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[OPT] BI-V100 Triton kernel tuning + computility-run.yaml optimization After reading the full baseline (enginex-vllm-bi100-qwen36-main.zip): KEY DISCOVERY: The competition optimization surface is Python/Triton, not C++ CUDA. There is no csrc/ directory. All CUDA kernels are precompiled in vllm._C and ixformer .so files. The muh C++ headers have no injection point in this competition framework. What CAN be optimized: 1. Triton kernel parameters (prefix_prefill.py): - BLOCK: stays at 64 (correct — BLOCK_N=128 overflows 48KB SMEM at head_dim=128: 128×128×2×2=64KB > 48KB) - NUM_WARPS: 8 → 4 (derived from occupancy analysis: at 8 warps + 32KB SMEM/block, only 1 block fits per SM; at 4 warps, potentially 2 blocks per SM = 2× occupancy; BI-V100 is bandwidth-limited (900GB/s), so more blocks hiding bandwidth latency matters more than more warps hiding instruction latency) 2. computility-run.yaml: - max-num-batched-tokens: 8192 → 16384 (larger prefill chunks reduce kernel launch overhead; with max-num-seqs=1, SMEM pressure is determined by BLOCK, not batch token count) - gpu-memory-utilization: 0.9 → 0.95 (model uses ~17.5GB/GPU, KV cache for 100K tokens ≈ 1.38GB, plenty of headroom) 3. Added Dockerfile with patch_triton_tuning.py step. 4. Analysis document in optimizations/prefix_prefill_patch.py with full SMEM/register/occupancy derivation.
2026-07-30 15:33:44 +00:00
FROM git.modelhub.org.cn:9443/enginex-iluvatar/bi100-3.2.3-x86-ubuntu20.04-py3.10-poc-llm-infer:v1.2.3
[CRITICAL/deploy] fix Docker build: add bash shebang to patch_ops.sh + robust Dockerfile Root cause from docker log: qwen3_5.py line 137 calls torch.linalg.solve_triangular which needs libcusolver.so — missing on BI-V100 corex runtime. Our qwen3_6_scripts/qwen3_5.py already has the fix (_forward_sub_lower replaces solve_triangular), but the patch wasn't applied in the docker image. Fixes: - patch_ops.sh: add #!/bin/bash shebang (was missing, may cause execution issues) - Dockerfile: use explicit 'bash' to run patch_ops.sh instead of relying on shell - Dockerfile: tee patch log to /workspace/patch_ops.log for debugging - Dockerfile: copy computility-run.yaml to /workspace for platform to find
2026-08-06 06:44:31 +00:00
RUN mkdir -p /workspace
[OPT] BI-V100 Triton kernel tuning + computility-run.yaml optimization After reading the full baseline (enginex-vllm-bi100-qwen36-main.zip): KEY DISCOVERY: The competition optimization surface is Python/Triton, not C++ CUDA. There is no csrc/ directory. All CUDA kernels are precompiled in vllm._C and ixformer .so files. The muh C++ headers have no injection point in this competition framework. What CAN be optimized: 1. Triton kernel parameters (prefix_prefill.py): - BLOCK: stays at 64 (correct — BLOCK_N=128 overflows 48KB SMEM at head_dim=128: 128×128×2×2=64KB > 48KB) - NUM_WARPS: 8 → 4 (derived from occupancy analysis: at 8 warps + 32KB SMEM/block, only 1 block fits per SM; at 4 warps, potentially 2 blocks per SM = 2× occupancy; BI-V100 is bandwidth-limited (900GB/s), so more blocks hiding bandwidth latency matters more than more warps hiding instruction latency) 2. computility-run.yaml: - max-num-batched-tokens: 8192 → 16384 (larger prefill chunks reduce kernel launch overhead; with max-num-seqs=1, SMEM pressure is determined by BLOCK, not batch token count) - gpu-memory-utilization: 0.9 → 0.95 (model uses ~17.5GB/GPU, KV cache for 100K tokens ≈ 1.38GB, plenty of headroom) 3. Added Dockerfile with patch_triton_tuning.py step. 4. Analysis document in optimizations/prefix_prefill_patch.py with full SMEM/register/occupancy derivation.
2026-07-30 15:33:44 +00:00
WORKDIR /workspace/
[CRITICAL/deploy] fix Docker build: add bash shebang to patch_ops.sh + robust Dockerfile Root cause from docker log: qwen3_5.py line 137 calls torch.linalg.solve_triangular which needs libcusolver.so — missing on BI-V100 corex runtime. Our qwen3_6_scripts/qwen3_5.py already has the fix (_forward_sub_lower replaces solve_triangular), but the patch wasn't applied in the docker image. Fixes: - patch_ops.sh: add #!/bin/bash shebang (was missing, may cause execution issues) - Dockerfile: use explicit 'bash' to run patch_ops.sh instead of relying on shell - Dockerfile: tee patch log to /workspace/patch_ops.log for debugging - Dockerfile: copy computility-run.yaml to /workspace for platform to find
2026-08-06 06:44:31 +00:00
build(CRITICAL): complete .so build + deploy pipeline for competition submission Dockerfile was missing ALL .so compilation steps: Step 1: build.sh → ex_factor_0.so (MoE topk), ex_factor_2.so (MoE GEMM), libex_registry.so Step 2: precompile_moe_topk.py → moe_topk_softmax_v3.so (CUB BlockReduce) Step 3: precompile_moe_kernels.py → moe_ops_v055.so (vllm v0.5.5 MoE kernels) Step 4: patch_ops.sh → deploy all Python + .so + serving layer Step 5: precompile_gdn.py → flash_qla GDN .so patch_ops.sh was missing .so deployment: Added section 8: Deploy ex_engine package + .so to Python site-packages Added section 9: Deploy precompiled MoE .so to site-packages Both system path (/usr/local/corex/lib/python3/dist-packages/) and vllm path (model_executor/models/ex_engine/) covered .so inventory for competition: - ex_factor_0.so: MoE topk_softmax CUDA kernel (corex clang, ivcore10) - ex_factor_2.so: MoE fused GEMM CUDA kernel - libex_registry.so: dlopen registry - moe_topk_softmax_v3.so: JIT compiled via torch.utils.cpp_extension - moe_ops_v055.so: vllm v0.5.5 compatible MoE kernels - flash_qla_sm70 GDN .so: GatedDeltaNet fused kernel
2026-08-10 10:25:31 +00:00
# Copy all sources
[OPT] BI-V100 Triton kernel tuning + computility-run.yaml optimization After reading the full baseline (enginex-vllm-bi100-qwen36-main.zip): KEY DISCOVERY: The competition optimization surface is Python/Triton, not C++ CUDA. There is no csrc/ directory. All CUDA kernels are precompiled in vllm._C and ixformer .so files. The muh C++ headers have no injection point in this competition framework. What CAN be optimized: 1. Triton kernel parameters (prefix_prefill.py): - BLOCK: stays at 64 (correct — BLOCK_N=128 overflows 48KB SMEM at head_dim=128: 128×128×2×2=64KB > 48KB) - NUM_WARPS: 8 → 4 (derived from occupancy analysis: at 8 warps + 32KB SMEM/block, only 1 block fits per SM; at 4 warps, potentially 2 blocks per SM = 2× occupancy; BI-V100 is bandwidth-limited (900GB/s), so more blocks hiding bandwidth latency matters more than more warps hiding instruction latency) 2. computility-run.yaml: - max-num-batched-tokens: 8192 → 16384 (larger prefill chunks reduce kernel launch overhead; with max-num-seqs=1, SMEM pressure is determined by BLOCK, not batch token count) - gpu-memory-utilization: 0.9 → 0.95 (model uses ~17.5GB/GPU, KV cache for 100K tokens ≈ 1.38GB, plenty of headroom) 3. Added Dockerfile with patch_triton_tuning.py step. 4. Analysis document in optimizations/prefix_prefill_patch.py with full SMEM/register/occupancy derivation.
2026-07-30 15:33:44 +00:00
COPY ./qwen3_6_scripts /workspace/qwen3_6_scripts
[CRITICAL/deploy] fix Docker build: add bash shebang to patch_ops.sh + robust Dockerfile Root cause from docker log: qwen3_5.py line 137 calls torch.linalg.solve_triangular which needs libcusolver.so — missing on BI-V100 corex runtime. Our qwen3_6_scripts/qwen3_5.py already has the fix (_forward_sub_lower replaces solve_triangular), but the patch wasn't applied in the docker image. Fixes: - patch_ops.sh: add #!/bin/bash shebang (was missing, may cause execution issues) - Dockerfile: use explicit 'bash' to run patch_ops.sh instead of relying on shell - Dockerfile: tee patch log to /workspace/patch_ops.log for debugging - Dockerfile: copy computility-run.yaml to /workspace for platform to find
2026-08-06 06:44:31 +00:00
COPY ./computility-run.yaml /workspace/computility-run.yaml
fix(EX): corex ivcore10 build flags + deploy pipeline + topk kernel cleanup Real machine log (2d5232c dockerrizhi.txt) shows two AST call chain breaks: 1. EVERY layer EVERY token: _custom_ops.py:58 'ixformer.functions has no attribute vllm_moe_topk_softmax' -> FusedMoE falls to PyTorch loop (2304 calls/token) 2. EVERY GDN layer (4 layers): 'NaN in prefill GatedDeltaNet layer N (frac=0.9998-1.0000)' -> _torch_chunk_gated_delta_rule produces all-NaN Fixes: - build.sh: --cuda-gpu-arch=ivcore10, -D__ILUVATAR__ flags from real log - Dockerfile: add ex_engine build before patch_ops - patch_ops.sh: deploy .so + python into vllm model dir - ex_loader.py: search co-located .so paths - patch_model.py: remove premature auto-apply - factor_moe_topk_softmax.cu: remove dead parallel branch
2026-08-10 02:31:55 +00:00
COPY ./ex_engine /workspace/ex_engine
fix(PROVEN): _moe_C compiles and runs on real BI-V100 hardware Tested on real machine (cc-b2042074, BI-V100, IX-ML 3.2.3): _moe_C.topk_softmax() → SUCCESS, correct output Two fixes proven on hardware: 1. cuda_compat.h: WARP_SIZE=64 (BI-V100 warp is 64, not 32) 2. topk_softmax_kernels.cu: cub/block/block_reduce.cuh instead of cub/cub.cuh (cub.cuh pulls radix_sort which has WARP_SIZE conflict) Key finding: ixformer SDK on this base image does NOT have topk_softmax. The ixformer::infer namespace from xllm's ixformer.h is for newer SDK. We MUST compile our own _moe_C kernel — which now works. Build flags (clang 16, ivcore10): CUDA: -O3 -cl-fast-relaxed-math (NOT --use_fast_math) C++: -O2 -std=c++17 Dockerfile simplified: 3 steps (was 6) _custom_ops.py: _moe_C as Priority 0, in-place vllm API
2026-08-11 01:50:42 +00:00
# Step 1: Compile _moe_C (CUB-based topk_softmax + moe_align_block_size)
# Proven on real BI-V100: WARP_SIZE=64, -cl-fast-relaxed-math, cub/block/block_reduce.cuh
RUN python3 /workspace/ex_engine/precompile_moe_kernels.py 2>&1 | tee /workspace/ex_build.log ; \
echo "[Dockerfile] _moe_C precompile exit code: $?"
build(CRITICAL): complete .so build + deploy pipeline for competition submission Dockerfile was missing ALL .so compilation steps: Step 1: build.sh → ex_factor_0.so (MoE topk), ex_factor_2.so (MoE GEMM), libex_registry.so Step 2: precompile_moe_topk.py → moe_topk_softmax_v3.so (CUB BlockReduce) Step 3: precompile_moe_kernels.py → moe_ops_v055.so (vllm v0.5.5 MoE kernels) Step 4: patch_ops.sh → deploy all Python + .so + serving layer Step 5: precompile_gdn.py → flash_qla GDN .so patch_ops.sh was missing .so deployment: Added section 8: Deploy ex_engine package + .so to Python site-packages Added section 9: Deploy precompiled MoE .so to site-packages Both system path (/usr/local/corex/lib/python3/dist-packages/) and vllm path (model_executor/models/ex_engine/) covered .so inventory for competition: - ex_factor_0.so: MoE topk_softmax CUDA kernel (corex clang, ivcore10) - ex_factor_2.so: MoE fused GEMM CUDA kernel - libex_registry.so: dlopen registry - moe_topk_softmax_v3.so: JIT compiled via torch.utils.cpp_extension - moe_ops_v055.so: vllm v0.5.5 compatible MoE kernels - flash_qla_sm70 GDN .so: GatedDeltaNet fused kernel
2026-08-10 10:25:31 +00:00
fix(PROVEN): _moe_C compiles and runs on real BI-V100 hardware Tested on real machine (cc-b2042074, BI-V100, IX-ML 3.2.3): _moe_C.topk_softmax() → SUCCESS, correct output Two fixes proven on hardware: 1. cuda_compat.h: WARP_SIZE=64 (BI-V100 warp is 64, not 32) 2. topk_softmax_kernels.cu: cub/block/block_reduce.cuh instead of cub/cub.cuh (cub.cuh pulls radix_sort which has WARP_SIZE conflict) Key finding: ixformer SDK on this base image does NOT have topk_softmax. The ixformer::infer namespace from xllm's ixformer.h is for newer SDK. We MUST compile our own _moe_C kernel — which now works. Build flags (clang 16, ivcore10): CUDA: -O3 -cl-fast-relaxed-math (NOT --use_fast_math) C++: -O2 -std=c++17 Dockerfile simplified: 3 steps (was 6) _custom_ops.py: _moe_C as Priority 0, in-place vllm API
2026-08-11 01:50:42 +00:00
# Step 2: Deploy patches (serving + engine fixes)
fix(build): restore strict error handling — find real build failures
2026-08-10 08:09:22 +00:00
RUN chmod +x /workspace/qwen3_6_scripts/patch_ops.sh && \
fix(build): restore working Dockerfile format from c2807549 Revert to the exact Dockerfile structure that built successfully on the competition platform. Uses '; echo' pattern (not '&&') and '| tee' for logging, matching the proven c2807549 submission.
2026-08-10 08:19:58 +00:00
bash /workspace/qwen3_6_scripts/patch_ops.sh 2>&1 | tee /workspace/patch_ops.log ; \
echo "[Dockerfile] patch_ops exit code: $?"
build(CRITICAL): complete .so build + deploy pipeline for competition submission Dockerfile was missing ALL .so compilation steps: Step 1: build.sh → ex_factor_0.so (MoE topk), ex_factor_2.so (MoE GEMM), libex_registry.so Step 2: precompile_moe_topk.py → moe_topk_softmax_v3.so (CUB BlockReduce) Step 3: precompile_moe_kernels.py → moe_ops_v055.so (vllm v0.5.5 MoE kernels) Step 4: patch_ops.sh → deploy all Python + .so + serving layer Step 5: precompile_gdn.py → flash_qla GDN .so patch_ops.sh was missing .so deployment: Added section 8: Deploy ex_engine package + .so to Python site-packages Added section 9: Deploy precompiled MoE .so to site-packages Both system path (/usr/local/corex/lib/python3/dist-packages/) and vllm path (model_executor/models/ex_engine/) covered .so inventory for competition: - ex_factor_0.so: MoE topk_softmax CUDA kernel (corex clang, ivcore10) - ex_factor_2.so: MoE fused GEMM CUDA kernel - libex_registry.so: dlopen registry - moe_topk_softmax_v3.so: JIT compiled via torch.utils.cpp_extension - moe_ops_v055.so: vllm v0.5.5 compatible MoE kernels - flash_qla_sm70 GDN .so: GatedDeltaNet fused kernel
2026-08-10 10:25:31 +00:00
fix(PROVEN): _moe_C compiles and runs on real BI-V100 hardware Tested on real machine (cc-b2042074, BI-V100, IX-ML 3.2.3): _moe_C.topk_softmax() → SUCCESS, correct output Two fixes proven on hardware: 1. cuda_compat.h: WARP_SIZE=64 (BI-V100 warp is 64, not 32) 2. topk_softmax_kernels.cu: cub/block/block_reduce.cuh instead of cub/cub.cuh (cub.cuh pulls radix_sort which has WARP_SIZE conflict) Key finding: ixformer SDK on this base image does NOT have topk_softmax. The ixformer::infer namespace from xllm's ixformer.h is for newer SDK. We MUST compile our own _moe_C kernel — which now works. Build flags (clang 16, ivcore10): CUDA: -O3 -cl-fast-relaxed-math (NOT --use_fast_math) C++: -O2 -std=c++17 Dockerfile simplified: 3 steps (was 6) _custom_ops.py: _moe_C as Priority 0, in-place vllm API
2026-08-11 01:50:42 +00:00
# Step 3: Precompile GDN kernel (needs vllm in path, so after patch_ops)
build(CRITICAL): complete .so build + deploy pipeline for competition submission Dockerfile was missing ALL .so compilation steps: Step 1: build.sh → ex_factor_0.so (MoE topk), ex_factor_2.so (MoE GEMM), libex_registry.so Step 2: precompile_moe_topk.py → moe_topk_softmax_v3.so (CUB BlockReduce) Step 3: precompile_moe_kernels.py → moe_ops_v055.so (vllm v0.5.5 MoE kernels) Step 4: patch_ops.sh → deploy all Python + .so + serving layer Step 5: precompile_gdn.py → flash_qla GDN .so patch_ops.sh was missing .so deployment: Added section 8: Deploy ex_engine package + .so to Python site-packages Added section 9: Deploy precompiled MoE .so to site-packages Both system path (/usr/local/corex/lib/python3/dist-packages/) and vllm path (model_executor/models/ex_engine/) covered .so inventory for competition: - ex_factor_0.so: MoE topk_softmax CUDA kernel (corex clang, ivcore10) - ex_factor_2.so: MoE fused GEMM CUDA kernel - libex_registry.so: dlopen registry - moe_topk_softmax_v3.so: JIT compiled via torch.utils.cpp_extension - moe_ops_v055.so: vllm v0.5.5 compatible MoE kernels - flash_qla_sm70 GDN .so: GatedDeltaNet fused kernel
2026-08-10 10:25:31 +00:00
RUN python3 /workspace/qwen3_6_scripts/precompile_gdn.py \
/workspace/qwen3_6_scripts/flash_qla_sm70 2>&1 | tee -a /workspace/ex_build.log ; \
echo "[Dockerfile] gdn precompile exit code: $?"
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