9f93d695a9
muh_dispatch.py: - Fix missing os/sys imports (was crashing on import) - Fix SM count 50→16 (confirmed via ixsmi, matches hardware.cuh) - Fix C++ struct name lookup to match actual tuning_reduce.cuh names: bi100_plus_float32_o4, bi100_plus_float64_o4, bi100_plus_accum2_o4 (was: bi100_float32_plus_o4 — wrong name, would always fall through to default) Dockerfile: - Add COPY for prefix_prefill.py and muh_dispatch.py - Deploy CCCL-tuned prefix_prefill.py into vllm attention ops (BLOCK=64, NUM_WARPS=4 for BI-V100 SM=16) - Deploy muh_dispatch.py into vllm package for type-dispatched kernel configs - These files were written but never deployed — dead code until now Impact: prefix_prefill.py deployment means the CCCL-derived block sizes actually take effect at runtime. Previously the base image's original prefix_prefill.py (BLOCK=128 for cc>=80, or 64 for cc<80) was used, which is correct for BI-V100 but our version adds explicit SM=16 documentation and the path for future tuning.
64 lines
3.3 KiB
Docker
64 lines
3.3 KiB
Docker
FROM git.modelhub.org.cn:9443/enginex-iluvatar/bi100-3.2.3-x86-ubuntu20.04-py3.10-poc-llm-infer:v1.2.3
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RUN mkdir /workspace
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WORKDIR /workspace/
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# Copy all scripts, V2 kernels, CCCL-tuned prefill, and muh dispatch
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COPY ./qwen3_6_scripts /workspace/qwen3_6_scripts
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COPY ./paged_attention_v2_pytorch.py /workspace/paged_attention_v2_pytorch.py
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COPY ./paged_attention_v2_triton.py /workspace/paged_attention_v2_triton.py
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COPY ./prefix_prefill.py /workspace/prefix_prefill.py
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COPY ./muh_dispatch.py /workspace/muh_dispatch.py
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# Run baseline patches (model registration, xformers fallback, tool parser, etc.)
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RUN cd ./qwen3_6_scripts && ./patch_ops.sh
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# CRITICAL: Enable ixformer native V1/V2 paged attention kernels.
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# Fixes: V1 head_mapping int→Tensor, V2 NotImplementedError → native kernel,
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# Triton path mismatch.
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RUN python3 /workspace/qwen3_6_scripts/patch_ixformer_native.py
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# 1. PagedAttention V2 — fills the NotImplementedError hole
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# Enables partitioned attention for long sequences (>8192 tokens)
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# Deploy BOTH PyTorch and Triton V2 to vllm package — _custom_ops.py
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# tries Triton first, falls back to PyTorch if import/runtime fails.
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# Triton V2 risk: SMEM=32KB zero margin at head_dim=256 BLOCK_N=32.
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# If Triton V2 crashes, PyTorch V2 (batched bmm, no intermediate tensor
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# savings but correct) takes over automatically via try/except.
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# Deploy Triton V2 kernel into vllm package
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RUN cp /workspace/paged_attention_v2_triton.py \
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/usr/local/corex/lib/python3/dist-packages/vllm/paged_attention_v2_triton.py 2>/dev/null || \
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cp /workspace/paged_attention_v2_triton.py \
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/usr/local/corex/lib64/python3/dist-packages/vllm/paged_attention_v2_triton.py 2>/dev/null || true
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RUN python3 /workspace/qwen3_6_scripts/patch_paged_attention_v2.py
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# Deploy CCCL-tuned prefix_prefill.py (SM=16: BLOCK=64, NUM_WARPS=4)
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RUN cp /workspace/prefix_prefill.py \
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/usr/local/corex/lib/python3/dist-packages/vllm/attention/ops/prefix_prefill.py 2>/dev/null || \
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cp /workspace/prefix_prefill.py \
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/usr/local/corex/lib64/python3/dist-packages/vllm/attention/ops/prefix_prefill.py 2>/dev/null || true
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# Deploy muh_dispatch.py (CCCL-style type dispatch for kernel configs)
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RUN cp /workspace/muh_dispatch.py \
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/usr/local/corex/lib/python3/dist-packages/vllm/muh_dispatch.py 2>/dev/null || \
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cp /workspace/muh_dispatch.py \
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/usr/local/corex/lib64/python3/dist-packages/vllm/muh_dispatch.py 2>/dev/null || true
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# 2. Triton kernel tuning: BLOCK=64, NUM_WARPS=4
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# SMEM: BLOCK_N=64 × head_dim=128 × 2B × 2(K+V) = 32KB ≤ 48KB
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# Occupancy: 4 warps allows 2 blocks/SM vs 1 at 8 warps
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RUN python3 /workspace/qwen3_6_scripts/patch_triton_tuning.py
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# 3. Enable Triton kernels with automatic fallback to PyTorch if they hang
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# Triton Flash Attention is 10-50x faster than PyTorch for-loop fallback
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RUN python3 /workspace/qwen3_6_scripts/patch_enable_triton.py
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# 5. head_dim=256 support: Qwen3.6 uses head_dim=256
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# BLOCK=64 overflows SMEM (64×256×2×2=64KB > 48KB)
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# → BLOCK=32 for head_dim=256 (32×256×2×2=32KB ≤ 48KB)
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RUN python3 /workspace/qwen3_6_scripts/patch_head256_triton.py
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# 4. Raise decode threshold: compiled paged_attention_v1 up to 65536
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# instead of falling back to Python at 32768
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RUN python3 /workspace/qwen3_6_scripts/patch_vectorized_decode.py
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