638858a317
Two optimizations that target the real bottlenecks:
1. patch_enable_triton.py: Enable Triton Flash Attention for prefill
- Sets HAS_TRITON = True (was hardcoded False)
- Adds try/except wrapper in forward_prefix: tries Triton kernel first,
permanently falls back to PyTorch if it hangs or errors
- Combined with patch_triton_tuning.py (BLOCK=64, NUM_WARPS=4),
this keeps SMEM at 32KB ≤ 48KB limit
- If Triton works: 10-50x prefill speedup (GPU-parallel Flash Attention
vs Python for-loop)
- If Triton still hangs: auto-fallback, no worse than baseline
2. patch_vectorized_decode.py: Raise _PYTORCH_DECODE_THRESHOLD 32768 → 65536
- Compiled ixf_F.paged_attention_v1 is ~100x faster than Python fallback
- Baseline conservatively falls back at 32K, may work fine at 64K
- If v1 crashes at higher seq_lens, threshold can be lowered back
Why these matter (competition scoring):
Token吞吐加权值 = Output TPS × 16.796 + Input TPS × 2.799 + Cache TPS × 0.56
Prefill (Input TPS, 14% weight): _forward_prefix_pytorch is a Python
for-loop doing matmul+softmax per tile. Triton kernel does this in a
single GPU launch with Flash Attention online softmax.
Decode (Output TPS, 83% weight): Every seq_len between 32K-65K that
stays on compiled v1 instead of falling to Python saves ~100x per token.
Deploy order in Dockerfile:
1. patch_ops.sh (baseline functional patches)
2. patch_triton_tuning.py (BLOCK=64, NUM_WARPS=4)
3. patch_enable_triton.py (HAS_TRITON=True + try/fallback)
4. patch_vectorized_decode.py (threshold 32K → 64K)
29 lines
1.2 KiB
Docker
29 lines
1.2 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 and the V2 module
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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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# 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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# 1. PagedAttention V2 — fills the NotImplementedError hole
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# Enables partitioned attention for long sequences (>8192 tokens)
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RUN python3 /workspace/qwen3_6_scripts/patch_paged_attention_v2.py
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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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# 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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