support kimi

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

Author: Potabk

docs/source/assets/multi_node_dp.png

@@ -13,4 +13,5 @@ multi_npu_qwen3_moe
 multi_npu_quantization
 single_node_300i
 multi_node
+multi_node_kimi
 :::

docs/source/assets/multi_node_dp_deepseek.png

@@ -178,7 +178,7 @@ vllm serve /root/.cache/ds_v3 \
 ```
 
 The Deployment view looks like:
-![alt text](../assets/multi_node_dp.png)
+![alt text](../assets/multi_node_dp_deepseek.png)
 
 Once your server is started, you can query the model with input prompts:
 

docs/source/assets/multi_node_dp_kimi.png

@@ -0,0 +1,153 @@
+# Multi-Node-DP (Kimi-K2)
+
+## Verify Multi-Node Communication Environment
+
+referring to [multi_node.md](https://vllm-ascend.readthedocs.io/en/latest/tutorials/multi_node.html#verification-process)
+
+## Run with docker
+Assume you have two Atlas 800 A3(64G*16) nodes, and want to deploy the `Kimi-K2-Instruct-W8A8` quantitative model across multi-node.
+
+```{code-block} bash
+   :substitutions:
+# Update the vllm-ascend image
+export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+export NAME=vllm-ascend
+
+# Run the container using the defined variables
+# Note if you are running bridge network with docker, Please expose available ports for multiple nodes communication in advance
+docker run --rm \
+--name $NAME \
+--net=host \
+--device /dev/davinci0 \
+--device /dev/davinci1 \
+--device /dev/davinci2 \
+--device /dev/davinci3 \
+--device /dev/davinci4 \
+--device /dev/davinci5 \
+--device /dev/davinci6 \
+--device /dev/davinci7 \
+--device /dev/davinci8 \
+--device /dev/davinci9 \
+--device /dev/davinci10 \
+--device /dev/davinci11 \
+--device /dev/davinci12 \
+--device /dev/davinci13 \
+--device /dev/davinci14 \
+--device /dev/davinci15 \
+--device /dev/davinci_manager \
+--device /dev/devmm_svm \
+--device /dev/hisi_hdc \
+-v /usr/local/dcmi:/usr/local/dcmi \
+-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
+-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /mnt/sfs_turbo/.cache:/home/cache \
+-it $IMAGE bash
+```
+
+:::{note}
+Before launch the inference server, ensure some environment variables are set for multi node communication
+:::
+
+Run the following scripts on two nodes respectively
+
+**node0**
+
+```shell
+#!/bin/sh
+
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip
+nic_name="enp23s0f3"
+local_ip="172.22.0.218"
+
+export HCCL_IF_IP=$local_ip
+export GLOO_SOCKET_IFNAME=$nic_name
+export TP_SOCKET_IFNAME=$nic_name
+export HCCL_SOCKET_IFNAME=$nic_name
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=100
+export VLLM_USE_V1=1
+export HCCL_BUFFSIZE=1024
+
+# The w8a8 weight can obtained from https://www.modelscope.cn/models/vllm-ascend/Kimi-K2-Instruct-W8A8
+# If you want to the quantization manually, please refer to https://vllm-ascend.readthedocs.io/en/latest/user_guide/feature_guide/quantization.html
+vllm serve /home/cache/weights/Kimi-K2-Instruct-W8A8 \
+--host 0.0.0.0 \
+--port 8004 \
+--data-parallel-size 4 \
+--api-server-count 2 \
+--data-parallel-size-local 2 \
+--data-parallel-address $local_ip \
+--data-parallel-rpc-port 13389 \
+--seed 1024 \
+--served-model-name kimi \
+--quantization ascend \
+--tensor-parallel-size 8 \
+--enable-expert-parallel \
+--max-num-seqs 16 \
+--max-model-len 32768 \
+--max-num-batched-tokens 4096 \
+--trust-remote-code \
+--no-enable-prefix-caching \
+--gpu-memory-utilization 0.9 \
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
+```
+
+**node1**
+
+```shell
+#!/bin/sh
+
+nic_name="enp23s0f3"
+local_ip="172.22.0.155"
+
+export HCCL_IF_IP=$local_ip
+export GLOO_SOCKET_IFNAME=$nic_name
+export TP_SOCKET_IFNAME=$nic_name
+export HCCL_SOCKET_IFNAME=$nic_name
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=100
+export VLLM_USE_V1=1
+export HCCL_BUFFSIZE=1024
+
+vllm serve /home/cache/weights/Kimi-K2-Instruct-W8A8 \
+--host 0.0.0.0 \
+--port 8004 \
+--headless \
+--data-parallel-size 4 \
+--data-parallel-size-local 2 \
+--data-parallel-start-rank 2 \
+--data-parallel-address 172.22.0.218 \
+--data-parallel-rpc-port 13389 \
+--seed 1024 \
+--tensor-parallel-size 8 \
+--served-model-name kimi \
+--max-num-seqs 16 \
+--max-model-len 32768 \
+--quantization ascend \
+--max-num-batched-tokens 4096 \
+--enable-expert-parallel \
+--trust-remote-code \
+--no-enable-prefix-caching \
+--gpu-memory-utilization 0.92 \
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
+```
+
+The Deployment view looks like:
+![alt text](../assets/multi_node_dp_kimi.png)
+
+Once your server is started, you can query the model with input prompts:
+
+```shell
+curl http://{ node0 ip:8004 }/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "kimi",
+        "prompt": "The future of AI is",
+        "max_tokens": 50,
+        "temperature": 0
+    }'
+```

docs/source/tutorials/index.md

@@ -17,7 +17,7 @@
 
 from vllm.logger import logger
 
-TORCHAIR_MODEL_LIST = ["deepseek", "pangu"]
+TORCHAIR_MODEL_LIST = ["deepseek", "pangu", "kimi_k2"]
 
 
 def _check_torchair_supported(model_type: str):

docs/source/tutorials/multi_node.md

@@ -93,12 +93,15 @@ def apply_patch(target_module, target_function, wrappers):
         if target_function is not None:
             setattr(original_module, target_function, candidate)
 
-        for key, value in sys.modules.copy().items():
-            if (target_function is not None
-                    and hasattr(value, target_function)
-                    and id(getattr(value,
-                                   target_function)) == original_function_id):
-                setattr(value, target_function, candidate)
+        for _, value in sys.modules.copy().items():
+            if target_function is None:
+                continue
+            try:
+                attr = getattr(value, target_function, None)
+                if attr is not None and id(attr) == original_function_id:
+                    setattr(value, target_function, candidate)
+            except ImportError:
+                continue
 
     @staticmethod
     def parse_path(module_path, function_name, create_dummy):