[CI][Doc] Optimize multi-node CI (#3565)
### What this PR does / why we need it?
This pull request mainly do the following things:
1. Add a doc for multi-node CI, The main content is the mechanism
principle and how to contribute
2. Simplify the config yaml for more developer-friendly
3. Optimized the mooncake installation script to prevent accidental
failures during installation
4. Fix the workflow to ensure the kubernetes can be apply correctly
5. Add Qwen3-235B-W8A8 disaggregated_prefill test
6. Add GLM-4.5 multi dp test
7. Add 2p1d 4nodes disaggregated_prefill test
8. Refactor nightly tests
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
163
tests/e2e/nightly/multi_node/config/models/DeepSeek-R1-W8A8.yaml
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163
tests/e2e/nightly/multi_node/config/models/DeepSeek-R1-W8A8.yaml
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test_name: "test DeepSeek-R1-W8A8 disaggregated_prefill"
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model: "vllm-ascend/DeepSeek-R1-0528-W8A8"
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num_nodes: 4
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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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HCCL_BUFFSIZE: 1024
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SERVER_PORT: 8080
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OMP_PROC_BIND: false
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OMP_NUM_THREADS: 10
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PYTORCH_NPU_ALLOC_CONF: expandable_segments:True
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HCCL_DETERMINISTIC: True
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TASK_QUEUE_ENABLE: 1
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HCCL_OP_RETRY_ENABLE: "L0:0, L1:0, L2:0"
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disaggregated_prefill:
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enabled: true
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prefiller_host_index: [0, 1]
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decoder_host_index: [2]
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ranktable_gen_path: "examples/disaggregated_prefill_v1/gen_ranktable.py"
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ranktable_path: "/tmp/ranktable.json"
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deployment:
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-
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server_cmd: >
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vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
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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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--tensor-parallel-size 8
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--enforce-eager
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--enable-expert-parallel
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--seed 1024
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--quantization ascend
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--max-num-seqs 4
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--max-model-len 36864
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--max-num-batched-tokens 16384
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--trust-remote-code
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--gpu-memory-utilization 0.9
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--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
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--kv-transfer-config
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'{"kv_connector": "LLMDataDistCMgrConnector",
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"kv_buffer_device": "npu",
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"kv_role": "kv_producer",
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"kv_parallel_size": 1,
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"kv_port": "20001",
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"engine_id": "0",
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"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
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}'
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--additional-config
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'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":false,"enable_multistream_shared_expert":false},"enable_prefill_optimizations":true,"enable_weight_nz_layout":true}'
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-
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server_cmd: >
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vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
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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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--tensor-parallel-size 8
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--enforce-eager
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--enable-expert-parallel
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--seed 1024
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--quantization ascend
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--max-num-seqs 4
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--max-model-len 36864
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--max-num-batched-tokens 16384
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--trust-remote-code
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--gpu-memory-utilization 0.9
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--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
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--kv-transfer-config
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'{"kv_connector": "LLMDataDistCMgrConnector",
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"kv_buffer_device": "npu",
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"kv_role": "kv_producer",
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"kv_parallel_size": 1,
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"kv_port": "20001",
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"engine_id": "0",
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"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
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}'
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--additional-config
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'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":false,"enable_multistream_shared_expert":false},"enable_prefill_optimizations":true,"enable_weight_nz_layout":true}'
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-
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server_cmd: >
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vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
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--host 0.0.0.0
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--port $SERVER_PORT
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--data-parallel-size 32
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--data-parallel-size-local 16
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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 1
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--enable-expert-parallel
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--seed 1024
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--quantization ascend
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--max-num-seqs 28
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--max-model-len 36864
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--max-num-batched-tokens 256
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--trust-remote-code
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--gpu-memory-utilization 0.9
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--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
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--kv-transfer-config
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'{"kv_connector": "LLMDataDistCMgrConnector",
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"kv_buffer_device": "npu",
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"kv_role": "kv_consumer",
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"kv_parallel_size": 1,
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"kv_port": "20001",
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"engine_id": "0",
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"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
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}'
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--additional-config
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'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":true,"enable_multistream_mla":true,"graph_batch_sizes":[28],"use_cached_graph":true,"enable_super_kernel":false},"multistream_overlap_shared_expert":true}'
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-
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server_cmd: >
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vllm serve vllm-ascend/DeepSeek-R1-0528-W8A8
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--headless
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--data-parallel-size 32
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--data-parallel-size-local 16
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--data-parallel-start-rank 16
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--data-parallel-address $MASTER_IP
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--data-parallel-rpc-port 13389
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--tensor-parallel-size 1
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--enable-expert-parallel
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--seed 1024
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--quantization ascend
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--max-num-seqs 28
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--max-model-len 36864
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--max-num-batched-tokens 256
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--trust-remote-code
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--gpu-memory-utilization 0.9
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--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}'
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--kv-transfer-config
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'{"kv_connector": "LLMDataDistCMgrConnector",
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"kv_buffer_device": "npu",
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"kv_role": "kv_consumer",
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"kv_parallel_size": 1,
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"kv_port": "20001",
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"engine_id": "0",
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"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
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}'
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--additional-config
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'{"ascend_scheduler_config":{"enabled":false},"torchair_graph_config":{"enabled":true,"enable_multistream_mla":true,"graph_batch_sizes":[28],"use_cached_graph":true,"enable_super_kernel":false},"multistream_overlap_shared_expert":true}'
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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-bs400
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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: 1
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max_out_len: 2
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batch_size: 1
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baseline: 5
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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/AIME2024
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request_conf: vllm_api_general_chat
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dataset_conf: aime2024/aime2024_gen_0_shot_chat_prompt
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max_out_len: 10
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batch_size: 32
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baseline: 1
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threshold: 1
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