# GLM-4.5/4.6/4.7 ## Introduction GLM-4.x series models use a Mixture-of-Experts (MoE) architecture and are foundational models specifically designed for agent applications. The `GLM-4.5` model is first supported in `vllm-ascend:v0.10.0rc1`. 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 - `GLM-4.5`(BF16 version): [Download model weight](https://www.modelscope.cn/models/ZhipuAI/GLM-4.5). - `GLM-4.6`(BF16 version): [Download model weight](https://www.modelscope.cn/models/ZhipuAI/GLM-4.6). - `GLM-4.7`(BF16 version): [Download model weight](https://www.modelscope.cn/models/ZhipuAI/GLM-4.7). - `GLM-4.5-w8a8-with-float-mtp`(Quantized version with mtp): [Download model weight](https://modelers.cn/models/Modelers_Park/GLM-4.5-w8a8). - `GLM-4.6-w8a8`(Quantized version without mtp): [Download model weight](https://modelers.cn/models/Modelers_Park/GLM-4.6-w8a8). Because vllm does not support GLM4.6 mtp in October, we do not provide an mtp version. Last month, it was supported; you can use the following quantization scheme to add mtp weights to the quantized weights. - `GLM-4.7-w8a8-with-float-mtp`(Quantized version without mtp): [Download model weight](https://modelscope.cn/models/Eco-Tech/GLM-4.7-W8A8-floatmtp). - `Method of Quantization`: [quantization scheme](https://ai.gitcode.com/Ascend-SACT/GLM-4.5-w8a8). 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/`. ### Installation You can use our official docker image to run `GLM-4.x` directly. :::::{tab-set} :sync-group: install ::::{tab-item} A3 series :sync: A3 Start the docker image on each node. ```{code-block} bash :substitutions: export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3 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/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. ```{code-block} bash :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](../../installation.md). If you want to deploy multi-node environment, you need to set up environment on each node. ## Deployment ### Single-node Deployment - In low-latency scenarios, we recommend a single-machine deployment. - Quantized model `glm4.7_w8a8_with_float_mtp` can be deployed on 1 Atlas 800 A3 (64G × 16) or 1 Atlas 800 A2 (64G × 8). Run the following script to execute online inference. ```shell #!/bin/sh export HCCL_BUFFSIZE=512 export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export HCCL_OP_EXPANSION_MODE=AIV export VLLM_ASCEND_BALANCE_SCHEDULING=1 export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --data-parallel-size 2 \ --tensor-parallel-size 8 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name glm \ --max-model-len 133000 \ --max-num-batched-tokens 8192 \ --max-num-seqs 16 \ --quantization ascend \ --trust-remote-code \ --gpu-memory-utilization 0.9 \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --compilation-config '{"cudagraph_capture_sizes": [1,2,4,8,16,32,64,128,256,512], "cudagraph_mode": "FULL_DECODE_ONLY"}' \ --additional-config '{"enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' ``` **Notice:** The parameters are explained as follows: - `fusion_ops_gmmswigluquant` The performance of the GmmSwigluQuant fusion operator tends to degrade when the total number of NPUs is ≤ 16. - `VLLM_ASCEND_ENABLE_FLASHCOMM1` Due to the FD feature of the FIA operator being invalidated by padding data introduced by this feature, we recommend disabling the `flashcomm1` feature for long-sequence (≥16k) and low-concurrency (≤8 batch size) scenarios.For long-sequence and high-concurrency scenarios, you may enable this feature to achieve improved Prefill performance. ### Multi-node Deployment Although the former tutorial said "Not recommended to deploy multi-node on Atlas 800 A2 (64G × 8)", but if you insist to deploy GLM-4.x model on multi-node like 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" 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=512 export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export HCCL_OP_EXPANSION_MODE=AIV export VLLM_ASCEND_BALANCE_SCHEDULING=1 export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --host 0.0.0.0 \ --port 8004 \ --data-parallel-size 2 \ --data-parallel-size-local 1 \ --data-parallel-start-rank 0 \ --data-parallel-address $local_ip \ --data-parallel-rpc-port 13389 \ --tensor-parallel-size 8 \ --enable-expert-parallel \ --seed 1024 \ --max-model-len 140000 \ --max-num-batched-tokens 8192 \ --max-num-seqs 16 \ --quantization ascend \ --trust-remote-code \ --gpu-memory-utilization 0.9 \ --enable-auto-tool-choice \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --served-model-name glm47 \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --compilation-config '{"cudagraph_capture_sizes": [1,2,4,8,16,32,64,128,256,512], "cudagraph_mode": "FULL_DECODE_ONLY"}' \ --additional-config '{"enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' ``` **Node 1** ```shell #!/bin/sh # this obtained through ifconfig # nic_name is the network interface name corresponding to local_ip of the current node nic_name="xxxx" local_ip="xxxx" node0_ip="xxxx" # same as the local_IP address in node 0 export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export HCCL_BUFFSIZE=512 export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export HCCL_OP_EXPANSION_MODE=AIV export VLLM_ASCEND_BALANCE_SCHEDULING=1 export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --host 0.0.0.0 \ --port 8004 \ --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 \ --enable-expert-parallel \ --seed 1024 \ --max-model-len 140000 \ --max-num-batched-tokens 8192 \ --max-num-seqs 16 \ --quantization ascend \ --trust-remote-code \ --gpu-memory-utilization 0.9 \ --enable-auto-tool-choice \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --served-model-name glm47 \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --compilation-config '{"cudagraph_capture_sizes": [1,2,4,8,16,32,64,128,256,512], "cudagraph_mode": "FULL_DECODE_ONLY"}' \ --additional-config '{"enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' ``` ### Prefill-Decode Disaggregation We'd like to show the deployment guide of `GLM-4.7` on multi-node environment with 2P1D for better performance. Before you start, please 1. prepare the script `launch_online_dp.py` on 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. prepare the script `run_dp_template.sh` on each node. 1. Prefill node 0 ```shell nic_name="xxxx" # change to your own nic name local_ip="xxxx" # change to your own ip 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=256 export HCCL_OP_EXPANSION_MODE="AIV" export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 export ASCEND_RT_VISIBLE_DEVICES=$1 export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --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 $7 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name glm \ --max-model-len 133000 \ --max-num-batched-tokens 8192 \ --trust-remote-code \ --max-num-seqs 64 \ --gpu-memory-utilization 0.9 \ --quantization ascend \ --enforce-eager \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --profiler-config '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --additional-config '{"recompute_scheduler_enable": true, "enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_producer", "kv_port": "30000", "engine_id": "0", "kv_connector_extra_config": { "prefill": { "dp_size": 2, "tp_size": 8 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' 2>&1 ``` 2. Prefill node 1 ```shell nic_name="xxxx" # change to your own nic name local_ip="xxxx" # change to your own ip export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export HCCL_BUFFSIZE=256 export HCCL_OP_EXPANSION_MODE="AIV" export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 export ASCEND_RT_VISIBLE_DEVICES=$1 export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --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 $7 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name glm \ --max-model-len 133000 \ --max-num-batched-tokens 8192 \ --trust-remote-code \ --max-num-seqs 64 \ --gpu-memory-utilization 0.9 \ --quantization ascend \ --enforce-eager \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --profiler-config '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --additional-config '{"recompute_scheduler_enable": true, "enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_producer", "kv_port": "30100", "engine_id": "1", "kv_connector_extra_config": { "prefill": { "dp_size": 2, "tp_size": 8 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' 2>&1 ``` 3. Decode node 0 ```shell nic_name="xxxx" # change to your own nic name local_ip="xxxx" # change to your own ip export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export HCCL_BUFFSIZE=512 export HCCL_OP_EXPANSION_MODE="AIV" export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request. export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480 export TASK_QUEUE_ENABLE=1 export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 export VLLM_ASCEND_ENABLE_FUSED_MC2=1 export ASCEND_RT_VISIBLE_DEVICES=$1 vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --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 $7 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name glm \ --max-model-len 133000 \ --max-num-batched-tokens 128 \ --max-num-seqs 4 \ --trust-remote-code \ --gpu-memory-utilization 0.9 \ --quantization ascend \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --profiler-config \ '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[1,2,4,6,8,10,12,14,16,18,20,24,26,28,30,32,64,128,256,512]}' \ --additional-config '{"recompute_scheduler_enable": true, "enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_consumer", "kv_port": "30200", "engine_id": "2", "kv_connector_extra_config": { "prefill": { "dp_size": 2, "tp_size": 8 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' ``` 4. Decode node 1 ```shell nic_name="xxxx" # change to your own nic name local_ip="xxxx" # change to your own ip export HCCL_IF_IP=$local_ip export GLOO_SOCKET_IFNAME=$nic_name export TP_SOCKET_IFNAME=$nic_name export HCCL_SOCKET_IFNAME=$nic_name export HCCL_BUFFSIZE=512 export HCCL_OP_EXPANSION_MODE="AIV" export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True export OMP_PROC_BIND=false export OMP_NUM_THREADS=1 export ASCEND_AGGREGATE_ENABLE=1 export ASCEND_TRANSPORT_PRINT=1 export ACL_OP_INIT_MODE=1 export ASCEND_A3_ENABLE=1 # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request. export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480 export TASK_QUEUE_ENABLE=1 export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH export VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1 export VLLM_ASCEND_ENABLE_FUSED_MC2=1 export ASCEND_RT_VISIBLE_DEVICES=$1 vllm serve Eco-Tech/GLM-4.7-W8A8-floatmtp \ --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 glm \ --max-model-len 133000 \ --max-num-batched-tokens 128 \ --max-num-seqs 4 \ --trust-remote-code \ --gpu-memory-utilization 0.9 \ --quantization ascend \ --speculative-config '{"num_speculative_tokens": 3, "method":"mtp"}' \ --profiler-config \ '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile", "torch_profiler_with_stack": false}' \ --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[1,2,4,6,8,10,12,14,16,18,20,24,26,28,30,32,64,128,256,512]}' \ --additional-config '{"recompute_scheduler_enable": true, "enable_shared_expert_dp": true, "ascend_fusion_config": {"fusion_ops_gmmswigluquant": false}}' \ --kv-transfer-config \ '{"kv_connector": "MooncakeConnectorV1", "kv_role": "kv_consumer", "kv_port": "30200", "engine_id": "2", "kv_connector_extra_config": { "prefill": { "dp_size": 2, "tp_size": 8 }, "decode": { "dp_size": 8, "tp_size": 4 } } }' ``` Once the preparation is done, you can start the server with the following command on each node: 1. Prefill node 0 ```shell # change ip to your own python launch_online_dp.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address $node_p0_ip --dp-rpc-port 12880 --vllm-start-port 9300 ``` 2. Prefill node 1 ```shell # change ip to your own python launch_online_dp.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address $node_p1_ip --dp-rpc-port 12880 --vllm-start-port 9300 ``` 3. Decode node 0 ```shell # change ip to your own python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address $node_d0_ip --dp-rpc-port 12778 --vllm-start-port 9300 ``` 4. Decode node 1 ```shell # change ip to your own python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address $node_d0_ip --dp-rpc-port 12778 --vllm-start-port 9300 ``` ### 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](https://github.com/vllm-project/vllm-ascend/blob/main/examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py) ```shell 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 \ 9300 9301 \ 9300 9301 \ --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 \ --decoder-ports \ 9300 9301 9302 9303 \ 9300 9301 9302 9303 ``` ## Functional Verification Once your server is started, you can query the model with input prompts: ```shell curl -H "Accept: application/json" \ -H "Content-type: application/json" \ -X POST \ -d '{ "model": "glm", "messages": [{ "role": "user", "content": "The future of AI is" }], "stream": false, "ignore_eos": false, "temperature": 0, "max_tokens": 200 }' http://:/v1/chat/completions ``` ## 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 `GLM4.7` in `vllm-ascend:main` (after `vllm-ascend:0.14.0rc1`) for reference only. | dataset | version | metric | mode | vllm-api-general-chat | note | |----- | ----- | ----- | ----- | -----| ----- | | GPQA | - | accuracy | gen | 84.85 | 1 Atlas 800 A3 (64G × 16) | | MATH500 | - | accuracy | gen | 98.8 | 1 Atlas 800 A3 (64G × 16) | ### Using Language Model Evaluation Harness Not tested 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 `GLM-4.x` as an example. Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/benchmarking/) for more details. There are three `vllm bench` subcommands: - `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 \ --backend vllm \ --dataset-name prefix_repetition \ --prefix-repetition-prefix-len 22400 \ --prefix-repetition-suffix-len 9600 \ --prefix-repetition-output-len 1024 \ --num-prompts 1 \ --prefix-repetition-num-prefixes 1 \ --ignore-eos \ --model glm \ --tokenizer Eco-Tech/GLM-4.7-W8A8-floatmtp \ --seed 1000 \ --host 0.0.0.0 \ --port 8000 \ --endpoint /v1/completions \ --max-concurrency 1 \ --request-rate 1 ``` After about several minutes, you can get the performance evaluation result. ## Best Practices In this chapter, we recommend best practices for three scenarios: - Long-context: For long sequences with low concurrency (≤ 4): set `dp1 tp16`; For long sequences with high concurrency (> 4): set `dp2 tp8` - Low-latency: For short sequences with low latency: we recommend setting `dp2 tp8` - High-throughput: For short sequences with high throughput: we also recommend setting `dp2 tp8` **Notice:** `max-model-len` and `max-num-seqs` need to be set according to the actual usage scenario. For other settings, please refer to the **[Deployment](#deployment)** chapter. ## FAQ - **Q: Startup fails with HCCL port conflicts (address already bound). What should I do?** A: Clean up old processes and restart: `pkill -f VLLM*`. - **Q: How to handle OOM or unstable startup?** A: Reduce `--max-num-seqs` and `--max-model-len` first. If needed, reduce concurrency and load-testing pressure (e.g., `max-concurrency` / `num-prompts`).