xc-llm-ascend/docs/source/tutorials/models/GLM5.md

GLM-5

Introduction

GLM-5 use a Mixture-of-Experts (MoE) architecture and targeting at complex systems engineering and long-horizon agentic tasks.

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 to get the model's supported feature matrix.

Refer to feature guide to get the feature's configuration.

Environment Preparation

Model Weight

• GLM-5(BF16 version): Download model weight.
• GLM-5-w4a8: Download model weight.
• GLM-5-w8a8: Download model weight.
• You can use msmodelslim to quantify the model naively.

It is recommended to download the model weight to the shared directory of multiple nodes, such as /root/.cache/

Installation

You can use our official docker image to run GLM-5 directly.

:::::{tab-set} :sync-group: install

::::{tab-item} A3 series :sync: A3

Start the docker image on your each node.

   :substitutions:

export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
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/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 /root/.cache:/root/.cache \
-it $IMAGE bash

:::: ::::{tab-item} A2 series :sync: A2

Start the docker image on your each node.

   :substitutions:

export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --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/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 /root/.cache:/root/.cache \
    -it $IMAGE bash

:::: :::::

In addition, if you don't want to use the docker image as above, you can also build all from source:

• Install vllm-ascend from source, refer to installation.

If you want to deploy multi-node environment, you need to set up environment on each node.

Deployment

Single-node Deployment

:::::{tab-set} :sync-group: install

::::{tab-item} A3 series :sync: A3

• Quantized model glm-5-w4a8 can be deployed on 1 Atlas 800 A3 (64G × 16) .

Run the following script to execute online inference.

   :substitutions:
export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 1 \
--tensor-parallel-size 16 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 8 \
--max-model-len 66600 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--async-scheduling \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' 

• Quantized model glm-5-w8a8 can be deployed on 1 Atlas 800 A3 (64G × 16) .

Run the following script to execute online inference.

   :substitutions:
export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export VLLM_ASCEND_ENABLE_MLAPO=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 1 \
--tensor-parallel-size 16 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 8 \
--max-model-len 40960 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--async-scheduling \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' 

:::: ::::{tab-item} A2 series :sync: A2

• Quantized model glm-5-w4a8 can be deployed on 1 Atlas 800 A2 (64G × 8) .

Run the following script to execute online inference.

   :substitutions:
export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 1 \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 2 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--async-scheduling \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

:::: :::::

Notice: The parameters are explained as follows:

• For single-node deployment, we recommend using dp1tp16 and turn off expert parallel in low-latency scenarios.
• --async-scheduling Asynchronous scheduling is a technique used to optimize inference efficiency. It allows non-blocking task scheduling to improve concurrency and throughput, especially when processing large-scale models.

Multi-node Deployment

If you want to deploy multi-node environment, you need to verify multi-node communication according to verify multi-node communication environment.

:::::{tab-set} :sync-group: install

::::{tab-item} A3 series :sync: A3

• glm-5-bf16: require at least 2 Atlas 800 A3 (64G × 16).

Run the following scripts on two nodes respectively.

node 0

   :substitutions:
# 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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-bf16 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--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 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

node 1

   :substitutions:
# 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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-bf16 \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--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 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

:::: ::::{tab-item} A2 series :sync: A2

Run the following scripts on two nodes respectively.

node 0

   :substitutions:
# 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="xxx"

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=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 2 \
--max-model-len 131072 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

node 1

   :substitutions:
# 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="xxx"

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=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 2 \
--max-model-len 131072 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

:::: :::::

• For bf16 weight, use this script on each node to enable Multi Token Prediction (MTP).

python adjust_weight.py "path_of_bf16_weight"

# adjust_weight.py
from safetensors.torch import safe_open, save_file
import torch
import json
import os
import sys

target_keys = ["model.embed_tokens.weight", "lm_head.weight"]

def get_tensor_info(file_path):
   with safe_open(file_path, framework="pt", device="cpu") as f:
         tensor_names = f.keys()
         tensor_dict = {}
         for name in tensor_names:
            tensor = f.get_tensor(name)
            tensor_dict[name] = tensor
         return tensor_dict


if __name__ == "__main__":
   directory_path = sys.argv[1]
   json_name = "model.safetensors.index.json"
   json_path = os.path.join(directory_path, json_name)
   with open(json_path, 'r', encoding='utf-8') as f:
         json_data = json.load(f)
   weight_map = json_data.get('weight_map', {})
   file_list = []
   for key in target_keys:
         safetensor_file = weight_map.get(key)
         file_list.append(directory_path + safetensor_file)

   new_dict = {}
   for file_path in file_list:
         tensor_dict = get_tensor_info(file_path)
         for key in target_keys:
            if key in tensor_dict:
               if key == "model.embed_tokens.weight":
                     new_key = "model.layers.78.embed_tokens.weight"
               elif key == "lm_head.weight":
                     new_key = "model.layers.78.shared_head.head.weight"
               new_dict[new_key] = tensor_dict[key]

   new_file_name = os.path.join(directory_path, "mtp-others.safetensors")
   new_key = ["model.layers.78.embed_tokens.weight", "model.layers.78.shared_head.head.weight"]
   save_file(tensors=new_dict, filename=new_file_name)
   for key in new_key:
         json_data["weight_map"][key] = "mtp-others.safetensors"
   with open(json_path, 'w', encoding='utf-8') as f:
         json.dump(json_data, f, indent=2)

:::::{tab-set} :sync-group: install

::::{tab-item} A3 series :sync: A3

• glm-5-w8a8: require 2 Atlas 800 A3 (64G × 16).

Run the following scripts on two nodes respectively.

node 0

   :substitutions:
# 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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_MLAPO=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 65536 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--async-scheduling \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

node 1

   :substitutions:
# 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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_MLAPO=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 65536 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--async-scheduling \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

:::: :::::

Prefill-Decode Disaggregation

We'd like to show the deployment guide of GLM-5 on multi-node environment with 1P1D for better performance.

Before you start, please

1. prepare the script launch_online_dp.py on each node:

   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. prepare the script run_dp_template.sh on each node.

   1. Prefill node 0

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # 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 OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/glm5-w8a8 \
          --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 \
          --speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 131072 \
          --additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true,"recompute_scheduler_enable" : true}' \
          --max-num-batched-tokens 4096 \
          --trust-remote-code \
          --max-num-seqs 64 \
          --quantization ascend \
          --gpu-memory-utilization 0.95 \
          --enforce-eager \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_producer",
          "kv_port": "30000",
          "engine_id": "0",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      
      

   2. Prefill node 1

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # 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 OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      
      export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/glm5-w8a8 \
          --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 \
          --speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 131072 \
          --additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true,"recompute_scheduler_enable" : true}' \
          --max-num-batched-tokens 4096 \
          --trust-remote-code \
          --max-num-seqs 64 \
          --gpu-memory-utilization 0.95 \
          --quantization ascend \
          --enforce-eager \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_producer",
          "kv_port": "30000",
          "engine_id": "0",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   3. Decode node 0

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # 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
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/glm5-w8a8 \
          --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 \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[4, 8, 12, 16,20,24,28, 32]}' \
          --additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true,"recompute_scheduler_enable" : true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --async-scheduling \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   4. Decode node 1

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # 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
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/glm5-w8a8 \
          --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 \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[4, 8, 12, 16,20,24,28, 32]}' \
          --additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true,"recompute_scheduler_enable" : true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --async-scheduling \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   5. Decode node 2

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # 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
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/glm5-w8a8 \
          --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 \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[4, 8, 12, 16,20,24,28, 32]}' \
          --additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true,"recompute_scheduler_enable" : true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --async-scheduling \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   6. Decode node 3

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # 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
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/glm5-w8a8 \
          --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 \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[4, 8, 12, 16,20,24,28, 32]}' \
          --additional-config '{"enable_npugraph_ex": true, "fuse_muls_add":true,"multistream_overlap_shared_expert":true,"recompute_scheduler_enable" : true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --async-scheduling \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "engine_id": "1",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 4,
                              "tp_size": 8
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

Once the preparation is done, you can start the server with the following command on each node:

1. Prefill node 0

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address $node_p0_ip --dp-rpc-port 10521 --vllm-start-port 6700

2. Prefill node 1

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 2 --dp-rank-start 2 --dp-address $node_p0_ip --dp-rpc-port 10521 --vllm-start-port 6700

3. Decode node 0

# change ip to your own
python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721

4. Decode node 1

# change ip to your own
python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721

5. Decode node 2

# change ip to your own
python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 8 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721

6. Decode node 3

# change ip to your own
python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 12 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721

Request Forwarding

To set up request forwarding, run the following script on any machine. You can get the proxy program in the repository's examples: load_balance_proxy_server_example.py

unset http_proxy
unset https_proxy

python load_balance_proxy_server_example.py \
    --port 8000 \
    --host 0.0.0.0 \
    --prefiller-hosts \
       $node_p0_ip \
       $node_p0_ip \
       $node_p1_ip \
       $node_p1_ip \
    --prefiller-ports \
       6700 6701 \
       6700 6701 \
    --decoder-hosts \
      $node_d0_ip \
      $node_d0_ip \
      $node_d0_ip \
      $node_d0_ip \
      $node_d1_ip \
      $node_d1_ip \
      $node_d1_ip \
      $node_d1_ip \
      $node_d2_ip \
      $node_d2_ip \
      $node_d2_ip \
      $node_d2_ip \
      $node_d3_ip \
      $node_d3_ip \
      $node_d3_ip \
      $node_d3_ip \
    --decoder-ports \
      6721 6722 6723 6724 \
      6721 6722 6723 6724 \
      6721 6722 6723 6724 \
      6721 6722 6723 6724      

Accuracy Evaluation

Here are two accuracy evaluation methods.

Using AISBench

1. Refer to Using AISBench for details.

2. After execution, you can get the result.

Using Language Model Evaluation Harness

Not test yet.

Performance

Using AISBench

Refer to Using AISBench for performance evaluation for details.

Using vLLM Benchmark

Refer to vllm benchmark for more details.