[CI] Enable linux-aarch64-a2 (64GB) and tp2 * 2 max-parallel to speed up CI (#2065)

### What this PR does / why we need it?
Currently our workflow run time takes about 3 hours in total, which
seriously affects the developer experience, so it is urgent to have a
optimization, after this pr, It is expected that the running time of the
full CI can be shortened to 1h40min.

- Enable linux-aarch64-a2 (64GB) to replace linux-arm64-npu (32GB)
- Change TP4 ---> TP2 * 2 max-parallel
- Move DeepSeek-V2-Lite-W8A8 to single card test

### Does this PR introduce _any_ user-facing change?
No


- vLLM version: v0.10.0
- vLLM main:
a2480251ec

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

.github/actionlint.yaml

@@ -1,8 +1,10 @@
 self-hosted-runner:
   # Labels of self-hosted runner in array of strings.
   labels:
-    - linux-arm64-npu-1
+    - linux-aarch64-a2-0
-    - linux-arm64-npu-2
+    - linux-aarch64-a2-1
-    - linux-arm64-npu-4
+    - linux-aarch64-a2-2
+    - linux-aarch64-a2-4
+    - linux-aarch64-a2-8
     - linux-arm64-npu-static-8
     - ubuntu-24.04-arm

.github/workflows/accuracy_test.yaml

@@ -85,8 +85,8 @@ jobs:
       }}
     runs-on: >-
       ${{
-          (matrix.model_name == 'Qwen/Qwen3-30B-A3B' && 'linux-arm64-npu-4') ||
+          (matrix.model_name == 'Qwen/Qwen3-30B-A3B' && 'linux-aarch64-a2-2') ||
-          'linux-arm64-npu-2'
+          'linux-aarch64-a2-1'
       }}
     strategy:
       matrix:

.github/workflows/vllm_ascend_doctest.yaml

@@ -48,7 +48,7 @@ jobs:
       matrix:
         vllm_verison: [v0.9.1-dev, v0.9.1-dev-openeuler, main, main-openeuler]
     name: vLLM Ascend test
-    runs-on: linux-arm64-npu-1
+    runs-on: linux-aarch64-a2-1
     container:
       image: swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/vllm-ascend:${{ matrix.vllm_verison }}
     steps:

.github/workflows/vllm_ascend_test.yaml

@@ -136,7 +136,7 @@ jobs:
     strategy:
       max-parallel: 2
       matrix:
-        os: [linux-arm64-npu-1]
+        os: [linux-aarch64-a2-1]
         vllm_version: [main, v0.10.0]
     name: singlecard e2e test
     runs-on: ${{ matrix.os }}
@@ -213,9 +213,9 @@ jobs:
     needs: [e2e]
     if: ${{ needs.e2e.result == 'success' }}
     strategy:
-      max-parallel: 1
+      max-parallel: 2
       matrix:
-        os: [linux-arm64-npu-4]
+        os: [linux-aarch64-a2-2]
         vllm_version: [main, v0.10.0]
     name: multicard e2e test
     runs-on: ${{ matrix.os }}
@@ -275,7 +275,6 @@ jobs:
           # To avoid oom, we need to run the test in a single process.
           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_QwQ
-          pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W8A8
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_dbo
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeekV3_dbo
           pytest -sv tests/e2e/multicard/test_data_parallel.py

.github/workflows/vllm_ascend_test_long_term.yaml

@@ -42,7 +42,7 @@ jobs:
     strategy:
       max-parallel: 2
       matrix:
-        os: [linux-arm64-npu-1, linux-arm64-npu-4]
+        os: [linux-aarch64-a2-1, linux-aarch64-a2-2]
         vllm_version: [main, v0.10.0]
     name: vLLM Ascend long term test
     runs-on: ${{ matrix.os }}

benchmarks/scripts/run_accuracy.py

@@ -50,17 +50,17 @@ MODEL_TYPE = {
 # Command templates for running evaluations
 MODEL_RUN_INFO = {
     "Qwen/Qwen3-30B-A3B": (
-        "export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=4,gpu_memory_utilization=0.6,enable_expert_parallel=True'\n"
+        "export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6,enable_expert_parallel=True'\n"
         "lm_eval --model vllm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
         "--apply_chat_template --fewshot_as_multiturn --num_fewshot 5 --batch_size 1"
     ),
     "Qwen/Qwen3-8B-Base": (
-        "export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6'\n"
+        "export MODEL_ARGS='pretrained={model},max_model_len=4096,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.6'\n"
         "lm_eval --model vllm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
         "--apply_chat_template --fewshot_as_multiturn --num_fewshot 5 --batch_size 1"
     ),
     "Qwen/Qwen2.5-VL-7B-Instruct": (
-        "export MODEL_ARGS='pretrained={model},max_model_len=8192,dtype=auto,tensor_parallel_size=2,max_images=2'\n"
+        "export MODEL_ARGS='pretrained={model},max_model_len=8192,dtype=auto,tensor_parallel_size=1,max_images=2'\n"
         "lm_eval --model vllm-vlm --model_args $MODEL_ARGS --tasks {datasets} \ \n"
         "--apply_chat_template --fewshot_as_multiturn  --batch_size 1"
     ),
@@ -94,9 +94,9 @@ EXECUTION_MODE = {
 
 # Model arguments for evaluation
 MODEL_ARGS = {
-    "Qwen/Qwen3-8B-Base": "pretrained=Qwen/Qwen3-8B-Base,max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6",
+    "Qwen/Qwen3-8B-Base": "pretrained=Qwen/Qwen3-8B-Base,max_model_len=4096,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.6",
-    "Qwen/Qwen2.5-VL-7B-Instruct": "pretrained=Qwen/Qwen2.5-VL-7B-Instruct,max_model_len=8192,dtype=auto,tensor_parallel_size=2,max_images=2",
+    "Qwen/Qwen2.5-VL-7B-Instruct": "pretrained=Qwen/Qwen2.5-VL-7B-Instruct,max_model_len=8192,dtype=auto,tensor_parallel_size=1,max_images=2",
-    "Qwen/Qwen3-30B-A3B": "pretrained=Qwen/Qwen3-30B-A3B,max_model_len=4096,dtype=auto,tensor_parallel_size=4,gpu_memory_utilization=0.6,enable_expert_parallel=True",
+    "Qwen/Qwen3-30B-A3B": "pretrained=Qwen/Qwen3-30B-A3B,max_model_len=4096,dtype=auto,tensor_parallel_size=2,gpu_memory_utilization=0.6,enable_expert_parallel=True",
 }
 
 # Whether to apply chat template formatting

tests/e2e/long_term/accuracy/accuracy_multicard.py

@@ -91,9 +91,9 @@ MORE_ARGS = {
     "Qwen/Qwen2.5-0.5B-Instruct":
     None,
     "Qwen/Qwen3-30B-A3B":
-    "tensor_parallel_size=4,enable_expert_parallel=True,enforce_eager=True",
+    "tensor_parallel_size=2,enable_expert_parallel=True,enforce_eager=True",
     "deepseek-ai/DeepSeek-V2-Lite":
-    "tensor_parallel_size=4,trust_remote_code=True,enforce_eager=True"
+    "tensor_parallel_size=2,trust_remote_code=True,enforce_eager=True"
 }
 
 multiprocessing.set_start_method("spawn", force=True)

tests/e2e/multicard/test_fused_moe_allgather_ep.py

@@ -46,7 +46,7 @@ def test_generate_with_allgather():
     sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
 
     with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V3-Pruning"),
-                    tensor_parallel_size=4,
+                    tensor_parallel_size=2,
                     enforce_eager=True,
                     max_model_len=1024,
                     dtype="auto",
@@ -74,7 +74,7 @@ def test_generate_with_alltoall():
     sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
 
     with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V3-Pruning"),
-                    tensor_parallel_size=4,
+                    tensor_parallel_size=2,
                     enforce_eager=True,
                     max_model_len=1024,
                     dtype="auto",

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -42,7 +42,7 @@ def test_models_distributed_QwQ():
     with VllmRunner(
             "Qwen/QwQ-32B",
             dtype=dtype,
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             distributed_executor_backend="mp",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -57,7 +57,7 @@ def test_models_distributed_DeepSeek_multistream_moe():
     with VllmRunner(
             "vllm-ascend/DeepSeek-V3-Pruning",
             dtype=dtype,
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             distributed_executor_backend="mp",
             additional_config={
                 "torchair_graph_config": {
@@ -82,7 +82,7 @@ def test_models_distributed_DeepSeek_dbo():
     with VllmRunner(
             "deepseek-ai/DeepSeek-V2-Lite",
             dtype=dtype,
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             distributed_executor_backend="mp",
     ) as vllm_model:
         model_arch = 'DeepseekV2ForCausalLM'
@@ -106,7 +106,7 @@ def test_models_distributed_DeepSeekV3_dbo():
     with VllmRunner(
             "vllm-ascend/DeepSeek-V3-Pruning",
             dtype=dtype,
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             distributed_executor_backend="mp",
     ) as vllm_model:
         model_arch = 'DeepseekV3ForCausalLM'
@@ -118,24 +118,6 @@ def test_models_distributed_DeepSeekV3_dbo():
         vllm_model.generate(example_prompts, sampling_params)
 
 
-@pytest.mark.skip(reason="Due to OOM,waiting for 1311pr to merge in")
-def test_models_distributed_DeepSeek_W8A8():
-    example_prompts = [
-        "Hello, my name is",
-    ]
-    max_tokens = 5
-
-    with VllmRunner(
-            snapshot_download("vllm-ascend/DeepSeek-V2-Lite-W8A8"),
-            max_model_len=8192,
-            enforce_eager=True,
-            dtype="auto",
-            tensor_parallel_size=4,
-            quantization="ascend",
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-
-
 def test_models_distributed_pangu():
     example_prompts = [
         "Hello, my name is",
@@ -147,7 +129,7 @@ def test_models_distributed_pangu():
             max_model_len=8192,
             enforce_eager=True,
             dtype="auto",
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             distributed_executor_backend="mp",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -169,7 +151,7 @@ def test_models_distributed_topk() -> None:
     with VllmRunner(
             "deepseek-ai/DeepSeek-V2-Lite",
             dtype=dtype,
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             distributed_executor_backend="mp",
     ) as vllm_model:
         vllm_model.generate(example_prompts, sampling_params)
@@ -186,7 +168,7 @@ def test_models_distributed_Qwen3_W8A8():
             max_model_len=8192,
             enforce_eager=True,
             dtype="auto",
-            tensor_parallel_size=4,
+            tensor_parallel_size=2,
             quantization="ascend",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)

tests/e2e/multicard/test_pipeline_parallel.py

@@ -22,7 +22,7 @@ MODELS = [
     "Qwen/Qwen3-0.6B",
 ]
 
-TENSOR_PARALLELS = [2]
+TENSOR_PARALLELS = [1]
 PIPELINE_PARALLELS = [2]
 DIST_EXECUTOR_BACKEND = ["mp", "ray"]
 

tests/e2e/multicard/test_torchair_graph_mode.py

@@ -30,7 +30,7 @@ os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 def _deepseek_torchair_test_fixture(
     additional_config: Dict,
     *,
-    tensor_parallel_size=4,
+    tensor_parallel_size=2,
 ):
     example_prompts = [
         "Hello, my name is",
@@ -98,7 +98,7 @@ def test_e2e_deepseekv3_with_torchair_ms_mla():
 def _pangu_torchair_test_fixture(
     additional_config: Dict,
     *,
-    tensor_parallel_size=4,
+    tensor_parallel_size=2,
 ):
     example_prompts = [
         "Hello, my name is",

tests/e2e/singlecard/quant/test_w8a8.py

@@ -0,0 +1,42 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+import pytest
+from modelscope import snapshot_download  # type: ignore[import-untyped]
+
+from tests.e2e.conftest import VllmRunner
+
+MODELS = [
+    "vllm-ascend/DeepSeek-V2-Lite-W8A8",
+    "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_quant_W8A8(example_prompts, model):
+    max_tokens = 5
+    model_path = snapshot_download(model)
+    with VllmRunner(
+            model_path,
+            max_model_len=8192,
+            enforce_eager=True,
+            dtype="auto",
+            gpu_memory_utilization=0.7,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)

tests/e2e/singlecard/test_offline_inference.py

@@ -25,7 +25,6 @@ from unittest.mock import patch
 
 import pytest
 import vllm  # noqa: F401
-from modelscope import snapshot_download  # type: ignore[import-untyped]
 from vllm import SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
@@ -40,9 +39,6 @@ MODELS = [
 MULTIMODALITY_VL_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
 MULTIMODALITY_AUDIO_MODELS = ["Qwen/Qwen2-Audio-7B-Instruct"]
 
-QUANTIZATION_MODELS = [
-    "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8",
-]
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 AUDIO_ASSETS = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
 AUDIO_PROMPT_TEMPLATES = {
@@ -70,27 +66,6 @@ def test_models(model: str, dtype: str, max_tokens: int) -> None:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-@pytest.mark.parametrize("model", QUANTIZATION_MODELS)
-@pytest.mark.parametrize("max_tokens", [5])
-def test_quantization_models(model: str, max_tokens: int) -> None:
-    prompt = "The following numbers of the sequence " + ", ".join(
-        str(i) for i in range(1024)) + " are:"
-    example_prompts = [prompt]
-
-    # NOTE: Using quantized model repo id from modelscope encounters an issue,
-    # this pr (https://github.com/vllm-project/vllm/pull/19212) fix the issue,
-    # after it is being merged, there's no need to download model explicitly.
-    model_path = snapshot_download(model)
-
-    with VllmRunner(model_path,
-                    max_model_len=8192,
-                    enforce_eager=True,
-                    dtype="auto",
-                    gpu_memory_utilization=0.7,
-                    quantization="ascend") as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-
-
 @pytest.mark.parametrize("model", MULTIMODALITY_VL_MODELS)
 def test_multimodal_vl(model, prompt_template, vllm_runner):
     image = ImageAsset("cherry_blossom") \