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xc-llm-ascend/docs/source/tutorials/DeepSeek-V3.1.md
zxr2333 073a3a6e6c [Doc][P/D] Fix MooncakeConnector's name (#5172) 
### What this PR does / why we need it?
vLLM community has integrated their MooncakeConnector. The original
scripts will now find this MooncakeConnector instead of the one from
vLLM-Ascend. All scripts that involve using the MooncakeConnector need
to be modified to another name.

### Does this PR introduce _any_ user-facing change?
Yes, users need to use a new name to load vLLM-Ascend MooncakeConnector.

### How was this patch tested?
By CI.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
2025-12-18 22:29:19 +08:00

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# DeepSeek-V3/3.1
## Introduction
DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects:
- Hybrid thinking mode: One model supports both thinking mode and non-thinking mode by changing the chat template.
- Smarter tool calling: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
- Higher thinking efficiency: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.
The `DeepSeek-V3.1` model is first supported in `vllm-ascend:v0.9.1rc3`
This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation.
## Supported Features
Refer to [supported features](../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
Refer to [feature guide](../user_guide/feature_guide/index.md) to get the feature's configuration.
## Environment Preparation
### Model Weight
- `DeepSeek-V3.1`(BF16 version): [Download model weight](https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V3.1)
- `DeepSeek-V3.1-w8a8`(Quantized version without mtp): [Download model weight](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3.1-w8a8).
- `DeepSeek-V3.1_w8a8mix_mtp`(Quantized version with mix mtp): [Download model weight](https://www.modelscope.cn/models/Eco-Tech/DeepSeek-V3.1-w8a8). Please modify `torch_dtype` from `float16` to `bfloat16` in `config.json`.
- `Method of Quantify`: [msmodelslim](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/example/DeepSeek/README.md#deepseek-v31-w8a8-%E6%B7%B7%E5%90%88%E9%87%8F%E5%8C%96-mtp-%E9%87%8F%E5%8C%96). You can use these methods to quantify the model.
It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
### Verify Multi-node Communication(Optional)
If you want to deploy multi-node environment, you need to verify multi-node communication according to [verify multi-node communication environment](../installation.md#verify-multi-node-communication).
### Installation
You can using our official docker image to run `DeepSeek-V3.1` directly.
Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).
```{code-block} bash
:substitutions:
# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
# Update the vllm-ascend image according to your environment.
# Note you should download the weight to /root/.cache in advance.
# 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 \
--shm-size=1g \
--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 /mnt/sfs_turbo/.cache:/root/.cache \
-it $IMAGE bash
```
If you want to deploy multi-node environment, you need to set up environment on each node.
## Deployment
### Single-node Deployment
- Quantized model `DeepSeek-V3.1_w8a8mix_mtp` can be deployed on 1 Atlas 800 A3 (64G × 16).
Run the following script to execute online inference.
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
# AIV
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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export VLLM_ASCEND_ENABLE_MLAPO=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=0
export DISABLE_L2_CACHE=1
vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
--host 0.0.0.0 \
--port 8015 \
--data-parallel-size 4 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
```
### Multi-node Deployment
- `DeepSeek-V3.1_w8a8mix_mtp`: require at least 2 Atlas 800 A2 (64G × 8).
Run the following scripts on two nodes respectively.
**Node 0**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
# AIV
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0
vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
--host 0.0.0.0 \
--port 8004 \
--data-parallel-size 4 \
--data-parallel-size-local 2 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--max-num-seqs 20 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.94 \
--speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
```
**Node 1**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
# AIV
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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0
vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
--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 $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--max-num-seqs 20 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.94 \
--speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
```
### Prefill-Decode Disaggregation
We recommend using Mooncake for deployment: [Mooncake](./pd_disaggregation_mooncake_multi_node.md).
Take Atlas 800 A3 (64G × 16) for example, we recommend to deploy 2P1D (4 nodes) rather than 1P1D (2 nodes), because there is no enough NPU memory to serve high concurrency in 1P1D case.
- `DeepSeek-V3.1_w8a8mix_mtp 2P1D Layerwise` require 4 Atlas 800 A3 (64G × 16).
To run the vllm-ascend `Prefill-Decode Disaggregation` service, you need to deploy a `launch_dp_program.py` script and a `run_dp_template.sh` script on each node and deploy a `proxy.sh` script on prefill master node to forward requests.
1. `launch_dp_program.py` script for each node:
```python
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. Prefill Node 0 `run_dp_template.sh` script
```shell
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.1"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
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_VERSION="0.11.0"
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 VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_BUFFSIZE=256
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICE=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
vllm serve /weights/DeepSeek-V3.1_w8a8mix_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 $7 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name deepseek_v3 \
--max-model-len 40000 \
--max-num-batched-tokens 16384 \
--max-num-seqs 8 \
--enforce-eager \
--trust-remote-code \
--gpu-memory-utilization 0.9 \
--quantization ascend \
--no-enable-prefix-caching \
--speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
--additional-config '{"recompute_scheduler_enable":true,"enable_shared_expert_dp": true}' \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_producer",
"kv_port": "30000",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"prefill": {
"dp_size": 2,
"tp_size": 8
},
"decode": {
"dp_size": 32,
"tp_size": 1
}
}
}'
```
3. Prefill Node 1 `run_dp_template.sh` script
```shell
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.2"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
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_VERSION="0.11.0"
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 VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_BUFFSIZE=256
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICE=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
vllm serve /weights/DeepSeek-V3.1_w8a8mix_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 $7 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name deepseek_v3 \
--max-model-len 40000 \
--max-num-batched-tokens 16384 \
--max-num-seqs 8 \
--enforce-eager \
--trust-remote-code \
--gpu-memory-utilization 0.9 \
--quantization ascend \
--no-enable-prefix-caching \
--speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
--additional-config '{"recompute_scheduler_enable":true,"enable_shared_expert_dp": true}' \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_producer",
"kv_port": "30100",
"engine_id": "1",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"prefill": {
"dp_size": 2,
"tp_size": 8
},
"decode": {
"dp_size": 32,
"tp_size": 1
}
}
}'
```
4. Decode Node 0 `run_dp_template.sh` script
```shell
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.3"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
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_VERSION="0.11.0"
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 VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_BUFFSIZE=600
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICE=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
vllm serve /weights/DeepSeek-V3.1_w8a8mix_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 $7 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name deepseek_v3 \
--max-model-len 40000 \
--max-num-batched-tokens 256 \
--max-num-seqs 40 \
--trust-remote-code \
--gpu-memory-utilization 0.94 \
--quantization ascend \
--no-enable-prefix-caching \
--speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"recompute_scheduler_enable":true,"multistream_overlap_shared_expert": true,"lm_head_tensor_parallel_size":16}' \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_consumer",
"kv_port": "30200",
"engine_id": "2",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"prefill": {
"dp_size": 2,
"tp_size": 8
},
"decode": {
"dp_size": 32,
"tp_size": 1
}
}
}'
```
5. Decode Node 1 `run_dp_template.sh` script
```shell
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.4"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
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_VERSION="0.11.0"
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 VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_BUFFSIZE=600
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICE=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
vllm serve /weights/DeepSeek-V3.1_w8a8mix_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_v3 \
--max-model-len 40000 \
--max-num-batched-tokens 256 \
--max-num-seqs 40 \
--trust-remote-code \
--gpu-memory-utilization 0.94 \
--quantization ascend \
--no-enable-prefix-caching \
--speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"recompute_scheduler_enable":true,"multistream_overlap_shared_expert": true,"lm_head_tensor_parallel_size":16}' \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_consumer",
"kv_port": "30300",
"engine_id": "3",
"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
"kv_connector_extra_config": {
"prefill": {
"dp_size": 2,
"tp_size": 8
},
"decode": {
"dp_size": 32,
"tp_size": 1
}
}
}'
```
6. run server for each node
```shell
# p0
python launch_dp_program.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address 141.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100
# p1
python launch_dp_program.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address 141.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100
# d0
python launch_dp_program.py --dp-size 32 --tp-size 1 --dp-size-local 16 --dp-rank-start 0 --dp-address 141.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100
# d1
python launch_dp_program.py --dp-size 32 --tp-size 1 --dp-size-local 16 --dp-rank-start 16 --dp-address 141.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100
```
7. Prefill master node `proxy.sh` scripts
```shell
python load_balance_proxy_server_example.py \
--port 1999 \
--host 141.xx.xx.1 \
--prefiller-hosts \
141.xx.xx.1 \
141.xx.xx.1 \
141.xx.xx.2 \
141.xx.xx.2 \
--prefiller-ports \
7100 7101 7100 7101 \
--decoder-hosts \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.3 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
141.xx.xx.4 \
--decoder-ports \
7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 \
7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 \
```
8. run proxy
Run a proxy server on the same node with the prefiller service instance. You can get the proxy program in the repository's examples: [load\_balance\_proxy\_layerwise\_server\_example.py](https://github.com/vllm-project/vllm-ascend/blob/main/examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py) or [load\_balance\_proxy\_server\_example.py](https://github.com/vllm-project/vllm-ascend/blob/main/examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py)
```shell
cd vllm-ascend/examples/disaggregated_prefill_v1/
bash proxy.sh
```
## Functional Verification
Once your server is started, you can query the model with input prompts:
```shell
curl http://<node0_ip>:<port>/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek_v3",
"prompt": "The future of AI is",
"max_tokens": 50,
"temperature": 0
}'
```
## Accuracy Evaluation
Here are two accuracy evaluation methods.
### Using AISBench
1. Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) for details.
2. After execution, you can get the result, here is the result of `DeepSeek-V3.1_w8a8mix_mtp` in `vllm-ascend:0.11.0rc1` for reference only.
| dataset | version | metric | mode | vllm-api-general-chat | note |
|----- | ----- | ----- | ----- | -----| ----- |
| ceval | - | accuracy | gen | 90.94 | 1 Atlas 800 A3 (64G × 16) |
| gsm8k | - | accuracy | gen | 96.28 | 1 Atlas 800 A3 (64G × 16) |
### Using Language Model Evaluation Harness
Not test yet.
## Performance
### Using AISBench
Refer to [Using AISBench for performance evaluation](../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details.
### Using vLLM Benchmark
Run performance evaluation of `DeepSeek-V3.1_w8a8mix_mtp` as an example.
Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
There are three `vllm bench` subcommand:
- `latency`: Benchmark the latency of a single batch of requests.
- `serve`: Benchmark the online serving throughput.
- `throughput`: Benchmark offline inference throughput.
Take the `serve` as an example. Run the code as follows.
```shell
vllm bench serve --model vllm-ascend/DeepSeek-V3.1_w8a8mix_mtp --dataset-name random --random-input 1024 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
```
After about several minutes, you can get the performance evaluation result.
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