DeepSeek-V4-Pro

简介

DeepSeek-V4在DeepSeek-V3基础上引入了多项关键升级。

• 流形约束超连接(mHC)增强传统残差连接；

• 混合注意力架构，通过Compress-4-Attention和Compress-128-Attention大幅提升长上下文效率。对于混合专家(MoE)组件，仍采用DeepSeekMoE架构，仅做少量调整。

DeepSeek-V4-Pro作为DeepSeek-V4-Pro的最大推理努力模式，显著提升了开源模型的知识能力，牢固确立了其作为当今最佳开源模型的地位。它在编码基准测试中达到顶级性能，并在推理和智能体任务上显著缩小了与领先闭源模型的差距。

本文档将展示该模型的主要验证步骤，包括支持特性、特性配置、环境准备、单节点和多节点部署、精度及性能评估。

环境准备

模型权重

• DeepSeek-V4-Pro-w4a8-mtp(量化版本)：需要2个Atlas 800 A3(128G × 8)节点或4个Atlas 800 A2(64G × 8)节点。下载模型权重

建议将模型权重下载到多节点共享目录，例如/root/.cache/

验证多节点通信(可选)

如需部署多节点环境，需按照验证多节点通信环境验证多节点通信。

安装

您可以直接使用官方docker镜像运行DeepSeek-V4。

A2系列

在每个节点上启动docker镜像。

export IMAGE=quay.io/ascend/vllm-ascend:v0.21.0rc1
export NAME=vllm-ascend
docker run --rm \
    --name $NAME \
    --net=host \
    --shm-size=512g \
    --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/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 /etc/hccn.conf:/etc/hccn.conf \
    -v /mnt/sfs_turbo/.cache:/root/.cache \
    -it $IMAGE bash

A3系列

在每个节点上启动docker镜像。

export IMAGE=quay.io/ascend/vllm-ascend:v0.21.0rc1-a3
export NAME=vllm-ascend
docker run --rm \
    --name $NAME \
    --net=host \
    --shm-size=512g \
    --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 /etc/hccn.conf:/etc/hccn.conf \
    -v /mnt/sfs_turbo/.cache:/root/.cache \
    -it $IMAGE bash

部署

备注

在本教程中，我们假设您已将模型权重下载到/root/.cache/。您可以根据需要更改路径。

多节点部署

• DeepSeek-V4-Pro-w4a8-mtp：至少需要2个Atlas 800 A3(128G × 8)或4个Atlas 800 A2(64G × 8)。请分别在每个节点上运行以下脚本。

A2系列

Node0

local_ip="xxx"
node0_ip="xxxx"

export HCCL_IF_IP=$local_ip
export IFNAME="xxx"
export GLOO_SOCKET_IFNAME="$IFNAME"
export TP_SOCKET_IFNAME="$IFNAME"
export HCCL_SOCKET_IFNAME="$IFNAME"
export HCCL_BUFFSIZE=512
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ACL_OP_INIT_MODE=1
export VLLM_ENGINE_READY_TIMEOUT_S=3600
export HCCL_OP_EXPANSION_MODE="AIV"

export TASK_QUEUE_ENABLE=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

export HCCL_CONNECT_TIMEOUT=7200
export ASCEND_CONNECT_TIMEOUT=10000
export ASCEND_TRANSFER_TIMEOUT=10000
export VLLM_RPC_TIMEOUT=1800000

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
  --host 0.0.0.0 \
  --port 10010 \
  --max_model_len 135000 \
  --max-num-batched-tokens 4096 \
  --served-model-name dsv4 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 16 \
  --data-parallel-size 4 \
  --tensor-parallel-size 8 \
  --data-parallel-size-local 1 \
  --data-parallel-start-rank 0 \
  --data-parallel-address $node0_ip  \
  --enable-expert-parallel \
  --quantization ascend \
  --no-enable-prefix-caching \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --async-scheduling \
  --safetensors-load-strategy 'prefetch' \
  --block-size 128 \
  --speculative-config '{
     "num_speculative_tokens": 1,
     "method": "mtp",
     "enforce_eager": true
  }' \
  --additional-config '{
     "ascend_compilation_config":{
        "enable_npugraph_ex":true,
        "enable_static_kernel":false
     },
     "enable_cpu_binding": true,
     "enable_shared_expert_dp": true,
     "multistream_overlap_shared_expert":true
  }' \
  --compilation-config '{
     "cudagraph_mode":"FULL_DECODE_ONLY"
  }' \
  --model-loader-extra-config '{
     "enable_multithread_load": "true",
     "num_threads": 128
  }'

Node1-Node3

local_ip="xxx"
node0_ip="xxxx"

export HCCL_IF_IP=$local_ip
export IFNAME="xxx"
export GLOO_SOCKET_IFNAME="$IFNAME"
export TP_SOCKET_IFNAME="$IFNAME"
export HCCL_SOCKET_IFNAME="$IFNAME"
export HCCL_BUFFSIZE=512
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ACL_OP_INIT_MODE=1
export VLLM_ENGINE_READY_TIMEOUT_S=3600
export HCCL_OP_EXPANSION_MODE="AIV"

export TASK_QUEUE_ENABLE=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

export HCCL_CONNECT_TIMEOUT=7200
export ASCEND_CONNECT_TIMEOUT=10000
export ASCEND_TRANSFER_TIMEOUT=10000
export VLLM_RPC_TIMEOUT=1800000

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
  --host 0.0.0.0 \
  --port 10010 \
  --max_model_len 135000 \
  --max-num-batched-tokens 4096 \
  --served-model-name dsv4 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 16 \
  --data-parallel-size 4 \
  --tensor-parallel-size 8 \
  --data-parallel-size-local 1 \
  --data-parallel-start-rank 1 \
  --data-parallel-address $node0_ip  \
  --enable-expert-parallel \
  --quantization ascend \
  --no-enable-prefix-caching \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --async-scheduling \
  --safetensors-load-strategy 'prefetch' \
  --block-size 128 \
  --headless \
  --speculative-config '{
     "num_speculative_tokens": 1,
     "method": "mtp",
     "enforce_eager": true
  }' \
  --additional-config '{
     "ascend_compilation_config":{
        "enable_npugraph_ex":true,
        "enable_static_kernel":false
     },
     "enable_cpu_binding": true,
     "enable_shared_expert_dp": true,
     "multistream_overlap_shared_expert":true
  }' \
  --compilation-config '{
     "cudagraph_mode":"FULL_DECODE_ONLY"
  }' \
  --model-loader-extra-config '{
     "enable_multithread_load": "true",
     "num_threads": 128
  }'

A3系列

Node0

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_OP_EXPANSION_MODE="AIV"
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 HCCL_BUFFSIZE=2048
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export TASK_QUEUE_ENABLE=1
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
  --safetensors-load-strategy 'prefetch' \
  --max_model_len 135000  \
  --max-num-batched-tokens 4096 \
  --served-model-name dsv4 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 32 \
  --data-parallel-size 2 \
  --data-parallel-size-local 1 \
  --data-parallel-start-rank 0 \
  --data-parallel-address $node0_ip \
  --data-parallel-rpc-port 13399 \
  --tensor-parallel-size 16 \
  --enable-expert-parallel \
  --quantization ascend \
  --port 8900 \
  --host 0.0.0.0 \
  --block-size 128 \
  --async-scheduling \
  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
  --additional-config '
    {"ascend_compilation_config":{
        "enable_npugraph_ex":true,
        "enable_static_kernel":false
        },
    "enable_cpu_binding": true,
    "multistream_overlap_shared_expert":true}'

Node1

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_OP_EXPANSION_MODE="AIV"
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 HCCL_BUFFSIZE=2048
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export TASK_QUEUE_ENABLE=1
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
  --safetensors-load-strategy 'prefetch' \
  --max_model_len 135000  \
  --max-num-batched-tokens 4096 \
  --served-model-name dsv4 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 32 \
  --data-parallel-size 2 \
  --data-parallel-size-local 1 \
  --data-parallel-start-rank 1 \
  --data-parallel-address $node0_ip \
  --data-parallel-rpc-port 13399 \
  --tensor-parallel-size 16 \
  --enable-expert-parallel \
  --quantization ascend \
  --port 8900 \
  --host 0.0.0.0 \
  --block-size 128 \
  --async-scheduling \
  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
  --additional-config '
    {"ascend_compilation_config":{
        "enable_npugraph_ex":true,
        "enable_static_kernel":false
        },
    "enable_cpu_binding": true,
    "multistream_overlap_shared_expert":true}'

预填充-解码分离

我们将展示在Atlas 800 A3(128G × 8)多节点环境中使用1P1D部署DeepSeek-V4的指南，以获得更佳性能。

开始前，请

1. 在每个节点上准备脚本launch_online_dp.py。

   import argparse
   import multiprocessing
   import os
   import subprocess
   import sys
   
   def parse_args():
       parser = argparse.ArgumentParser()
       parser.add_argument(
           "--dp-size",
           type=int,
           required=True,
           help="Data parallel size."
       )
       parser.add_argument(
           "--tp-size",
           type=int,
           default=1,
           help="Tensor parallel size."
       )
       parser.add_argument(
           "--dp-size-local",
           type=int,
           default=-1,
           help="Local data parallel size."
       )
       parser.add_argument(
           "--dp-rank-start",
           type=int,
           default=0,
           help="Starting rank for data parallel."
       )
       parser.add_argument(
           "--dp-address",
           type=str,
           required=True,
           help="IP address for data parallel master node."
       )
       parser.add_argument(
           "--dp-rpc-port",
           type=str,
           default=12345,
           help="Port for data parallel master node."
       )
       parser.add_argument(
           "--vllm-start-port",
           type=int,
           default=9000,
           help="Starting port for the engine."
       )
       return parser.parse_args()
   
   args = parse_args()
   dp_size = args.dp_size
   tp_size = args.tp_size
   dp_size_local = args.dp_size_local
   if dp_size_local == -1:
       dp_size_local = dp_size
   dp_rank_start = args.dp_rank_start
   dp_address = args.dp_address
   dp_rpc_port = args.dp_rpc_port
   vllm_start_port = args.vllm_start_port
   
   def run_command(visible_devices, dp_rank, vllm_engine_port):
       command = [
           "bash",
           "./run_dp_template.sh",
           visible_devices,
           str(vllm_engine_port),
           str(dp_size),
           str(dp_rank),
           dp_address,
           dp_rpc_port,
           str(tp_size),
       ]
       subprocess.run(command, check=True)
   
   if __name__ == "__main__":
       template_path = "./run_dp_template.sh"
       if not os.path.exists(template_path):
           print(f"Template file {template_path} does not exist.")
           sys.exit(1)
   
       processes = []
       num_cards = dp_size_local * tp_size
       for i in range(dp_size_local):
           dp_rank = dp_rank_start + i
           vllm_engine_port = vllm_start_port + i
           visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
           process = multiprocessing.Process(target=run_command,
                                           args=(visible_devices, dp_rank,
                                                   vllm_engine_port))
           processes.append(process)
           process.start()
   
       for process in processes:
           process.join()
   
   

2. 在每个节点上准备脚本run_dp_template.sh。

   1. 预填充节点0

      nic_name="xxxx" # change to your own nic name
      local_ip=xx.xx.xx.1 # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      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 VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=204
      export HCCL_CONNECT_TIMEOUT=120
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=10
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=1024
      export TASK_QUEUE_ENABLE=1
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export ASCEND_RT_VISIBLE_DEVICES=$1
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --seed 1024 \
          --served-model-name auto \
          --max-model-len 131072 \
          --max-num-batched-tokens 4096 \
          --max-num-seqs 16 \
          --no-disable-hybrid-kv-cache-manager \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --safetensors-load-strategy 'prefetch' \
          --model-loader-extra-config='{"enable_multithread_load": "true", "num_threads": 128}' \
          --trust-remote-code \
          --gpu-memory-utilization 0.92 \
          --quantization ascend \
          --block-size 128 \
          --enforce-eager \
          --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
          --additional-config '{"enable_cpu_binding": true, "enable_dsa_cp": true}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_producer",
          "kv_port": "30200",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 2
                      }
              }
          }'
      

   2. 预填充节点1

      nic_name="xxxx" # change to your own nic name
      local_ip=xx.xx.xx.2 # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      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 VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=204
      export HCCL_CONNECT_TIMEOUT=120
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=10
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=1024
      export TASK_QUEUE_ENABLE=1
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export ASCEND_RT_VISIBLE_DEVICES=$1
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --seed 1024 \
          --served-model-name auto \
          --max-model-len 131072 \
          --max-num-batched-tokens 4096 \
          --max-num-seqs 16 \
          --no-disable-hybrid-kv-cache-manager \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --safetensors-load-strategy 'prefetch' \
          --model-loader-extra-config='{"enable_multithread_load": "true", "num_threads": 128}' \
          --trust-remote-code \
          --gpu-memory-utilization 0.92 \
          --quantization ascend \
          --block-size 128 \
          --enforce-eager \
          --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
          --additional-config '{"enable_cpu_binding": true, "enable_dsa_cp": true}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_producer",
          "kv_port": "30200",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 2
                      }
              }
          }'
      

   3. 解码节点(与另一个D节点相同)

      nic_name="xxxx" # change to your own nic name
      local_ip=xx.xx.xx.3/4 # change to your own ip
      
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export HCCL_OP_EXPANSION_MODE="AIV"
      export TASK_QUEUE_ENABLE=1
      export VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=2000
      export HCCL_CONNECT_TIMEOUT=1200
      export HCCL_IF_IP=$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=10
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=1024
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --seed 1024 \
          --served-model-name auto \
          --max-model-len 131072 \
          --max-num-batched-tokens 120 \
          --max-num-seqs 60 \
          --async-scheduling \
          --block-size 128 \
          --no-enable-prefix-caching \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --no-disable-hybrid-kv-cache-manager \
          --safetensors-load-strategy 'prefetch' \
          --model-loader-extra-config='{"enable_multithread_load": "true", "num_threads": 128}' \
          --trust-remote-code \
          --gpu-memory-utilization 0.9 \
          --quantization ascend \
          --speculative-config '{"num_speculative_tokens": 1, "method":"mtp", "enforce_eager": true}' \
          --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_consumer",
          "kv_port": "30800",
          "engine_id": "8",
          "kv_connector_extra_config": {
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 2
                      }
              }
          }' \
          --additional-config '{
              "ascend_compilation_config":{
                  "enable_npugraph_ex":true,
                  "enable_static_kernel":false
              },
          "enable_cpu_binding":true,
          "recompute_scheduler_enable":true
          }' 
      

准备工作完成后，您可以在每个节点上使用以下命令启动服务器：

1. 预填充节点0

# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 0 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100

2. 预填充节点1

# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 1 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100

3. 解码节点0

# change ip to your own
python launch_online_dp.py --dp-size 16 --tp-size 2 --dp-size-local 8 --dp-rank-start 0 --dp-address xx.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100

4. 解码节点1

# change ip to your own
python launch_online_dp.py --dp-size 16 --tp-size 2 --dp-size-local 8 --dp-rank-start 8 --dp-address xx.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100

最后，参考预填充-解码分离(Deepseek)部署P-D分离代理。

A2预填充-解码分离

我们将展示在Atlas 800 A2(64G × 8)多节点环境中使用1P1D部署DeepSeek-V4的指南，以获得更佳性能。

开始前，请

1. 在每个节点上准备脚本launch_online_dp.py。

   import argparse
   import multiprocessing
   import os
   import subprocess
   import sys
   
   def parse_args():
       parser = argparse.ArgumentParser()
       parser.add_argument(
           "--dp-size",
           type=int,
           required=True,
           help="Data parallel size."
       )
       parser.add_argument(
           "--tp-size",
           type=int,
           default=1,
           help="Tensor parallel size."
       )
       parser.add_argument(
           "--dp-size-local",
           type=int,
           default=-1,
           help="Local data parallel size."
       )
       parser.add_argument(
           "--dp-rank-start",
           type=int,
           default=0,
           help="Starting rank for data parallel."
       )
       parser.add_argument(
           "--dp-address",
           type=str,
           required=True,
           help="IP address for data parallel master node."
       )
       parser.add_argument(
           "--dp-rpc-port",
           type=str,
           default=12345,
           help="Port for data parallel master node."
       )
       parser.add_argument(
           "--vllm-start-port",
           type=int,
           default=9000,
           help="Starting port for the engine."
       )
       return parser.parse_args()
   
   args = parse_args()
   dp_size = args.dp_size
   tp_size = args.tp_size
   dp_size_local = args.dp_size_local
   if dp_size_local == -1:
       dp_size_local = dp_size
   dp_rank_start = args.dp_rank_start
   dp_address = args.dp_address
   dp_rpc_port = args.dp_rpc_port
   vllm_start_port = args.vllm_start_port
   
   def run_command(visible_devices, dp_rank, vllm_engine_port):
       command = [
           "bash",
           "./run_dp_template.sh",
           visible_devices,
           str(vllm_engine_port),
           str(dp_size),
           str(dp_rank),
           dp_address,
           dp_rpc_port,
           str(tp_size),
       ]
       subprocess.run(command, check=True)
   
   if __name__ == "__main__":
       template_path = "./run_dp_template.sh"
       if not os.path.exists(template_path):
           print(f"Template file {template_path} does not exist.")
           sys.exit(1)
   
       processes = []
       num_cards = dp_size_local * tp_size
       for i in range(dp_size_local):
           dp_rank = dp_rank_start + i
           vllm_engine_port = vllm_start_port + i
           visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
           process = multiprocessing.Process(target=run_command,
                                           args=(visible_devices, dp_rank,
                                                   vllm_engine_port))
           processes.append(process)
           process.start()
   
       for process in processes:
           process.join()
   

2. 在每个节点上准备脚本run_dp_template.sh。

   1. 预填充节点(4个P节点共享相同脚本)

      nic_name="xxxx" # change to your own nic name
      local_ip=xx.xx.xx.1/2/3/4 # change to your own ip
      
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export HCCL_OP_EXPANSION_MODE="AIV"
      export TASK_QUEUE_ENABLE=1
      
      export VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=204
      export HCCL_CONNECT_TIMEOUT=1200
      
      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=10
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=1024
      
      sysctl -w vm.swappiness=0
      sysctl -w kernel.numa_balancing=0
      sysctl kernel.sched_migration_cost_ns=50000
      
      export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --seed 1024 \
          --served-model-name deepseek_v4 \
          --max-model-len 133072 \
          --max-num-batched-tokens 4096 \
          --max-num-seqs 16 \
          --no-disable-hybrid-kv-cache-manager \
          --trust-remote-code \
          --gpu-memory-utilization 0.9 \
          --quantization ascend \
          --safetensors-load-strategy 'prefetch' \
          --model-loader-extra-config='{"enable_multithread_load": "true", "num_threads": 128}' \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --enforce-eager \
          --no-enable-prefix-caching \
          --speculative-config '{"num_speculative_tokens": 1, "method":"mtp", "enforce_eager": true}' \
          --additional-config '{"enable_cpu_binding": true, "enable_shared_expert_dp": true, "enable_dsa_cp": true}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_producer",
          "kv_port": "30000",
          "engine_id": "0",
          "kv_connector_extra_config": {
                    "prefill": {
                            "dp_size": 4,
                            "tp_size": 8
                     },
                     "decode": {
                            "dp_size": 8,
                            "tp_size": 4
                     }
              }
          }'
      

   2. 解码节点(4个D节点共享相同脚本)

      nic_name="xxxx" # change to your own nic name
      local_ip=xx.xx.xx.5/6/7/8 # change to your own ip
      
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export HCCL_OP_EXPANSION_MODE="AIV"
      export TASK_QUEUE_ENABLE=1
      
      export VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=204
      export HCCL_CONNECT_TIMEOUT=1200
      
      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=10
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=1024
      
      sysctl -w vm.swappiness=0
      sysctl -w kernel.numa_balancing=0
      sysctl kernel.sched_migration_cost_ns=50000
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --seed 1024 \
          --served-model-name deepseek_v4 \
          --max-model-len 133072 \
          --max-num-batched-tokens 120 \
          --max-num-seqs 60 \
          --async-scheduling \
          --block-size 128 \
          --no-disable-hybrid-kv-cache-manager \
          --trust-remote-code \
          --gpu-memory-utilization 0.9 \
          --quantization ascend \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --safetensors-load-strategy 'prefetch' \
          --model-loader-extra-config='{"enable_multithread_load": "true", "num_threads": 128}' \
          --no-enable-prefix-caching \
          --speculative-config '{"num_speculative_tokens": 1, "method":"mtp", "enforce_eager": true}' \
          --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 8,
                              "tp_size": 4
                      }
              }
          }' \
          --additional-config '{"ascend_compilation_config":{"enable_npugraph_ex":true,"enable_static_kernel":false}, "enable_cpu_binding":true, "recompute_scheduler_enable":true}'
      

准备工作完成后，您可以在每个节点上使用以下命令启动服务器：

1. 预填充节点0

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 0 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100

2. 预填充节点1

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 1 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100

3. 预填充节点2

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 2 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100

4. 预填充节点3

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 3 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100

5. 解码节点0

# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 0 --dp-address xx.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100

6. 解码节点1

# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 2 --dp-address xx.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100

7. 解码节点2

# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 4 --dp-address xx.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100

8. 解码节点3

# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 6 --dp-address xx.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100

最后，参考预填充-解码分离(Deepseek)部署P-D分离代理。

功能验证

服务器启动后，您可以使用输入提示查询模型：

curl http://<node0_ip>:<port>/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "deepseek_v4",
        "messages": [
            {
                "role": "user",
                "content": "Who are you?"
            }
        ],
        "max_tokens": 256,
        "temperature": 0
    }'

精度评估

以下是两种精度评估方法。

使用AISBench

1. 详情请参考使用AISBench。

2. 执行后即可获取结果。

使用Language Model Evaluation Harness

例如，以gsm8k数据集作为测试数据集，在线模式下运行DeepSeek-V4的精度评估。

1. lm_eval安装请参考使用lm_eval。

2. 运行lm_eval执行精度评估。

lm_eval \
  --model local-completions \
  --model_args model=/root/.cache/Eco-Tech/DeepSeek-V4-Pro-w4a8-mtp,base_url=http://127.0.0.1:8006/v1/completions,tokenized_requests=False,trust_remote_code=True \
  --tasks gsm8k \
  --output_path ./

3. 执行后即可获取结果。

性能

使用AISBench

详情请参考使用AISBench进行性能评估。

使用vLLM Benchmark

以DeepSeek-V4-Pro-w4a8-mtp为例运行性能评估。

更多详情请参考vllm benchmark。

vllm bench有三个子命令：

• latency：基准测试单批次请求的延迟。

• serve：基准测试在线服务吞吐量。

• throughput：基准测试离线推理吞吐量。

以serve为例，运行代码如下。

export VLLM_USE_MODELSCOPE=true
vllm bench serve --model /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Pro-w4a8-mtp  --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./