[CI]Add Disaggregated PD Nightly Test for Qwen3-235B and Qwen3-VL-235B (#5502)

### What this PR does / why we need it?
This PR adds online **Disaggregated Prefill/Decode** performance and
accuracy tests for the **Qwen3-235B-A22B** and
**Qwen3-VL-235B-A22B-Instruct** models to the Nightly test suite.

These test configurations simulate the deployment of massive MoE and
Vision-Language models in **a dual-node (32 NPU)** environment,
utilizing Mooncake (KVCache Transfer) technology to achieve efficient KV
cache transfer between the Prefill node and the Decode node.

#### Test Configuration
**Qwen3-235B-A22B**
- Model: Qwen/Qwen3-235B-A22B
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Disaggregated Prefill & Decode
- Node 0 (Producer/Prefill): **DP2 + TP8 + EP + FLASHCOMM1 +
FUSED_MC2**.
- Node 1 (Consumer/Decode): **DP4 + TP4 + EP + FLASHCOMM1 + FUSED_MC2 +
FULL_DECODE_ONLY**.
- Benchmarks:
  - Performance: vllm-ascend/GSM8K-in3500-bs2800.
  - Accuracy: vllm-ascend/gsm8k-lite.

**Qwen3-VL-235B-A22B-Instruct**
- Model: Qwen/Qwen3-VL-235B-A22B-Instruct
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Disaggregated Prefill & Decode
  - Node 0 (Producer/Prefill): **DP2 + TP8 + EP**.
  - Node 1 (Consumer/Decode): **DP4 + TP4 + EP + FULL_DECODE_ONLY**.
- Benchmarks:
  - Performance: vllm-ascend/textvqa-perf-1080p.
  - Accuracy: vllm-ascend/textvqa-lite.

### How was this patch tested?
Nightly test action on CI

- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1

---------

Signed-off-by: MrZ20 <2609716663@qq.com>

.github/workflows/nightly_test_a3.yaml

@@ -77,6 +77,12 @@ jobs:
           - name: multi-node-qwenw8a8-2node-longseq
             config_file_path: Qwen3-235B-W8A8-longseq.yaml
             size: 2
+          - name: multi-node-qwen-disagg-pd
+            config_file_path: Qwen3-235B-disagg-pd.yaml
+            size: 2
+          - name: multi-node-qwen-vl-disagg-pd
+            config_file_path: Qwen3-VL-235B-disagg-pd.yaml
+            size: 2
     uses: ./.github/workflows/_e2e_nightly_multi_node.yaml
     with:
       soc_version: a3

tests/e2e/nightly/multi_node/config/Qwen3-235B-disagg-pd.yaml

@@ -0,0 +1,121 @@
+test_name: "test Qwen3-235B-A22B disaggregated_prefill"
+model: "Qwen/Qwen3-235B-A22B"
+num_nodes: 2
+npu_per_node: 16
+env_common:
+  VLLM_USE_MODELSCOPE: true
+  PYTORCH_NPU_ALLOC_CONF: expandable_segments:True
+  HCCL_BUFFSIZE: 1024
+  HCCL_OP_EXPANSION_MODE: "AIV"
+  OMP_PROC_BIND: false
+  OMP_NUM_THREADS: 1
+  VLLM_ASCEND_ENABLE_FLASHCOMM1: 1
+  VLLM_ASCEND_ENABLE_FUSED_MC2: 2
+  TASK_QUEUE_ENABLE: 1
+  SERVER_PORT: 8080
+  
+disaggregated_prefill:
+  enabled: true
+  prefiller_host_index: [0]
+  decoder_host_index: [1]
+
+deployment:
+  -
+    server_cmd: >
+        vllm serve "Qwen/Qwen3-235B-A22B"
+        --host 0.0.0.0
+        --port $SERVER_PORT
+        --data-parallel-size 2
+        --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
+        --max-num-seqs 32
+        --max-model-len 8192
+        --max-num-batched-tokens 8192
+        --enable-expert-parallel
+        --trust-remote-code
+        --gpu-memory-utilization 0.9
+        --no-enable-prefix-caching
+        --kv-transfer-config
+        '{"kv_connector": "MooncakeConnectorV1",
+        "kv_role": "kv_producer",
+        "kv_port": "30000",
+        "engine_id": "0",
+        "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+        "kv_connector_extra_config": {
+                  "use_ascend_direct": true,
+                  "prefill": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  },
+                  "decode": {
+                          "dp_size": 4,
+                          "tp_size": 4
+                  }
+            }
+        }'
+
+  -
+    server_cmd: >
+        vllm serve "Qwen/Qwen3-235B-A22B"
+        --host 0.0.0.0
+        --port $SERVER_PORT
+        --data-parallel-size 4
+        --data-parallel-size-local 4
+        --data-parallel-start-rank 0
+        --data-parallel-address $LOCAL_IP
+        --data-parallel-rpc-port 13389
+        --tensor-parallel-size 4
+        --seed 1024
+        --max-num-seqs 32
+        --max-model-len 8192
+        --max-num-batched-tokens 8192
+        --enable-expert-parallel
+        --trust-remote-code
+        --gpu-memory-utilization 0.9
+        --no-enable-prefix-caching
+        --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'
+        --async-scheduling
+        --kv-transfer-config
+        '{"kv_connector": "MooncakeConnectorV1",
+        "kv_role": "kv_consumer",
+        "kv_port": "30100",
+        "engine_id": "1",
+        "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+        "kv_connector_extra_config": {
+                  "use_ascend_direct": true,
+                  "prefill": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  },
+                  "decode": {
+                          "dp_size": 4,
+                          "tp_size": 4
+                  }
+            }
+        }'
+
+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: 2800
+    max_out_len: 1500
+    batch_size: 700
+    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: 512
+    baseline: 97
+    threshold: 3

tests/e2e/nightly/multi_node/config/Qwen3-VL-235B-disagg-pd.yaml

@@ -0,0 +1,108 @@
+test_name: "test Qwen3-VL-235B-A22B disaggregated_prefill"
+model: "Qwen/Qwen3-VL-235B-A22B-Instruct"
+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: 1
+  HCCL_OP_EXPANSION_MODE: "AIV"
+  TASK_QUEUE_ENABLE: 1
+  PYTORCH_NPU_ALLOC_CONF: expandable_segments:True
+
+disaggregated_prefill:
+  enabled: true
+  prefiller_host_index: [0]
+  decoder_host_index: [1]
+
+deployment:
+  -
+    server_cmd: >
+      vllm serve "Qwen/Qwen3-VL-235B-A22B-Instruct"
+      --host 0.0.0.0
+      --port $SERVER_PORT
+      --data-parallel-size 2
+      --data-parallel-size-local 2
+      --tensor-parallel-size 8
+      --seed 1024
+      --enable-expert-parallel
+      --max-num-seqs 32
+      --max-model-len 8192
+      --max-num-batched-tokens 8192
+      --trust-remote-code
+      --no-enable-prefix-caching
+      --gpu-memory-utilization 0.9
+      --kv-transfer-config
+      '{"kv_connector": "MooncakeConnectorV1",
+      "kv_role": "kv_producer",
+      "kv_port": "30000",
+      "engine_id": "0",
+      "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+      "kv_connector_extra_config": {
+                "prefill": {
+                        "dp_size": 2,
+                        "tp_size": 8
+                },
+                "decode": {
+                        "dp_size": 4,
+                        "tp_size": 4
+                }
+          }
+      }'
+  -
+    server_cmd: >
+      vllm serve "Qwen/Qwen3-VL-235B-A22B-Instruct"
+      --host 0.0.0.0
+      --port $SERVER_PORT
+      --data-parallel-size 4
+      --data-parallel-size-local 4
+      --tensor-parallel-size 4
+      --seed 1024
+      --enable-expert-parallel
+      --max-num-seqs 32
+      --max-model-len 8192
+      --max-num-batched-tokens 8192
+      --trust-remote-code
+      --no-enable-prefix-caching
+      --gpu-memory-utilization 0.9
+      --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'
+      --kv-transfer-config
+      '{"kv_connector": "MooncakeConnectorV1",
+      "kv_role": "kv_consumer",
+      "kv_port": "30200",
+      "engine_id": "1",
+      "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+      "kv_connector_extra_config": {
+                "prefill": {
+                        "dp_size": 2,
+                        "tp_size": 8
+                },
+                "decode": {
+                        "dp_size": 4,
+                        "tp_size": 4
+                }
+          }
+      }'
+benchmarks:
+  perf:
+    case_type: performance
+    dataset_path: vllm-ascend/textvqa-perf-1080p
+    request_conf: vllm_api_stream_chat
+    dataset_conf: textvqa/textvqa_gen_base64
+    num_prompts: 2800
+    max_out_len: 1500
+    batch_size: 64
+    request_rate: 11.2
+    baseline: 1
+    threshold: 0.97
+  acc:
+    case_type: accuracy
+    dataset_path: vllm-ascend/textvqa-lite
+    request_conf: vllm_api_stream_chat
+    dataset_conf: textvqa/textvqa_gen_base64
+    max_out_len: 7680
+    batch_size: 64
+    baseline: 85
+    threshold: 5

vllm_ascend/ascend_config.py

@@ -108,8 +108,12 @@ class AscendConfig:
                     decode_tp_size = min(decode_tp_size, num_kv_head)
                     self.pd_head_ratio = prefill_tp_size // decode_tp_size
                 except Exception:
-                    raise AssertionError(
+                    raise ValueError(
-                        "Can not get num_key_value_heads from model_config")
+                        "The text_config extracted from the model config does not have "
+                        "`num_key_value_heads` attribute. This indicates a mismatch "
+                        "between the model config and vLLM's expectations. Please "
+                        "ensure that the model config is compatible with vLLM."
+                    )
 
             if self.pd_tp_ratio == 0:
                 raise AssertionError(