15ad56a454
chunked_prefill requires max_num_batched_tokens >= max_model_len/max_num_seqs = 80000/2 = 40000. But we need small batched_tokens for profiling OOM. Without chunked_prefill, max_num_batched_tokens=2048 is fine for profiling and real inference processes full sequences in one pass.
52 lines
1.1 KiB
YAML
52 lines
1.1 KiB
YAML
concurrency: 1
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command:
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- python3
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- -m
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- vllm.entrypoints.openai.api_server
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- --model
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- /model
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- --served-model-name
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- llm
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- --max-model-len
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- '80000'
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- --gpu-memory-utilization
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- '0.80'
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- --trust-remote-code
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- -tp
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- '4'
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- --max-num-seqs
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- '2'
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- --disable-log-requests
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- --disable-frontend-multiprocessing
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- --max-num-batched-tokens
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- '2048'
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- --max-seq-len-to-capture
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- '32768'
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- --enable-auto-tool-choice
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- --tool-call-parser
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- qwen3_coder
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- --reasoning-parser
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- qwen3
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- --enable-prefix-caching
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- --enforce-eager
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- --dtype
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- half
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env:
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- name: VLLM_ENGINE_ITERATION_TIMEOUT_S
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value: 3600
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- name: BI100_MOE_COREX_DIRECT_ROUTED
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value: 1
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- name: BI100_GDN_COREX_PACKED_DECODE
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value: 1
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- name: BI100_HYBRID_KV_ACCOUNTING
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value: full_attention
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- name: BI100_GDN_CACHE_POLICY
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value: admission64
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- name: BI100_GDN_RESTORE_MODE
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value: hybrid64
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- name: BI100_MOE_COREX_TOPK_SOFTMAX
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value: '1'
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- name: PYTORCH_CUDA_ALLOC_CONF
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value: max_split_size_mb:512
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