[P/D] add layerwise connector CI

.github/workflows/vllm_ascend_test_nightly_a3.yaml

@@ -52,6 +52,9 @@ jobs:
           - name: multi-node-deepseek-pd
             config_file_path: DeepSeek-V3.yaml
             size: 2
+          - name: multi-node-deepseek-pd
+            config_file_path: DeepSeek-V3-layerwise.yaml
+            size: 2
           - name: multi-node-qwen3-dp
             config_file_path: Qwen3-235B-A3B.yaml
             size: 2
@@ -61,6 +64,9 @@ jobs:
           - name: multi-node-qwenw8a8-2node
             config_file_path: Qwen3-235B-W8A8.yaml
             size: 2
+          - name: multi-node-qwenw8a8-2node
+            config_file_path: Qwen3-235B-W8A8-layerwise.yaml
+            size: 2
           - name: multi-node-glm-2node
             config_file_path: GLM-4_5.yaml
             size: 2

tests/e2e/nightly/multi_node/config/models/DeepSeek-V3-layerwise.yaml

@@ -0,0 +1,112 @@
+# For disaggregated mode, set is_disaggregated: true, and set the following parameters:
+# Prefiller_index: the hosts index of the node running prefiller
+# Decoder_index: the hosts index of the node running decoder
+# Suppose we have **4 nodes** running a 2P1D setup (2 Prefillers + 1 Decoder):
+#   ┌───────────────┬───────────────┬───────────────┬───────────────┐
+#   │   node0       │   node1       │   node2       │   node3       │
+#   │ Prefiller #1  │ Prefiller #2  │   Decoder     │   Decoder     │
+#   └───────────────┴───────────────┴───────────────┴───────────────┘
+# For the prefiller nodes. the hosts should be node0 and node1
+# For the decoder nodes. we only have 1 decoder node(dp+tp+ep across node2 and node3. Where node3 is running with headless mode)
+# So the prefiller_host_index is [0, 1], and the decoder_host_index is [2]
+test_name: "test DeepSeek-V3 disaggregated_prefill"
+model: "vllm-ascend/DeepSeek-V3-W8A8"
+num_nodes: 2
+npu_per_node: 16
+env_common:
+  VLLM_USE_MODELSCOPE: true
+  OMP_PROC_BIND: false
+  OMP_NUM_THREADS: 100
+  HCCL_BUFFSIZE: 1024
+  SERVER_PORT: 8080
+  NUMEXPR_MAX_THREADS: 128
+  DISAGGREGATED_PREFILL_PROXY_SCRIPT: "examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py"
+# For None kubernetes deployment, list the IPs of all nodes used in order as follow
+# cluster_hosts: []
+disaggregated_prefill:
+  enabled: true
+  prefiller_host_index: [0]
+  decoder_host_index: [1]
+
+deployment:
+  -
+    server_cmd: >
+        vllm serve "vllm-ascend/DeepSeek-V3-W8A8"
+        --host 0.0.0.0
+        --port $SERVER_PORT
+        --data-parallel-size 2
+        --data-parallel-size-local 2
+        --tensor-parallel-size 8
+        --seed 1024
+        --enforce-eager
+        --enable-expert-parallel
+        --max-num-seqs 16
+        --max-model-len 8192
+        --max-num-batched-tokens 8192
+        --quantization ascend
+        --trust-remote-code
+        --no-enable-prefix-caching
+        --gpu-memory-utilization 0.9
+        --kv-transfer-config
+        '{"kv_connector": "MooncakeLayerwiseConnector",
+        "kv_role": "kv_producer",
+        "kv_port": "30000",
+        "engine_id": "0",
+        "kv_connector_module_path": "vllm_ascend.distributed.mooncake_layerwise_connector",
+        "kv_connector_extra_config": {
+                  "prefill": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  },
+                  "decode": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  }
+            }
+        }'
+
+  -
+    server_cmd: >
+        vllm serve "vllm-ascend/DeepSeek-V3-W8A8"
+        --host 0.0.0.0
+        --port $SERVER_PORT
+        --data-parallel-size 2
+        --data-parallel-size-local 2
+        --tensor-parallel-size 8
+        --seed 1024
+        --quantization ascend
+        --max-num-seqs 16
+        --max-model-len 8192
+        --max-num-batched-tokens 8192
+        --enable-expert-parallel
+        --trust-remote-code
+        --no-enable-prefix-caching
+        --gpu-memory-utilization 0.9
+        --additional-config '{"torchair_graph_config":{"enabled":true}}'
+        --kv-transfer-config
+        '{"kv_connector": "MooncakeLayerwiseConnector",
+        "kv_role": "kv_consumer",
+        "kv_port": "30200",
+        "engine_id": "1",
+        "kv_connector_module_path": "vllm_ascend.distributed.mooncake_layerwise_connector",
+        "kv_connector_extra_config": {
+                  "prefill": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  },
+                  "decode": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  }
+            }
+        }'
+benchmarks:
+  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: 4096
+    batch_size: 512
+    baseline: 95
+    threshold: 5

tests/e2e/nightly/multi_node/config/models/Qwen3-235B-W8A8-layerwise.yaml

@@ -0,0 +1,90 @@
+test_name: "test Qwen3-235B-A22B-W8A8 disaggregated_prefill"
+model: "vllm-ascend/Qwen3-235B-A22B-W8A8"
+num_nodes: 2
+npu_per_node: 16
+env_common:
+  VLLM_USE_MODELSCOPE: true
+  OMP_PROC_BIND: false
+  OMP_NUM_THREADS: 100
+  HCCL_BUFFSIZE: 1024
+  SERVER_PORT: 8080
+  NUMEXPR_MAX_THREADS: 128
+  DISAGGREGATED_PREFILL_PROXY_SCRIPT: "examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py"
+# For None kubernetes deployment, list the IPs of all nodes used in order as follow
+# cluster_hosts: []
+disaggregated_prefill:
+  enabled: true
+  prefiller_host_index: [0]
+  decoder_host_index: [1]
+
+deployment:
+  -
+    server_cmd: >
+        vllm serve "vllm-ascend/Qwen3-235B-A22B-W8A8"
+        --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 16
+        --max-model-len 8192
+        --max-num-batched-tokens 8192
+        --quantization ascend
+        --trust-remote-code
+        --no-enable-prefix-caching
+        --gpu-memory-utilization 0.9
+        --kv-transfer-config
+        '{"kv_connector": "MooncakeLayerwiseConnector",
+        "kv_role": "kv_producer",
+        "kv_port": "30000",
+        "engine_id": "0",
+        "kv_connector_module_path": "vllm_ascend.distributed.mooncake_layerwise_connector",
+        "kv_connector_extra_config": {
+                  "prefill": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  },
+                  "decode": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  }
+            }
+        }'
+
+  -
+    server_cmd: >
+        vllm serve "vllm-ascend/Qwen3-235B-A22B-W8A8"
+        --host 0.0.0.0
+        --port $SERVER_PORT
+        --data-parallel-size 2
+        --data-parallel-size-local 2
+        --tensor-parallel-size 8
+        --seed 1024
+        --quantization ascend
+        --max-num-seqs 16
+        --max-model-len 8192
+        --max-num-batched-tokens 8192
+        --enable-expert-parallel
+        --trust-remote-code
+        --no-enable-prefix-caching
+        --gpu-memory-utilization 0.9
+        --kv-transfer-config
+        '{"kv_connector": "MooncakeLayerwiseConnector",
+        "kv_role": "kv_consumer",
+        "kv_port": "30200",
+        "engine_id": "1",
+        "kv_connector_module_path": "vllm_ascend.distributed.mooncake_layerwise_connector",
+        "kv_connector_extra_config": {
+                  "prefill": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  },
+                  "decode": {
+                          "dp_size": 2,
+                          "tp_size": 8
+                  }
+            }
+        }'
+benchmarks: