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

# GLM-5

## Introduction

[GLM-5](https://huggingface.co/zai-org/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](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_models.html)to get the model's supported feature matrix.

Refer to [feature guide](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_features.html) to get the feature's configuration.

## Environment Preparation

### Model Weight

- `GLM-5`(BF16 version): [Download model weight](https://www.modelscope.cn/models/ZhipuAI/GLM-5).
- `GLM-5-w4a8`(Quantized version without mtp): [Download model weight](https://modelers.cn/models/Eco-Tech/GLM-5-w4a8).
- You can use [msmodelslim](https://gitcode.com/Ascend/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

vLLM and vLLM-ascend only support GLM-5 on our main branches. you can use our official docker images and upgrade vllm and vllm-ascend for inference.

```{code-block} bash
# Update --device according to your device (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, alm5-a3 can be replaced by: glm5;glm5-openeuler;glm5-a3-openeuler
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:glm5-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
```

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](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).

To inference `GLM-5`, you should upgrade vllm、vllm-ascend、transformers to main branches:

```shell
# upgrade vllm
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout 978a37c82387ce4a40aaadddcdbaf4a06fc4d590
VLLM_TARGET_DEVICE=empty pip install -v .

# upgrade vllm-ascend
git clone https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
git checkout ff3a50d011dcbea08f87ebed69ff1bf156dbb01e
git submodule update --init --recursive
pip install -v .

# reinstall transformers
pip install git+https://github.com/huggingface/transformers.git
```

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

## Deployment

### Single-node Deployment

**A2 series**

Not test yet.

**A3 series**

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

Run the following script to execute online inference.

```shell
export HCCL_OP_EXPANSION_MODE="AIV"
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_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 '{"multistream_overlap_shared_expert":true}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--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

**A2 series**

Not test yet.

**A3 series**

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

Run the following scripts on two nodes respectively.

**node 0**

```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="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=10
export VLLM_USE_V1=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**

```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="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=10
export VLLM_USE_V1=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"}'
```

### Prefill-Decode Disaggregation

Not test yet.

## Accuracy Evaluation

Here are two accuracy evaluation methods.

### Using AISBench

1. Refer to [Using AISBench](https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/evaluation/using_ais_bench.html) 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](https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/evaluation/using_ais_bench.html#execute-performance-evaluation) for details.

### Using vLLM Benchmark

Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.