[CI]Add Kimi k2 nightly test

.github/workflows/_e2e_nightly_single_node.yaml

@@ -143,5 +143,3 @@ jobs:
           # ignore test_dispatch_ffn_combine until the test is fixed
           pytest -sv ${{ inputs.tests }} \
           --ignore=tests/e2e/nightly/single_node/ops/singlecard_ops/test_fused_moe.py
-
-

.github/workflows/nightly_test_a3.yaml

@@ -83,6 +83,9 @@ jobs:
           - name: multi-node-qwen-vl-disagg-pd
             config_file_path: Qwen3-VL-235B-disagg-pd.yaml
             size: 2
+          - name: multi-node-kimi-k2-instruct-w8a8
+            config_file_path: Kimi-K2-Instruct-W8A8.yaml
+            size: 2
     uses: ./.github/workflows/_e2e_nightly_multi_node.yaml
     with:
       soc_version: a3
@@ -144,6 +147,9 @@ jobs:
           - name: qwen3-next-w8a8
             os: linux-aarch64-a3-4
             tests: tests/e2e/nightly/single_node/models/test_qwen3_next_w8a8.py
+          - name: kimi-k2-thinking
+            os: linux-aarch64-a3-16
+            tests: tests/e2e/nightly/single_node/models/test_kimi_k2_thinking.py
           # TODO: Replace deepseek3.2-exp with deepseek3.2 after nightly tests pass
           # - name: deepseek3_2-exp-w8a8
           #   os: linux-aarch64-a3-16

tests/e2e/nightly/multi_node/config/Kimi-K2-Instruct-W8A8.yaml

@@ -0,0 +1,79 @@
+test_name: "test Kimi-K2-Instruct-W8A8 2-nodes-dp4-tp8-torchair"
+model: "vllm-ascend/Kimi-K2-Instruct-W8A8"
+
+num_nodes: 2
+npu_per_node: 16
+env_common:
+  VLLM_USE_MODELSCOPE: true
+  HCCL_BUFFSIZE: 1024
+  SERVER_PORT: 8080
+  OMP_PROC_BIND: false
+  OMP_NUM_THREADS: 100
+  NUMEXPR_MAX_THREADS: 128
+
+deployment:
+  -
+    server_cmd: >
+      vllm serve "vllm-ascend/Kimi-K2-Instruct-W8A8"
+      --host 0.0.0.0
+      --port $SERVER_PORT
+      --data-parallel-size 4
+      --data-parallel-size-local 2
+      --data-parallel-start-rank 0
+      --data-parallel-address $LOCAL_IP
+      --data-parallel-rpc-port 13389
+      --tensor-parallel-size 8
+      --seed 1024
+      --enable-expert-parallel
+      --max-num-seqs 32
+      --max-model-len 8192
+      --max-num-batched-tokens 8192
+      --quantization ascend
+      --trust-remote-code
+      --no-enable-prefix-caching
+      --gpu-memory-utilization 0.9
+      --additional-config '{"torchair_graph_config":{"enabled":true}}'
+
+  -
+    server_cmd: >
+      vllm serve "vllm-ascend/Kimi-K2-Instruct-W8A8"
+      --headless
+      --data-parallel-size 4
+      --data-parallel-size-local 2
+      --data-parallel-start-rank 2
+      --data-parallel-address $MASTER_IP
+      --data-parallel-rpc-port 13389
+      --tensor-parallel-size 8
+      --seed 1024
+      --enable-expert-parallel
+      --max-num-seqs 32
+      --max-model-len 8192
+      --max-num-batched-tokens 8192
+      --quantization ascend
+      --trust-remote-code
+      --no-enable-prefix-caching
+      --gpu-memory-utilization 0.9
+      --additional-config '{"torchair_graph_config":{"enabled":true}}'
+
+benchmarks:
+  perf:
+    case_type: performance
+    dataset_path: vllm-ascend/GSM8K-in3500-bs2800
+    request_conf: vllm_api_stream_chat
+    dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
+    num_prompts: 512
+    max_out_len: 256
+    batch_size: 64
+    trust_remote_code: True
+    request_rate: 11.2
+    baseline: 1
+    threshold: 0.97
+  acc:
+    case_type: accuracy
+    dataset_path: vllm-ascend/gsm8k-lite
+    request_conf: vllm_api_general_chat
+    dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_chat_prompt
+    max_out_len: 7680
+    batch_size: 64
+    baseline: 95
+    threshold: 5

tests/e2e/nightly/single_node/models/test_kimi_k2_thinking.py

@@ -0,0 +1,110 @@
+# 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.
+#
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils.network_utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import run_aisbench_cases
+
+MODELS = [
+    "moonshotai/Kimi-K2-Thinking",
+]
+
+TENSOR_PARALLELS = [16]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+aisbench_cases = [{
+    "case_type": "accuracy",
+    "dataset_path": "vllm-ascend/gsm8k-lite",
+    "request_conf": "vllm_api_general_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
+    "max_out_len": 4096,
+    "batch_size": 32,
+    "baseline": 95,
+    "threshold": 5
+}, {
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/GSM8K-in3500-bs400",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 512,
+    "max_out_len": 256,
+    "batch_size": 64,
+    "trust_remote_code": True,
+    "request_rate": 11.2,
+    "baseline": 1,
+    "threshold": 0.97
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
+async def test_models(model: str, tp_size: int) -> None:
+    port = get_open_port()
+    env_dict = {
+        "HCCL_BUFFSIZE": "1024",
+        "TASK_QUEUE_ENABLE": "1",
+        "OMP_PROC_BIND": "false",
+        "HCCL_OP_EXPANSION_MODE": "AIV",
+        "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True"
+    }
+    server_args = [
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--port",
+        str(port),
+        "--max-model-len",
+        "8192",
+        "--max-num-batched-tokens",
+        "8192",
+        "--max-num-seqs",
+        "12",
+        "--gpu-memory-utilization",
+        "0.9",
+        "--trust-remote-code",
+        "--enable-expert-parallel",
+        "--no-enable-prefix-caching",
+    ]
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        # aisbench test
+        run_aisbench_cases(model, port, aisbench_cases)

tools/aisbench.py

@@ -92,6 +92,8 @@ def __init__(self,
         self.max_out_len = aisbench_config["max_out_len"]
         self.batch_size = aisbench_config["batch_size"]
         self.request_rate = aisbench_config.get("request_rate", 0)
+        self.trust_remote_code = aisbench_config.get("trust_remote_code",
+                                                     False)
         self.temperature = aisbench_config.get("temperature")
         self.top_k = aisbench_config.get("top_k")
         self.top_p = aisbench_config.get("top_p")
@@ -145,6 +147,9 @@ def _init_request_conf(self):
                          f'max_out_len = {self.max_out_len},', content)
         content = re.sub(r'batch_size.*', f'batch_size = {self.batch_size},',
                          content)
+        content = re.sub(r'trust_remote_code=.*',
+                         f'trust_remote_code={self.trust_remote_code},',
+                         content)
         content = content.replace("top_k", "#top_k")
         content = content.replace("seed", "#seed")
         content = content.replace("repetition_penalty", "#repetition_penalty")