53 lines
3.3 KiB
Bash
Executable File
53 lines
3.3 KiB
Bash
Executable File
# BI-V100 patch script for Qwen3.6-27B (Qwen3_5 architecture)
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#
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# Triton situation on BI-V100:
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# - Standard Triton 2.3.1 is already present in the image.
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# - HAS_TRITON = False (hardcoded in vendor vllm), but Triton is still used
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# for TP-mode cache management (custom_cache_manager / libentry).
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# - The vendor's triton_utils/__init__.py, custom_cache_manager.py, libentry.py
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# are already correct for standard Triton 2.3.1 — do NOT overwrite them.
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# - DO NOT install BI-V150 corex Triton 2.1.0 (pkgs/triton): that causes
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# GPU hang on BI-V100 because the Triton CUDA PTX kernels are incompatible.
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#
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# Important Note: Qwen3.6-27B must apply TP=4,PP=2 combination in order to deploy using 8 GPUs
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#
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# Recommended server start command for TP=4, context length: 50K, no chunked prefill mechanism:
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# CUDA_VISIBLE_DEVICES="4,5,6,7" VLLM_ENGINE_ITERATION_TIMEOUT_S=3600 python3 -m vllm.entrypoints.openai.api_server \
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# --model /workspace/models/Qwen3.6-27B --port 1111 --served-model-name llm \
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# --max-model-len 50000 --enforce-eager --trust-remote-code -tp 4 --gpu-memory-utilization 0.90 \
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# --max-num-seqs 1 --disable-log-requests --disable-frontend-multiprocessing \
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# --max-num-batched-tokens 50000
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# Recommended server start command for TP=4 support 100K, need chunked prefill
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# CUDA_VISIBLE_DEVICES="4,5,6,7" VLLM_ENGINE_ITERATION_TIMEOUT_S=3600 python3 -m vllm.entrypoints.openai.api_server \
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# --model /workspace/models/Qwen3.6-27B --port 1111 --served-model-name llm \
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# --max-model-len 100000 --enforce-eager --trust-remote-code -tp 8 --gpu-memory-utilization 0.95 \
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# --max-num-seqs 1 --disable-log-requests --disable-frontend-multiprocessing \
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# --max-num-batched-tokens 4096 --enable-chunked-prefill
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# --- paged_attn.py: replace forward_prefix with pure-PyTorch fallback -------
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# The Triton context_attention_fwd kernel hangs BI-V100 GPUs permanently
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# (standard Triton 2.3.1 PTX is not supported by the corex runtime either).
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# Our paged_attn.py bypasses it entirely via _forward_prefix_pytorch, which
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# also implements query-chunking (_ATTN_Q_CHUNK=256) to keep peak attention
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# memory at O(256 × kv_len) instead of O(q_len × kv_len).
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cp ./paged_attn.py /usr/local/corex/lib64/python3/dist-packages/vllm/attention/ops/paged_attn.py
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# --- transformers: Qwen3_5 tokenizer / model files --------------------------
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pip install transformers==4.55.3 -i https://pypi.tuna.tsinghua.edu.cn/simple
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cp -r ./qwen3_5 /usr/local/lib/python3.10/site-packages/transformers/models/
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python3 ./patch_transformers_qwen3_5.py
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# --- vllm model: Qwen3.6-27B (Qwen3_5 arch) --------------------------------
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cp ./mamba_cache.py /usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/
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cp ./qwen3_5.py /usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/qwen3_5.py
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python3 ./patch_vllm_qwen3_5.py
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# --- xformers: bypass cudnnFlashAttnForward (head_dim=256 > 128 limit) ------
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# Injects _run_sdpa_fallback (pure matmul+softmax) into xformers.py.
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# Required because head_dim=256 > 128 and ixformer flash attention either
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# crashes (is_causal=True) or produces wrong output (attn_mask path).
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# The fallback uses query_start_loc to derive actual query lengths, so it
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# works correctly during profiling runs with chunked-prefill-style batches.
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python3 ./patch_xformers_sdpa_seq.py
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