[doc]add GLM5.md

docs/source/tutorials/models/GLM5.md

@@ -0,0 +1,271 @@
+# 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/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 $node0_ip \
+--data-parallel-rpc-port 12890 \
+--tensor-parallel-size 16 \
+--quantization ascend \
+--seed 1024 \
+--served-model-name glm-5 \
+--enable-expert-parallel \
+--max-num-seqs 16 \
+--max-model-len 8192 \
+--max-num-batched-tokens 4096 \
+--trust-remote-code \
+--no-enable-prefix-caching \
+--gpu-memory-utilization 0.95 \
+--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
+--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'
+```
+
+**node 1**
+
+```shell
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxx"
+local_ip="xxx"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+export HCCL_OP_EXPANSION_MODE="AIV"
+
+export HCCL_IF_IP=$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 \
+--quantization ascend \
+--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.

docs/source/tutorials/models/index.md

@@ -26,6 +26,7 @@ DeepSeek-V3.1.md
 DeepSeek-V3.2.md
 DeepSeek-R1.md
 GLM4.x.md
+GLM5.md
 Kimi-K2-Thinking.md
 PaddleOCR-VL.md
 :::