09b3f9d91b
### What this PR does / why we need it?
This PR adds online **Disaggregated Prefill/Decode** performance and
accuracy tests for the **Qwen3-235B-A22B** and
**Qwen3-VL-235B-A22B-Instruct** models to the Nightly test suite.
These test configurations simulate the deployment of massive MoE and
Vision-Language models in **a dual-node (32 NPU)** environment,
utilizing Mooncake (KVCache Transfer) technology to achieve efficient KV
cache transfer between the Prefill node and the Decode node.
#### Test Configuration
**Qwen3-235B-A22B**
- Model: Qwen/Qwen3-235B-A22B
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Disaggregated Prefill & Decode
- Node 0 (Producer/Prefill): **DP2 + TP8 + EP + FLASHCOMM1 +
FUSED_MC2**.
- Node 1 (Consumer/Decode): **DP4 + TP4 + EP + FLASHCOMM1 + FUSED_MC2 +
FULL_DECODE_ONLY**.
- Benchmarks:
- Performance: vllm-ascend/GSM8K-in3500-bs2800.
- Accuracy: vllm-ascend/gsm8k-lite.
**Qwen3-VL-235B-A22B-Instruct**
- Model: Qwen/Qwen3-VL-235B-A22B-Instruct
- Hardware: A3, 2 Nodes (32 NPUs total, 16 NPUs per node)
- Architecture: Disaggregated Prefill & Decode
- Node 0 (Producer/Prefill): **DP2 + TP8 + EP**.
- Node 1 (Consumer/Decode): **DP4 + TP4 + EP + FULL_DECODE_ONLY**.
- Benchmarks:
- Performance: vllm-ascend/textvqa-perf-1080p.
- Accuracy: vllm-ascend/textvqa-lite.
### How was this patch tested?
Nightly test action on CI
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
122 lines
3.6 KiB
YAML
122 lines
3.6 KiB
YAML
test_name: "test Qwen3-235B-A22B disaggregated_prefill"
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model: "Qwen/Qwen3-235B-A22B"
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num_nodes: 2
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npu_per_node: 16
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env_common:
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VLLM_USE_MODELSCOPE: true
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PYTORCH_NPU_ALLOC_CONF: expandable_segments:True
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HCCL_BUFFSIZE: 1024
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HCCL_OP_EXPANSION_MODE: "AIV"
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OMP_PROC_BIND: false
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OMP_NUM_THREADS: 1
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VLLM_ASCEND_ENABLE_FLASHCOMM1: 1
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VLLM_ASCEND_ENABLE_FUSED_MC2: 2
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TASK_QUEUE_ENABLE: 1
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SERVER_PORT: 8080
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disaggregated_prefill:
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enabled: true
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prefiller_host_index: [0]
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decoder_host_index: [1]
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deployment:
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-
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server_cmd: >
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vllm serve "Qwen/Qwen3-235B-A22B"
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--host 0.0.0.0
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--port $SERVER_PORT
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--data-parallel-size 2
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--data-parallel-size-local 2
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--data-parallel-start-rank 0
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--data-parallel-address $LOCAL_IP
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--data-parallel-rpc-port 13389
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--tensor-parallel-size 8
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--seed 1024
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--max-num-seqs 32
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--max-model-len 8192
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--max-num-batched-tokens 8192
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--enable-expert-parallel
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--trust-remote-code
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--gpu-memory-utilization 0.9
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--no-enable-prefix-caching
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--kv-transfer-config
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'{"kv_connector": "MooncakeConnectorV1",
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"kv_role": "kv_producer",
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"kv_port": "30000",
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"engine_id": "0",
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"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
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"kv_connector_extra_config": {
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"use_ascend_direct": true,
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"prefill": {
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"dp_size": 2,
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"tp_size": 8
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},
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"decode": {
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"dp_size": 4,
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"tp_size": 4
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}
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}
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}'
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-
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server_cmd: >
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vllm serve "Qwen/Qwen3-235B-A22B"
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--host 0.0.0.0
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--port $SERVER_PORT
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--data-parallel-size 4
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--data-parallel-size-local 4
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--data-parallel-start-rank 0
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--data-parallel-address $LOCAL_IP
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--data-parallel-rpc-port 13389
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--tensor-parallel-size 4
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--seed 1024
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--max-num-seqs 32
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--max-model-len 8192
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--max-num-batched-tokens 8192
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--enable-expert-parallel
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--trust-remote-code
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--gpu-memory-utilization 0.9
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--no-enable-prefix-caching
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--compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'
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--async-scheduling
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--kv-transfer-config
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'{"kv_connector": "MooncakeConnectorV1",
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"kv_role": "kv_consumer",
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"kv_port": "30100",
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"engine_id": "1",
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"kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
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"kv_connector_extra_config": {
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"use_ascend_direct": true,
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"prefill": {
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"dp_size": 2,
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"tp_size": 8
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},
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"decode": {
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"dp_size": 4,
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"tp_size": 4
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}
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}
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}'
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benchmarks:
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perf:
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case_type: performance
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dataset_path: vllm-ascend/GSM8K-in3500-bs2800
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request_conf: vllm_api_stream_chat
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dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_str_perf
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num_prompts: 2800
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max_out_len: 1500
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batch_size: 700
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request_rate: 11.2
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baseline: 1
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threshold: 0.97
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acc:
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case_type: accuracy
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dataset_path: vllm-ascend/gsm8k-lite
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request_conf: vllm_api_general_chat
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dataset_conf: gsm8k/gsm8k_gen_0_shot_cot_chat_prompt
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max_out_len: 7680
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batch_size: 512
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baseline: 97
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threshold: 3
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