8,
distributed_executor_backend="mp",
enforce_eager=False,
compilation_config={
"cudagraph_mode": "FULL_DECODE_ONLY",
"cudagraph_capture_sizes": [1, 8, 24, 48, 60]
@@ -64,7 +62,7 @@ def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
# TODO: Fix the accuary of batch chunked prefill
def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
def test_qwen3_next_distributed_mp_eager_mtp_similarity_tp4():
example_prompts = ["Hello, my name is"]
max_tokens = 20
@@ -110,16 +108,15 @@ def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
# TODO: will conduct accuracy verification after the subsequent version becomes stable
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
def test_models_distributed_Qwen3_NEXT_W8A8DYNAMIC_WITH_EP():
def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
example_prompts = [
"Hello, my name is",
]
max_tokens = 5
with VllmRunner(
snapshot_download(
"vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8-Pruning"),
snapshot_download("vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8"),
max_model_len=4096,
tensor_parallel_size=2,
tensor_parallel_size=4,
gpu_memory_utilization=0.4,
max_num_seqs=1,
enable_expert_parallel=True,

6
tests/e2e/multicard/test_shared_expert_dp.py
View File

@@ -7,13 +7,13 @@ from tests.e2e.conftest import VllmRunner
from tests.e2e.model_utils import check_outputs_equal
MODELS = [
"vllm-ascend/DeepSeek-V2-Lite",
"deepseek-ai/DeepSeek-V2-Lite",
]
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
@pytest.mark.parametrize("model", MODELS)
def test_models_with_enable_shared_expert_dp(model: str) -> None:
def test_deepseek_v2_lite_enable_shared_expert_dp_tp2(model: str) -> None:
if 'HCCL_OP_EXPANSION_MODE' in os.environ:
del os.environ['HCCL_OP_EXPANSION_MODE']
@@ -51,7 +51,7 @@ def test_models_with_enable_shared_expert_dp(model: str) -> None:
model,
max_model_len=1024,
tensor_parallel_size=2,
enforce_eager=False,
enable_expert_parallel=True,
compilation_config={
"cudagraph_capture_sizes": [1, 4, 8, 16],
"cudagraph_mode": "FULL_DECODE_ONLY",

5
tests/e2e/multicard/test_single_request_aclgraph.py
View File

@@ -23,7 +23,7 @@ from vllm.utils.network_utils import get_open_port
from tests.e2e.conftest import RemoteOpenAIServer
MODELS = ["Qwen/Qwen3-30B-A3B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
DATA_PARALLELS = [2]
@@ -39,7 +39,8 @@ api_keyword_args = {
@pytest.mark.asyncio
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dp_size", DATA_PARALLELS)
async def test_single_request_aclgraph(model: str, dp_size: int) -> None:
async def test_models_single_request_aclgraph_dp2(model: str,
dp_size: int) -> None:
port = get_open_port()
env_dict = {
"TASK_QUEUE_ENABLE": "1",