# Ascend Store Deployment Guide ## KV Pool Parameter Description ### `kv_connector_extra_config`: Additional Configurable Parameters for Pooling | Parameter | Description | | :--- | :--- | | `lookup_rpc_port` | Port for RPC Communication Between Pooling Scheduler Process and Worker Process: Each Instance Requires a Unique Port Configuration. | | `load_async` | Whether to Enable Asynchronous Loading. The default value is false. | | `backend` | Set the storage backend for kvpool (`mooncake`, `memcache`, `yuanrong`), with the default being `mooncake`. | | `consumer_is_to_put` | Whether Decode node put KV Cache into KV Pool. The default value is false. | | `consumer_is_to_load` | Whether Decode node load KV cache from KV Pool. The default value is false. | | `prefill_pp_size` | Prefill PP size, needs to be set when Prefill node enables PP. | | `prefill_pp_layer_partition` | Prefill PP layer partition, needs to be set when Prefill node enables PP. | ### Environment Variable Configuration To guarantee uniform hash generation, it is required to synchronize the PYTHONHASHSEED environment variable across all nodes upon enabling KV Pool. ```bash export PYTHONHASHSEED=0 ``` ## Example of using Mooncake as a KV Pool backend * Software: * Check NPU HCCN Configuration: Ensure that the hccn.conf file exists in the environment. If using Docker, mount it into the container. ```bash cat /etc/hccn.conf ``` * Install Mooncake Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. Installation and Compilation Guide: . First, we need to obtain the Mooncake project. Refer to the following command: ```shell git clone -b v0.3.9 --depth 1 https://github.com/kvcache-ai/Mooncake.git ``` (Optional) Replace go install url if the network is poor ```shell cd Mooncake sed -i 's|https://go.dev/dl/|https://golang.google.cn/dl/|g' dependencies.sh ``` Install mpi ```shell apt-get install mpich libmpich-dev -y ``` Install the relevant dependencies. The installation of Go is not required. ```shell bash dependencies.sh -y ``` Compile and install ```shell mkdir build cd build cmake .. -DUSE_ASCEND_DIRECT=ON make -j make install ``` Set environment variables **Note:** * Adjust the Python path according to your specific Python installation * Ensure `/usr/local/lib` and `/usr/local/lib64` are in your `LD_LIBRARY_PATH` ```shell export LD_LIBRARY_PATH=/usr/local/lib64/python3.11/site-packages/mooncake:$LD_LIBRARY_PATH ``` ### Environment Variables Description | Hardware | HDK & CANN versions | Export Command | Description | | :--- | :--- | :--- | :--- | | 800 I/T A3 series | HDK >= 25.5
CANN >= 9.0.0
LingQu Computing Network >= 1.5 | `export ASCEND_ENABLE_USE_FABRIC_MEM=1` | **Recommended**. Enables unified memory address direct transmission scheme. | | 800 I/T A3 series | 25.5.0<=HDK<26.0.0 | `export ASCEND_BUFFER_POOL=4:8` | Configures the number and size of buffers on the NPU Device for aggregation and KV transfer (e.g., `4:8` means 4 buffers of 8MB). | | 800 I/T A2 series | N/A | `export HCCL_INTRA_ROCE_ENABLE=1` | Required by direct transmission scheme on 800 I/T A2 series| ### FAQ for HIXL (ascend_direct) backend For common troubleshooting and issue localization guidance for HIXL (ascend_direct), see: ### Run Mooncake Master #### 1.Configure mooncake.json The environment variable **MOONCAKE_CONFIG_PATH** is configured to the full path where mooncake.json is located. ```shell { "metadata_server": "P2PHANDSHAKE", "protocol": "ascend", "device_name": "", "master_server_address": "xx.xx.xx.xx:50088", "global_segment_size": "1GB" (1024MB/1048576KB/1073741824B/1073741824), "preferred_segment": false, "prefer_alloc_in_same_node": true } ``` **metadata_server**: Configured as **P2PHANDSHAKE**. **protocol:** Must be set to 'Ascend' on the NPU. **device_name**: "" **master_server_address**: Configured with the IP and port of the master service. It can also be set via the **MOONCAKE_MASTER** environment variable, which takes precedence over this configuration item (useful for injecting the master address through Kubernetes). **global_segment_size**: Registered memory size per card to the KV Pool. **Needs to be aligned to 1GB.** It can also be set via the **MOONCAKE_GLOBAL_SEGMENT_SIZE** environment variable, which takes precedence over this configuration item. **preferred_segment**: Whether to prefer storing KV on the local segment when putting objects to the KV Pool. Defaults to **false**. **prefer_alloc_in_same_node**: Whether to prefer allocating KV on the same node. Defaults to **true**. #### 2.Start mooncake_master Under the mooncake folder: ```shell mooncake_master --port 50088 --eviction_high_watermark_ratio 0.9 --eviction_ratio 0.1 --default_kv_lease_ttl 11000 ``` `eviction_high_watermark_ratio` determines the watermark where Mooncake Store will perform eviction，and `eviction_ratio` determines the portion of stored objects that would be evicted. `default_kv_lease_ttl` controls the default lease TTL for KV objects (milliseconds); configure it via `--default_kv_lease_ttl` and keep it larger than `ASCEND_CONNECT_TIMEOUT` and `ASCEND_TRANSFER_TIMEOUT`. ### PD Disaggregation Scenario #### 1.Run `prefill` Node and `decode` Node Using `MultiConnector` to simultaneously utilize both `MooncakeConnectorV1` and `AscendStoreConnector`. `MooncakeConnectorV1` performs kv_transfer, while `AscendStoreConnector` serves as the prefix-cache node. `prefill` Node： ```shell bash multi_producer.sh ``` The content of the multi_producer.sh script: ```shell export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH export PYTHONHASHSEED=0 export PYTHONPATH=$PYTHONPATH:/xxxxx/vllm export MOONCAKE_CONFIG_PATH="/xxxxxx/mooncake.json" export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 export ACL_OP_INIT_MODE=1 #A3 export ASCEND_ENABLE_USE_FABRIC_MEM=1 #A2 #export HCCL_INTRA_ROCE_ENABLE=1 #Minimum retransmission timeout of the RDMA, equals 4.096 μs * 2 ^ timeout. #Needs to satisfy the equation: ASCEND_TRANSFER_TIMEOUT > RDMA_TIMEOUT * 7, where 7 is the default number of retry for RDMA transfer. #HCCL_RDMA_TIMEOUT also affects collective communication behavior and should be configured carefully. export HCCL_RDMA_TIMEOUT=17 # Unit: ms. The timeout for one-sided communication connection establishment is set to 10 seconds by default (see PR: https://github.com/kvcache-ai/Mooncake/pull/1039). Users can adjust this value based on their specific setup. # The recommended formula is: ASCEND_CONNECT_TIMEOUT = connection_time_per_card (typically within 500ms) × total_number_of_Decode_cards. # This ensures that even in the worst-case scenario—where all Decode cards simultaneously attempt to connect to the same Prefill card the connection will not time out. export ASCEND_CONNECT_TIMEOUT=10000 # Unit: ms. The timeout for one-sided communication transfer is set to 10 seconds by default (see PR: https://github.com/kvcache-ai/Mooncake/pull/1039). export ASCEND_TRANSFER_TIMEOUT=10000 python3 -m vllm.entrypoints.openai.api_server \ --model /xxxxx/Qwen2.5-7B-Instruct \ --port 8100 \ --trust-remote-code \ --enforce-eager \ --no-enable-prefix-caching \ --tensor-parallel-size 1 \ --data-parallel-size 1 \ --max-model-len 32768 \ --block-size 128 \ --max-num-batched-tokens 16384 \ --kv-transfer-config \ '{ "kv_connector": "MultiConnector", "kv_role": "kv_producer", "kv_connector_extra_config": { "connectors": [ { "kv_connector": "MooncakeConnectorV1", "kv_role": "kv_producer", "kv_port": "20001", "kv_connector_extra_config": { "prefill": { "dp_size": 1, "tp_size": 1 }, "decode": { "dp_size": 1, "tp_size": 1 } } }, { "kv_connector": "AscendStoreConnector", "kv_role": "kv_producer", "kv_connector_extra_config": { "lookup_rpc_port":"0", "backend": "mooncake" } } ] } }' ``` `decode` Node： ```shell bash multi_consumer.sh ``` The content of multi_consumer.sh: ```shell export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH export PYTHONPATH=$PYTHONPATH:/xxxxx/vllm export PYTHONHASHSEED=0 export MOONCAKE_CONFIG_PATH="/xxxxx/mooncake.json" export ASCEND_RT_VISIBLE_DEVICES=4,5,6,7 export ACL_OP_INIT_MODE=1 #A3 export ASCEND_ENABLE_USE_FABRIC_MEM=1 #A2 #export HCCL_INTRA_ROCE_ENABLE=1 export HCCL_RDMA_TIMEOUT=17 export ASCEND_CONNECT_TIMEOUT=10000 export ASCEND_TRANSFER_TIMEOUT=10000 python3 -m vllm.entrypoints.openai.api_server \ --model /xxxxx/Qwen2.5-7B-Instruct \ --port 8200 \ --trust-remote-code \ --enforce-eager \ --no-enable-prefix-caching \ --tensor-parallel-size 1 \ --data-parallel-size 1 \ --max-model-len 32768 \ --block-size 128 \ --max-num-batched-tokens 16384 \ --kv-transfer-config \ '{ "kv_connector": "MultiConnector", "kv_role": "kv_consumer", "kv_connector_extra_config": { "connectors": [ { "kv_connector": "MooncakeConnectorV1", "kv_role": "kv_consumer", "kv_port": "20002", "kv_connector_extra_config": { "prefill": { "dp_size": 1, "tp_size": 1 }, "decode": { "dp_size": 1, "tp_size": 1 } } }, { "kv_connector": "AscendStoreConnector", "kv_role": "kv_consumer", "kv_connector_extra_config": { "lookup_rpc_port":"0", "backend": "mooncake" } } ] } }' ``` Currently, the key-value pool in PD Disaggregate only stores the kv cache generated by the Prefill node by default. In models using MLA, it is now supported that the Decode node stores the kv cache for use by the Prefill node, enabled by adding `consumer_is_to_put: true` to the AscendStoreConnector. If the Prefill node enables PP, `prefill_pp_size` or `prefill_pp_layer_partition` also needs to be set. Example as follows: ```python { "kv_connector": "AscendStoreConnector", "kv_role": "kv_consumer", "kv_connector_extra_config": { "lookup_rpc_port": "0", "backend": "mooncake", "consumer_is_to_put": true, "prefill_pp_size": 2, "prefill_pp_layer_partition": "30,31" } } ``` #### 2、Start proxy_server ```shell python vllm-ascend/examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py \ --host localhost\ --prefiller-hosts localhost \ --prefiller-ports 8100 \ --decoder-hosts localhost\ --decoder-ports 8200 \ ``` Change localhost to your actual IP address. #### 3.Run Inference Configure the localhost, port, and model weight path in the command to your own settings. Short question: ```shell curl -s http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{ "model": "/xxxxx/Qwen2.5-7B-Instruct", "prompt": "Hello. I have a question. The president of the United States is", "max_completion_tokens": 200, "temperature":0.0 }' ``` Long question: ```shell curl -s http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{ "model": "/xxxxx/Qwen2.5-7B-Instruct", "prompt": "Given the accelerating impacts of climate change—including rising sea levels, increasing frequency of extreme weather events, loss of biodiversity, and adverse effects on agriculture and human health—there is an urgent need for a robust, globally coordinated response. However, international efforts are complicated by a range of factors: economic disparities between high-income and low-income countries, differing levels of industrialization, varying access to clean energy technologies, and divergent political systems that influence climate policy implementation. In this context, how can global agreements like the Paris Accord be redesigned or strengthened to not only encourage but effectively enforce emission reduction targets? Furthermore, what mechanisms can be introduced to promote fair and transparent technology transfer, provide adequate financial support for climate adaptation in vulnerable regions, and hold nations accountable without exacerbating existing geopolitical tensions or disproportionately burdening those with historically lower emissions?", "max_completion_tokens": 256, "temperature":0.0 }' ``` ### PD-Mixed Inference #### 1.Run Mixed Department Script ```shell bash pd_mix.sh ``` Content of pd_mix.sh: ```shell export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$LD_LIBRARY_PATH export PYTHONPATH=$PYTHONPATH:/xxxxx/vllm export MOONCAKE_CONFIG_PATH="/xxxxxx/mooncake.json" export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 export PYTHONHASHSEED=0 export ACL_OP_INIT_MODE=1 #A3 export ASCEND_ENABLE_USE_FABRIC_MEM=1 #A2 #export HCCL_INTRA_ROCE_ENABLE=1 export HCCL_RDMA_TIMEOUT=17 export ASCEND_CONNECT_TIMEOUT=10000 export ASCEND_TRANSFER_TIMEOUT=10000 python3 -m vllm.entrypoints.openai.api_server \ --model /xxxxx/Qwen2.5-7B-Instruct \ --port 8100 \ --trust-remote-code \ --enforce-eager \ --no-enable-prefix-caching \ --tensor-parallel-size 1 \ --data-parallel-size 1 \ --max-model-len 32768 \ --block-size 128 \ --max-num-batched-tokens 16384 \ --kv-transfer-config \ '{ "kv_connector": "AscendStoreConnector", "kv_role": "kv_both", "kv_connector_extra_config": { "lookup_rpc_port":"1", "backend": "mooncake" } }' > mix.log 2>&1 ``` #### 2.Run Inference Configure the localhost, port, and model weight path in the command to your own settings. The requests sent will only go to the port where the mixed deployment script is located, and there is no need to start a separate proxy. Short question: ```shell curl -s http://localhost:8100/v1/completions -H "Content-Type: application/json" -d '{ "model": "/xxxxx/Qwen2.5-7B-Instruct", "prompt": "Hello. I have a question. The president of the United States is", "max_completion_tokens": 200, "temperature":0.0 }' ``` Long question: ```shell curl -s http://localhost:8100/v1/completions -H "Content-Type: application/json" -d '{ "model": "/xxxxx/Qwen2.5-7B-Instruct", "prompt": "Given the accelerating impacts of climate change—including rising sea levels, increasing frequency of extreme weather events, loss of biodiversity, and adverse effects on agriculture and human health—there is an urgent need for a robust, globally coordinated response. However, international efforts are complicated by a range of factors: economic disparities between high-income and low-income countries, differing levels of industrialization, varying access to clean energy technologies, and divergent political systems that influence climate policy implementation. In this context, how can global agreements like the Paris Accord be redesigned or strengthened to not only encourage but effectively enforce emission reduction targets? Furthermore, what mechanisms can be introduced to promote fair and transparent technology transfer, provide adequate financial support for climate adaptation in vulnerable regions, and hold nations accountable without exacerbating existing geopolitical tensions or disproportionately burdening those with historically lower emissions?", "max_completion_tokens": 256, "temperature":0.0 }' ``` Note: For MooncakeStore with `ASCEND_BUFFER_POOL` enabled, it is recommended to perform a warm-up phase before running actual performance benchmarks. This is because HCCL one-sided communication connections are created lazily after the instance is launched when Device-to-Device communication is involved. Currently, full-mesh connections between all devices are required. Establishing these connections introduces a one-time time overhead and persistent device memory consumption (4 MB of device memory per connection). **For warm-up, it is recommended to issue requests with an input sequence length of 8K and an output sequence length of 1, with the total number of requests being 2–3× the number of devices (cards/dies).** ## Example of using Memcache as a KV Pool backend ### Installing Memcache **MemCache depends on MemFabric. Therefore, MemFabric must be installed.Installing the memcache after the memfabric is installed.** ```shell pip install memfabric-hybrid pip install memcache-hybrid ``` ### Configuring the memcache Config File **mmc-meta.conf：** ```shell ock.mmc.meta_service_url = tcp://xx.xx.xx.xx:5000 ock.mmc.meta_service.config_store_url = tcp://xx.xx.xx.xx:6000 ock.mmc.log_level = error ``` **mmc-local.conf：** ```shell ock.mmc.meta_service_url = tcp://xx.xx.xx.xx:5000 ock.mmc.local_service.config_store_url = tcp://xx.xx.xx.xx:6000 ock.mmc.log_level = error ock.mmc.local_service.world_size = 256 ock.mmc.local_service.protocol = device_sdma ock.mmc.local_service.dram.size = 1GB ``` **Key Focuses：** | Parameter | Description | | :--- | :--- | | `ock.mmc.meta_service_url` | Configure the IP address and port number of the master node. The IP address and port number of the P node and D node can be the same. | | `ock.mmc.local_service.config_store_url` | Configure the IP address and port number of the master node. The IP address and port number of the P node and D node can be the same. | | `ock.mmc.local_service.world_size` | Total count of local service, including services that will be added in the future. | | `ock.mmc.local_service.protocol` | `device_rdma` (supported for A2 and A3 when device ROCE available, recommended for A2), `device_sdma` (supported for A3 when HCCS available, recommended for A3). Currently does not support heterogeneous protocol setting.| | `ock.mmc.local_service.dram.size` | Sets the size of the memory occupied by the master. The configured value is the size of the memory occupied by each card. | ### Run Memcache Master Starting the MetaService service. ```shell export MMC_META_CONFIG_PATH=/usr/local/memcache_hybrid/latest/config/mmc-meta.conf python -c "from memcache_hybrid import MetaService; MetaService.main()" ``` ### PD Disaggregation Scenario #### 1.Run `prefill` Node and `decode` Node Using `MultiConnector` to simultaneously utilize both `MooncakeConnectorV1` and `AscendStoreConnector`. `MooncakeConnectorV1` performs kv_transfer, while `AscendStoreConnector` enables KV Cache Pool #### 800I A2/800T A2/800I A3/800T A3 Series **run_prefill.sh/run_decode.sh:** ```shell #!/bin/bash ROLE="prefill" # prefill / decode HARDWARE_SERIES="A2" # A2 (800I/800T A2) or A3 (800I/800T A3) LOCAL_IP="xx.xx.xx.xx" NIC_NAME="xxxxxx" MODEL_PATH="xxxxxxx/Qwen3-32B" SERVED_MODEL_NAME="qwen3" DATA_PARALLEL_SIZE=1 TENSOR_PARALLEL_SIZE=8 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export MMC_LOCAL_CONFIG_PATH=/home/memcache/mmc-local.conf if [ "$ROLE" == "prefill" ]; then KV_ROLE="kv_producer" KV_PORT="20001" LOOKUP_RPC_PORT="0" else KV_ROLE="kv_consumer" KV_PORT="20002" LOOKUP_RPC_PORT="1" fi echo "Starting vLLM on Series: $HARDWARE_SERIES, Role: $ROLE" rm -rf /root/ascend/log/* rm -rf ./connector.log if [ "$HARDWARE_SERIES" == "A2" ]; then echo 200000 > /proc/sys/vm/nr_hugepages export HCCL_IF_IP=$LOCAL_IP export GLOO_SOCKET_IFNAME=$NIC_NAME export TP_SOCKET_IFNAME=$NIC_NAME export HCCL_SOCKET_IFNAME=$NIC_NAME elif [ "$HARDWARE_SERIES" == "A3" ]; then export ACL_OP_INIT_MODE=1 else echo "Error: Invalid HARDWARE_SERIES. Set to 'A2' or 'A3'." exit 1 fi source /usr/local/Ascend/ascend-toolkit/set_env.sh source /usr/local/Ascend/nnal/atb/set_env.sh export PYTHONHASHSEED=0 export HCCL_BUFFSIZE=1024 export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_USE_V1=1 KV_CONFIG='{ "kv_connector": "MultiConnector", "kv_role": "'$KV_ROLE'", "engine_id": "2", "kv_connector_extra_config": { "connectors": [ { "kv_connector": "MooncakeConnectorV1", "kv_role": "'$KV_ROLE'", "kv_port": "'$KV_PORT'", "kv_connector_extra_config": { "prefill": { "dp_size": '$DATA_PARALLEL_SIZE', "tp_size": '$TENSOR_PARALLEL_SIZE' }, "decode": { "dp_size": '$DATA_PARALLEL_SIZE', "tp_size": '$TENSOR_PARALLEL_SIZE' } } }, { "kv_connector": "AscendStoreConnector", "kv_role": "'$KV_ROLE'", "kv_connector_extra_config": { "backend": "memcache", "lookup_rpc_port": "'$LOOKUP_RPC_PORT'" } } ] } }' CMD_ARGS=( --model "$MODEL_PATH" --served-model-name "$SERVED_MODEL_NAME" --trust-remote-code --enforce-eager --data-parallel-size "$DATA_PARALLEL_SIZE" --tensor-parallel-size "$TENSOR_PARALLEL_SIZE" --port 30050 --max-num_seqs 20 --max-model-len 32768 --max-num-batched-tokens 16384 --gpu-memory-utilization 0.9 --kv-transfer-config "$KV_CONFIG" ) python -m vllm.entrypoints.openai.api_server "${CMD_ARGS[@]}" > log_${ROLE}.log 2>&1 echo "vLLM started. Log file: log_${ROLE}.log" ``` #### [2、Start proxy_server](#2start-proxy_server) #### [3、run-inference](#3run-inference) ### PD-Mixed Scenario #### 1.Run Mixed Department Script #### 800I A2/800T A2/800I A3/800T A3 Series **Run_pd_mix.sh:** ```shell #!/bin/bash HARDWARE_SERIES="A2" # A2 (800I/800T A2) or A3 (800I/800T A3) LOCAL_IP="xx.xx.xx.xx" NIC_NAME="xxxxxx" MODEL_PATH="xxxxxxx/Qwen3-32B" SERVED_MODEL_NAME="qwen3" DATA_PARALLEL_SIZE=1 TENSOR_PARALLEL_SIZE=8 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export MMC_LOCAL_CONFIG_PATH=/home/memcache/mmc-local.conf echo "Starting vLLM on Series: $HARDWARE_SERIES" rm -rf /root/ascend/log/* rm -rf ./connector.log if [ "$HARDWARE_SERIES" == "A2" ]; then echo 200000 > /proc/sys/vm/nr_hugepages export HCCL_IF_IP=$LOCAL_IP export GLOO_SOCKET_IFNAME=$NIC_NAME export TP_SOCKET_IFNAME=$NIC_NAME export HCCL_SOCKET_IFNAME=$NIC_NAME elif [ "$HARDWARE_SERIES" == "A3" ]; then export ACL_OP_INIT_MODE=1 else echo "Error: Invalid HARDWARE_SERIES. Set to 'A2' or 'A3'." exit 1 fi source /usr/local/Ascend/ascend-toolkit/set_env.sh source /usr/local/Ascend/nnal/atb/set_env.sh export PYTHONHASHSEED=0 export HCCL_BUFFSIZE=1024 export OMP_PROC_BIND=false export OMP_NUM_THREADS=10 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export VLLM_USE_V1=1 KV_CONFIG='{ "kv_connector": "AscendStoreConnector", "kv_role": "kv_both", "kv_connector_extra_config": { "backend": "memcache", "lookup_rpc_port": "0" } }' CMD_ARGS=( --model "$MODEL_PATH" --served-model-name "$SERVED_MODEL_NAME" --trust-remote-code --enforce-eager --data-parallel-size "$DATA_PARALLEL_SIZE" --tensor-parallel-size "$TENSOR_PARALLEL_SIZE" --port 30050 --max-num_seqs 20 --max-model-len 32768 --max-num-batched-tokens 16384 --gpu-memory-utilization 0.9 --kv-transfer-config "$KV_CONFIG" ) python -m vllm.entrypoints.openai.api_server "${CMD_ARGS[@]}" > log_mix.log 2>&1 echo "vLLM started. Log file: log_mix.log" ``` #### [2.Run Inference](#2run-inference) ## Example of using Yuanrong as a KV Pool backend * Software: * Install `openyuanrong-datasystem` on all nodes (`yr.datasystem` must be importable). ### Install Yuanrong Datasystem ```bash pip install openyuanrong-datasystem ``` If the prebuilt package does not match the CANN or Ascend driver version in your environment, build Yuanrong Datasystem from source in the vLLM Ascend image. Follow the official Yuanrong Datasystem build instructions: ### Start etcd Yuanrong Datasystem uses etcd for service discovery. The following example starts a single-node etcd cluster: ```bash ETCD_VERSION="v3.5.12" ETCD_IP="127.0.0.1" if [ "$(uname -m)" = "aarch64" ]; then ETCD_ARCH="linux-arm64" else ETCD_ARCH="linux-amd64" fi wget https://github.com/etcd-io/etcd/releases/download/${ETCD_VERSION}/etcd-${ETCD_VERSION}-${ETCD_ARCH}.tar.gz tar -xvf etcd-${ETCD_VERSION}-${ETCD_ARCH}.tar.gz cd etcd-${ETCD_VERSION}-${ETCD_ARCH} sudo cp etcd etcdctl /usr/local/bin/ etcd \ --name etcd-single \ --data-dir /tmp/etcd-data \ --listen-client-urls http://0.0.0.0:2379 \ --advertise-client-urls http://${ETCD_IP}:2379 \ --listen-peer-urls http://0.0.0.0:2380 \ --initial-advertise-peer-urls http://${ETCD_IP}:2380 \ --initial-cluster etcd-single=http://${ETCD_IP}:2380 & etcdctl --endpoints "${ETCD_IP}:2379" put key "value" etcdctl --endpoints "${ETCD_IP}:2379" get key ``` For production environments, refer to the official etcd clustering documentation: ### Start Datasystem Worker Start a Datasystem worker on each node by using `dscli`: ```bash dscli start -w \ --worker_address "${WORKER_IP}:31501" \ --etcd_address "${ETCD_IP}:2379" \ --shared_memory_size_mb 40960 \ --enable_worker_worker_batch_get=true ``` The `--worker_address` value is consumed later by `DS_WORKER_ADDR`, so keep the host and port identical on the same node. For more parameters, refer to the `dscli` usage documentation on the Yuanrong Datasystem official site: To stop the worker: ```bash dscli stop --worker_address "${WORKER_IP}:31501" ``` ### Environment Variable Configuration Set the following environment variables on each node before starting vLLM: | Variable | Required | Default | Description | | :--- | :--- | :--- | :--- | | `PYTHONHASHSEED` | Yes | `0` | Must be consistent across all nodes to guarantee uniform hash generation. | | `DS_WORKER_ADDR` | Yes | N/A | Datasystem worker address in `:` format. This must match the local `dscli start --worker_address` value. | | `DS_ENABLE_EXCLUSIVE_CONNECTION` | No | `0` | Passed to Yuanrong `HeteroClient.enable_exclusive_connection`. Use `1` to enable the exclusive connection mode when required by your deployment. | | `DS_ENABLE_REMOTE_H2D` | No | `0` | Passed to Yuanrong `HeteroClient.enable_remote_h2d`. Use `1` only after the Remote H2D requirements below are met. | ```bash export PYTHONHASHSEED=0 export DS_WORKER_ADDR="${WORKER_IP}:31501" export DS_ENABLE_EXCLUSIVE_CONNECTION=0 export DS_ENABLE_REMOTE_H2D=0 ``` #### Remote H2D Requirements Set `DS_ENABLE_REMOTE_H2D=1` only when Remote Host-to-Device transfer is enabled and verified in the Yuanrong Datasystem deployment: * Reserve enough 2 MiB HugeTLB pages before starting the worker. For 40 GiB shared memory, reserve at least 20480 2 MiB huge pages. * Start each Datasystem worker with Remote H2D enabled. The worker start command must include `--remote_h2d_device_ids`, `--enable_huge_tlb true`, `--arena_per_tenant 1`, and `--enable_fallocate false`. Using multiple available NPU device IDs is recommended, for example `"0,1,2,3,4,5,6,7"` on an 8-NPU node. ```bash dscli start -w \ --worker_address "${WORKER_IP}:31501" \ --etcd_address "${ETCD_IP}:2379" \ --shared_memory_size_mb 40960 \ --arena_per_tenant 1 \ --enable_huge_tlb true \ --enable_fallocate false \ --remote_h2d_device_ids "0,1,2,3,4,5,6,7" \ --enable_worker_worker_batch_get=true ``` * Make sure the NPU driver, firmware, and CANN toolkit required by Yuanrong Remote H2D are installed and visible to the worker process. In containers, mount the Ascend driver path, `npu-smi`, `hccn_tool`, `/etc/hccn.conf`, `/etc/ascend_install.info`, and the required `/dev/davinci*` devices. * Verify the NPU and RoCE environment before enabling the client flag: ```bash # Check the current 2 MiB HugeTLB page size, total count, and free count. grep -E "HugePages_Total|HugePages_Free|Hugepagesize" /proc/meminfo # Optional: check 2 MiB HugeTLB pages on each NUMA node. for node in /sys/devices/system/node/node*/hugepages/hugepages-2048kB; do echo "$node total=$(cat "$node/nr_hugepages") free=$(cat "$node/free_hugepages")" done # Check that NPU devices and the driver are visible to the worker environment. npu-smi info # Check that the NPU topology is visible. npu-smi info -t topo # Check optical module detection on the selected local NPU. hccn_tool -i -optical -g # Check RoCE physical link status. The expected link status is UP. for i in {0..7}; do hccn_tool -i $i -link -g; done # Check the selected NPU IP address and reachability to the remote NPU. hccn_tool -i -ip -g hccn_tool -i -ping -g address ``` If these checks fail, keep `DS_ENABLE_REMOTE_H2D=0` and use the default Datasystem transfer path. ### Run AscendStoreConnector with Yuanrong backend Use `AscendStoreConnector` with `backend: "yuanrong"`: ```bash python3 -m vllm.entrypoints.openai.api_server \ --model /xxxxx/Qwen2.5-7B-Instruct \ --port 8100 \ --trust-remote-code \ --enforce-eager \ --no-enable-prefix-caching \ --tensor-parallel-size 1 \ --data-parallel-size 1 \ --max-model-len 10000 \ --block-size 128 \ --max-num-batched-tokens 4096 \ --kv-transfer-config \ '{ "kv_connector": "AscendStoreConnector", "kv_role": "kv_both", "kv_connector_extra_config": { "lookup_rpc_port": "1", "backend": "yuanrong" } }' ``` `lookup_rpc_port` is the RPC port used between the pooling scheduler process and the worker process. Each instance must use a unique port value. ### Notes * The Yuanrong backend normalizes KV keys before calling Datasystem. Keys longer than 255 characters or containing unsupported characters are rewritten, so do not rely on the raw key string when debugging backend storage. * No extra buffer pre-registration step is required for Yuanrong. The backend uses device pointers directly when building blob lists. #### [2.Run Inference](#2run-inference)