biondizzle
9908fd64d9
Major changes from initial TileLang prototype: Kernel: - CUTLASS NVFP4 block-scaled GEMM (SM100 Blackwell, OpClassBlockScaledTensorOp) - Slot-based dispatch: L1 GEMM → SiLU+Mul per-slot → L2 GEMM → index_add scatter - 1D slot_expert_ids passed to both L1 and L2 (no 2D topk_ids rebuild) - slot_token gathered in cutlass_grouped_nvfp4_gemm when provided SF Remap (source-first): - Iterates logical (m, k_sf) source grid, uses layout_sf(make_coord(m, k_sf)) for CUTLASS dest index — no idx2crd/flatten coordinate extraction - 2D kernel launch: dim3 block(32,8), grid over (K_sf, MN) - Uses cute::cosize() for physical allocation size (not cute::size) - SFA: (MN, K_sf) row-major; SFB: (K_sf, MN) row-major (col-major) Weight transform: - UE4M3 unpack with bit reinterpret (not value cast) - Global scale folding (weight_scale_2) for gate/up split - clamp(0,448) → float8_e4m3fn, transpose (N,K)→(K,N) for CUTLASS No prepack cache: - SFB remapped per-call inside CUTLASS (~µs, not the bottleneck) - See README for why prepack cache must never return (OOM, CUDA graphs, M-dependent layout, cross-layer collisions) Stage activation: - Nearest-neighbor E2M1 quantization (no clamp, no uniform steps) - Per-tensor global scale → alpha for L2 GEMM Bug fixes: - _fold_global_scale: removed broken logical_widths branch - unpack_ue4m3_u32: int32 for CUDA bitwise, view not to, ND support - Correct expert param mapping for NVFP4 checkpoint - SiLU applied per-slot (not after summing expert paths)
51 lines
2.3 KiB
Docker
51 lines
2.3 KiB
Docker
# DeepSeek V4 NVFP4 vLLM + CUTLASS NVFP4 Mega MoE Kernel
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FROM vllm/vllm-openai:nightly-x86_64
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# Remove broken nixl_ep (built against CUDA 12, image is CUDA 13)
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RUN pip uninstall -y nixl-ep; rm -rf /usr/local/lib/python3.12/dist-packages/nixl_ep
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RUN apt-get update && apt-get install -y git screen cmake libcusolver-dev-13-0 libcusparse-dev-13-0 libcublas-dev-13-0 libcurand-dev-13-0 libcufft-dev-13-0 libnvjitlink-dev-13-0 && rm -rf /var/lib/apt/lists/*
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# Remove the broken symlink if it exists
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RUN rm -f /usr/local/cuda/lib64/libcudart.so.12
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ENV CUDA_HOME=/usr/local/cuda
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ENV TORCH_CUDA_ARCH_LIST="10.0"
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# Clone latest CUTLASS (has NVFP4 block-scaled MMA support)
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RUN git clone --depth 1 https://github.com/NVIDIA/cutlass.git /root/cutlass
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ARG CACHE_BUSTER=${TIMESTAMP}
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# Copy and install the NVFP4 mega_moe kernel (from this repo)
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COPY src/ /root/nvfp4-megamoe-kernel/src/
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COPY pyproject.toml /root/nvfp4-megamoe-kernel/pyproject.toml
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RUN cd /root/nvfp4-megamoe-kernel && pip install -e .
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# Build the CUTLASS NVFP4 block-scaled GEMM extension
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RUN cd /root/nvfp4-megamoe-kernel/src/nvfp4_megamoe_kernel/cutlass_nvfp4_gemm && \
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mkdir -p cutlass_nvfp4_gemm && \
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CUTLASS_INCLUDE_DIR=/root/cutlass/include \
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TORCH_CUDA_ARCH_LIST=10.0 \
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python3 setup.py build_ext --inplace
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# Install TileLang (for potential future use)
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RUN pip install tilelang
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ENV PYTHONPATH="/root/nvfp4-megamoe-kernel/src/nvfp4_megamoe_kernel/cutlass_nvfp4_gemm:/root/nvfp4-megamoe-kernel:${PYTHONPATH}"
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# Patch vLLM — overwrite model files and register architecture
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ARG VLLM_MODELS_DIR=/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models
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ARG VLLM_LAYERS_DIR=/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers
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COPY vllm/patches/deepseek_v4.py ${VLLM_MODELS_DIR}/deepseek_v4.py
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COPY vllm/patches/deepseek_v4_attention.py ${VLLM_LAYERS_DIR}/deepseek_v4_attention.py
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RUN sed -i 's/"DeepseekV32ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),/"DeepseekV32ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),\n "DeepseekV4ForCausalLM": ("deepseek_v4", "DeepseekV4ForCausalLM"),/' \
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${VLLM_MODELS_DIR}/registry.py
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# Verify
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RUN python3 -c "import torch; import cutlass_nvfp4_gemm._C; print('CUTLASS NVFP4 OK')" && \
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python3 -c "import vllm; print('vLLM OK')" && \
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python3 -c "import nvfp4_megamoe_kernel; print('NVFP4 kernel OK')"
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