cuda nn
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@ -55,7 +55,16 @@ ARG TARGETPLATFORM
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ENV PATH /opt/conda/bin:$PATH
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RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
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RUN apt-get update
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# Add new repo to install gcc 11 on Ubuntu 20.04
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RUN echo "deb http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu focal main" >> /etc/apt/sources.list && \
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apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 60C317803A41BA51845E371A1E9377A2BA9EF27F && \
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apt-get update
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RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
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gcc-11 \
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g++-11 \
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build-essential \
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ca-certificates \
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ccache \
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@ -70,6 +79,9 @@ RUN /usr/sbin/update-ccache-symlinks
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RUN mkdir /opt/ccache && ccache --set-config=cache_dir=/opt/ccache
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ENV PATH /opt/conda/bin:$PATH
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# Set gcc path to new gcc version
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RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 60 --slave /usr/bin/g++ g++ /usr/bin/g++-11
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# Install conda
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# translating Docker's TARGETPLATFORM into mamba arches
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RUN case ${TARGETPLATFORM} in \
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@ -88,30 +100,89 @@ RUN git clone --recursive https://github.com/pytorch/pytorch && \
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WORKDIR /pytorch
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# Write the Pytorch version into the version.txt file because it isn't always the same as the tag
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# Write the Pytorch version into the version.txt file because it isn't always the same as the tag we checked out
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RUN echo $PYTORCH_VERSION > version.txt
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RUN /opt/conda/bin/conda install -y python=${PYTHON_VERSION} cmake ninja conda-build pyyaml numpy ipython mkl mkl-include && \
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/opt/conda/bin/conda install -c pytorch magma-cuda118 && \
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RUN /opt/conda/bin/conda install -y python=${PYTHON_VERSION} cmake ninja conda-build pyyaml numpy ipython mkl mkl-include cudnn && \
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/opt/conda/bin/conda install -c pytorch magma-cuda118 && \
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/opt/conda/bin/python -mpip install -r requirements.txt && \
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/opt/conda/bin/conda clean -ya
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# https://github.com/cresset-template/cresset/blob/37c7b5df7236d3b9d96c4908efe5af8bc90066e3/reqs/train-conda-build.requirements.txt
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RUN /opt/conda/bin/conda install -y \
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jemalloc \
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astunparse \
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ccache \
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cmake \
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expecttest \
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filelock \
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fsspec \
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git \
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hypothesis \
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jinja2 \
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libjpeg-turbo \
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libpng \
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networkx \
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ninja \
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numpy \
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psutil \
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pyyaml \
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requests \
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setuptools \
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sympy \
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types-dataclasses \
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typing-extensions
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# Use Intel OpenMP with optimizations. See the documentation for details.
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# https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html
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# Intel OpenMP thread blocking time in ms.
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ENV KMP_BLOCKTIME=0
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# Configure CPU thread affinity.
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# ENV KMP_AFFINITY="granularity=fine,compact,1,0"
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ENV LD_PRELOAD=/opt/conda/lib/libiomp5.so:${LD_PRELOAD}
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# Use Jemalloc for efficient memory management.
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ENV LD_PRELOAD=/opt/conda/lib/libjemalloc.so:${LD_PRELOAD}
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ENV MALLOC_CONF="background_thread:true,metadata_thp:auto,dirty_decay_ms:30000,muzzy_decay_ms:30000"
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# Install PyTorch without AVX2
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RUN python setup.py clean && \
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USE_CUDA=1 \
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TORCH_CUDA_ARCH_LIST="3.5 5.2 6.0 6.1 7.0+PTX 8.0" TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \
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# https://github.com/cresset-template/cresset/blob/37c7b5df7236d3b9d96c4908efe5af8bc90066e3/docker-compose.yaml#L124
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# print(torch.__config__.show().split("\n"), sep="\n")
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RUN --mount=type=cache,target=/opt/ccache \
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python setup.py clean && \
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BLAS_INFO=mklBUILD_TYPE=Release \
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CXX_FLAGS="-D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow" \
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LAPACK_INFO=mkl \
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PERF_WITH_AVX=1 \
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PERF_WITH_AVX2=0 \
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PERF_WITH_AVX512=0 \
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TORCH_DISABLE_GPU_ASSERTS=ON \
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TORCH_VERSION=${PYTORCH_VERSION} \
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USE_CUDA=ON \
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USE_CUDNN=ON \
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USE_EXCEPTION_PTR=1 \
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USE_GFLAGS=OFF \
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USE_GLOG=OFF \
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USE_MKL=ON \
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USE_MKLDNN=ON \
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USE_MPI=OFF \
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USE_NCCL=1 \
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USE_NNPACK=ON \
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USE_OPENMP=ON \
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USE_ROCM=OFF \
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BUILD_TEST=0 \
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TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \
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CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
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CMAKE_ARGS='-DDISABLE_AVX2:BOOL=TRUE -DCXX_AVX2_FOUND:BOOL=FALSE -DC_AVX2_FOUND:BOOL=FALSE -DDISABLE_AVX512F:BOOL=TRUE' \
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python setup.py install && \
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cd .. && \
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rm -rf pytorch
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# BUILD_TEST=0 \
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# Make sure we built everything properly. Build will fail if CUDA isn't available.
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RUN python -c "import torch; exit(1 if not torch.version.cuda else 0)"
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# RUN pip freeze | grep "torch"
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RUN nm -D /opt/conda/lib/python3.9/site-packages/torch/lib/libtorch.so
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# ==============================================================================
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@ -165,7 +236,7 @@ WORKDIR /usr/src
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COPY server/custom_kernels/ .
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# Build specific version of transformers
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RUN BUILD_EXTENSIONS=True MAX_JOBS=5 python setup.py build
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RUN MAX_JOBS=5 python setup.py build
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# ==============================================================================
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@ -190,7 +261,8 @@ ENV PATH=/opt/conda/bin:$PATH \
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# Text Generation Inference base env
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ENV HUGGINGFACE_HUB_CACHE=/data \
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HF_HUB_ENABLE_HF_TRANSFER=1 \
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PORT=80
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PORT=80 \
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LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.8/targets/x86_64-linux/lib:/opt/conda/lib/python3.9/site-packages/torch/lib
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WORKDIR /usr/src
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@ -220,8 +292,6 @@ COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39 /
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# Copy builds artifacts from vllm builder
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COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
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RUN python -c "import torch; exit(1 if not torch.version.cuda else 0)"
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# Install flash-attention dependencies
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RUN pip install einops --no-cache-dir
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@ -232,15 +302,41 @@ COPY server/Makefile server/Makefile
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RUN cd server && \
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make gen-server && \
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sed -i '/torch/d' requirements.txt && \
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pip install -r requirements.txt
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RUN pip freeze | grep torch
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RUN cd server && \
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pip install ".[bnb, accelerate, quantize]" --no-cache-dir && \
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pip install optimum auto-gptq
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# Fix the error
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# /opt/conda/lib/python3.9/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
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RUN cp /opt/conda/lib/python3.9/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /opt/conda/lib/python3.9/site-packages/bitsandbytes/libbitsandbytes_cpu.so
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RUN ldd /opt/conda/lib/python3.9/site-packages/exllama_kernels.cpython-39-x86_64-linux-gnu.so
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RUN nm -D /opt/conda/lib/python3.9/site-packages/torch/lib/libtorch.so
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RUN python3 -c "import torch; import text_generation_server.utils.gptq.exllama"
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# RUN ls /opt/conda/lib/python3.9/site-packages/bitsandbytes/
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# RUN find / -name libcudart.so 2>/dev/null
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# RUN find / -name "*libc10.so*"
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# RUN find / -name libtorch.so
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# Make sure our special dependencies were compiled and copied correctly
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RUN python -c "import torch; exit(1 if not torch.version.cuda else 0)"
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RUN python -c "import torch; torch.cuda.is_available()"
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RUN python -c "import torch; import flash_attn_2_cuda"
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RUN python -c "import torch; import flash_attn_cuda"
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RUN python -c "import torch; import vllm_cache_ops"
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RUN python -c "import torch; import vllm_attention_ops"
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RUN python -c "import torch; import custom_kernels"
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RUN python -c "import torch; import text_generation_server.utils.gptq.exllama"
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# Install benchmarker
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COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark
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# Install router
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