[Disaggregated Prefill] P2P Disaggregated Prefill based on llm_datadist (#694)

### What this PR does / why we need it?
- This PR proposes a P2P version of Disaggregated Prefill based on
llm_datadist which manages data transfer.

- This solution reconstructs previous offline single-node Disaggregated
Prefill solution, and supports multi-node and online serveing now.

- Currently this solution supports 1P1D situation of Deepseek hybrid
parallelism (P: TP+EP, D: DP+EP). Note that xPyD situation is considered
in the solution design, and will be supported soon within v1 engine.

---------

Signed-off-by: hw_whx <wanghexiang7@huawei.com>
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
Co-authored-by: hw_whx <wanghexiang7@huawei.com>
Co-authored-by: ganyi <pleaplusone.gy@gmail.com>

examples/disaggregated_prefill/run_decode_server.sh

@@ -0,0 +1,37 @@
+export HCCL_IF_IP=2.0.0.0
+export GLOO_SOCKET_IFNAME="enp189s0f0"
+export TP_SOCKET_IFNAME="enp189s0f0"
+export HCCL_SOCKET_IFNAME="enp189s0f0"
+
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=100
+
+export VLLM_USE_V1=0
+
+export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+
+
+vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
+    --host 0.0.0.0 \
+    --port 20002 \
+    --tensor-parallel-size 8 \
+    --seed 1024 \
+    --served-model-name deepseek \
+    --max-model-len 2000 \
+    --max-num-batched-tokens 2000 \
+    --trust-remote-code \
+    --gpu-memory-utilization 0.9 \
+    --kv-transfer-config \
+    '{"kv_connector": "AscendSimpleConnector",
+    "kv_buffer_device": "npu",
+    "kv_role": "kv_consumer",
+    "kv_parallel_size": 8,
+    "kv_port":"21001",
+    "kv_connector_extra_config":
+    {"prompt_device_ips": ["1.2.3.1", "1.2.3.2", "1.2.3.3", "1.2.3.4", "1.2.3.5", "1.2.3.6", "1.2.3.7", "1.2.3.8"],
+    "decode_device_ips": ["1.2.3.9", "1.2.3.10", "1.2.3.11", "1.2.3.12", "1.2.3.13", "1.2.3.14", "1.2.3.15", "1.2.3.16"],
+    "llmdatadist_comm_port": 26000,
+    "proxy_ip":"3.0.0.0",
+    "proxy_port":"30001",
+    "http_port": 10002}
+    }'