import subprocess from typing import List, Set from packaging.version import parse, Version import setuptools import torch from torch.utils.cpp_extension import BuildExtension, CUDAExtension from torch.utils.cpp_extension import CUDA_HOME # Compiler flags. CXX_FLAGS = ["-g", "-O2"] # TODO(woosuk): Should we use -O3? NVCC_FLAGS = ["-O2"] if not torch.cuda.is_available(): raise RuntimeError( f"Cannot find CUDA at CUDA_HOME: {CUDA_HOME}. " "CUDA must be available in order to build the package.") def get_nvcc_cuda_version(cuda_dir: str) -> Version: """Get the CUDA version from nvcc. Adapted from https://github.com/NVIDIA/apex/blob/8b7a1ff183741dd8f9b87e7bafd04cfde99cea28/setup.py """ nvcc_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True) output = nvcc_output.split() release_idx = output.index("release") + 1 nvcc_cuda_version = parse(output[release_idx].split(",")[0]) return nvcc_cuda_version # Collect the compute capabilities of all available GPUs. device_count = torch.cuda.device_count() compute_capabilities: Set[int] = set() for i in range(device_count): major, minor = torch.cuda.get_device_capability(i) if major < 7: raise RuntimeError( "GPUs with compute capability less than 7.0 are not supported.") compute_capabilities.add(major * 10 + minor) # If no GPU is available, add all supported compute capabilities. if not compute_capabilities: compute_capabilities = {70, 75, 80, 86, 90} # Add target compute capabilities to NVCC flags. for capability in compute_capabilities: NVCC_FLAGS += ["-gencode", f"arch=compute_{capability},code=sm_{capability}"] # Validate the NVCC CUDA version. nvcc_cuda_version = get_nvcc_cuda_version(CUDA_HOME) if nvcc_cuda_version < Version("11.0"): raise RuntimeError("CUDA 11.0 or higher is required to build the package.") if 86 in compute_capabilities and nvcc_cuda_version < Version("11.1"): raise RuntimeError( "CUDA 11.1 or higher is required for GPUs with compute capability 8.6.") if 90 in compute_capabilities and nvcc_cuda_version < Version("11.8"): raise RuntimeError( "CUDA 11.8 or higher is required for GPUs with compute capability 9.0.") ext_modules = [] # Cache operations. cache_extension = CUDAExtension( name="cacheflow.cache_ops", sources=["csrc/cache.cpp", "csrc/cache_kernels.cu"], extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS}, ) ext_modules.append(cache_extension) # Attention kernels. attention_extension = CUDAExtension( name="cacheflow.attention_ops", sources=["csrc/attention.cpp", "csrc/attention/attention_kernels.cu"], extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS}, ) ext_modules.append(attention_extension) # Positional encoding kernels. positional_encoding_extension = CUDAExtension( name="cacheflow.pos_encoding_ops", sources=["csrc/pos_encoding.cpp", "csrc/pos_encoding_kernels.cu"], extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS}, ) ext_modules.append(positional_encoding_extension) # Layer normalization kernels. layernorm_extension = CUDAExtension( name="cacheflow.layernorm_ops", sources=["csrc/layernorm.cpp", "csrc/layernorm_kernels.cu"], extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS}, ) ext_modules.append(layernorm_extension) # Activation kernels. activation_extension = CUDAExtension( name="cacheflow.activation_ops", sources=["csrc/activation.cpp", "csrc/activation_kernels.cu"], extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS}, ) ext_modules.append(activation_extension) def get_requirements() -> List[str]: """Get Python package dependencies from requirements.txt.""" with open("requirements.txt") as f: requirements = f.read().strip().split("\n") return requirements setuptools.setup( name="cacheflow", python_requires=">=3.8", install_requires=get_requirements(), ext_modules=ext_modules, cmdclass={"build_ext": BuildExtension}, )