33f6ead1b8
Phase 1 kernel (_paged_attn_v2_partition_kernel) now has complete
paged K/V gather implementation, adapted from prefix_prefill.py:
K gather:
bn = tl.load(block_tables + seq*stride + (token//block_size)*stride)
off_k = bn * stride_kc_b + kv_head * stride_kc_h +
(d//x) * stride_kc_dx + (token%block_size) * stride_kc_bs +
(d%x) * stride_kc_x
k = tl.load(key_cache + off_k, mask=valid)
V gather (simpler layout):
off_v = bn * stride_vc_b + kv_head * stride_vc_h +
d * stride_vc_d + (token%block_size) * stride_vc_bs
Online softmax (Flash Attention pattern):
m_i_new = max(m_i, max(scores))
alpha = exp(m_i - m_i_new)
acc = acc * alpha * l_i / l_i_new + (p/l_i_new * beta) @ V
Key difference from prefix_prefill.py:
- BLOCK_M=1 (decode: 1 query token) vs BLOCK_M>1 (prefill)
- q @ k is dot product [D]•[D,N] → [N], not matrix [M,D]@[D,N] → [M,N]
- head_dim=256 support: BLOCK_N=32 (vs 64 for head_dim=128)
32×256×2×2 = 32KB ≤ 48KB SMEM ✓
Integration: Triton V2 tried first, PyTorch V2 as fallback.
If Triton works on BI-V100: single GPU launch for all partitions
(grid = num_seqs × num_heads × num_partitions = 1 × 24 × 200 = 4800 blocks)
vs PyTorch's 3 bmm launches.
35 lines
1.6 KiB
Docker
35 lines
1.6 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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COPY ./paged_attention_v2_triton.py /workspace/paged_attention_v2_triton.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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# 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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