eef9939c21
Use full length commit sha instead of short prefixes, to improve reproducibility (future clashes) and guard against compromised repos and man in the middle attacks. Abbreviated commit shas are expanded to full length, to guard against future clashes on short hash. It also guards against compromised repos and man in the middle attacks, where attackers can easily fabricate a malicious commit with a shasum prefix collision. Versions with just tags now also get a commit sha, which can later be used to check for retagged commits.
171 lines
6.5 KiB
Python
171 lines
6.5 KiB
Python
# Copyright 2013-2023 Lawrence Livermore National Security, LLC and other
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# Spack Project Developers. See the top-level COPYRIGHT file for details.
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#
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# SPDX-License-Identifier: (Apache-2.0 OR MIT)
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from spack.package import *
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class PyDgl(CMakePackage, PythonExtension, CudaPackage):
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"""Deep Graph Library (DGL).
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DGL is an easy-to-use, high performance and scalable Python package for
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deep learning on graphs. DGL is framework agnostic, meaning if a deep graph
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model is a component of an end-to-end application, the rest of the logics
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can be implemented in any major frameworks, such as PyTorch, Apache MXNet
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or TensorFlow."""
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homepage = "https://www.dgl.ai/"
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git = "https://github.com/dmlc/dgl.git"
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maintainers("adamjstewart", "meyersbs")
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version("master", branch="master", submodules=True)
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version(
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"1.0.1", tag="1.0.1", commit="cc2e9933f309f585fae90965ab61ad11ac1eecd5", submodules=True
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)
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version(
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"0.4.3", tag="0.4.3", commit="e1d90f9b5eeee7359a6b4f5edca7473a497984ba", submodules=True
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)
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version(
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"0.4.2", tag="0.4.2", commit="55e056fbae8f25f3da4aab0a0d864d72c2a445ff", submodules=True
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)
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variant("cuda", default=True, description="Build with CUDA")
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variant("openmp", default=True, description="Build with OpenMP")
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variant(
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"backend",
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default="pytorch",
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description="Default backend",
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values=["pytorch", "mxnet", "tensorflow"],
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multi=False,
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)
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depends_on("cmake@3.5:", type="build")
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depends_on("llvm-openmp", when="%apple-clang +openmp")
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# Python dependencies
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# See python/setup.py
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extends("python")
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depends_on("python@3.5:", type=("build", "run"))
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depends_on("py-pip", type="build")
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depends_on("py-wheel", type="build")
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depends_on("py-setuptools", type="build")
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depends_on("py-cython", type="build")
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depends_on("py-numpy@1.14.0:", type=("build", "run"))
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depends_on("py-scipy@1.1.0:", type=("build", "run"))
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depends_on("py-networkx@2.1:", type=("build", "run"))
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depends_on("py-requests@2.19.0:", when="@0.4.3:", type=("build", "run"))
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depends_on("py-tqdm", when="@1.0.1:", type=("build", "run"))
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depends_on("py-psutil@5.8.0:", when="@1.0.1:", type=("build", "run"))
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# Backends
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# See https://docs.dgl.ai/install/index.html#working-with-different-backends
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depends_on("py-torch@1.12.0:", when="@1.0.1: backend=pytorch", type="run")
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depends_on("py-torch@1.2.0:", when="@0.4.3: backend=pytorch", type="run")
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depends_on("py-torch@0.4.1:", when="backend=pytorch", type="run")
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depends_on("mxnet@1.6.0:", when="@1.0.1: backend=mxnet", type="run")
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depends_on("mxnet@1.5.1:", when="@0.4.3: backend=mxnet", type="run")
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depends_on("mxnet@1.5.0:", when="backend=mxnet", type="run")
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depends_on("py-tensorflow@2.3:", when="@1.0.1: backend=tensorflow", type="run")
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depends_on("py-tensorflow@2.1:", when="@0.4.3: backend=tensorflow", type="run")
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depends_on("py-tensorflow@2.0:", when="backend=tensorflow", type="run")
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# Cuda
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# See https://github.com/dmlc/dgl/issues/3083
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depends_on("cuda@:10", when="@:0.4 +cuda", type=("build", "run"))
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# From error: "Your installed Caffe2 version uses cuDNN but I cannot find the
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# cuDNN libraries. Please set the proper cuDNN prefixes and / or install cuDNN."
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depends_on("cudnn", when="+cuda", type=("build", "run"))
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patch(
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"https://patch-diff.githubusercontent.com/raw/dmlc/dgl/pull/5434.patch?full_index=1",
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sha256="8c5f14784637a9bb3dd55e6104715d4a35b4e6594c99884aa19e67bc0544e91a",
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when="@1.0.1",
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)
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build_directory = "build"
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# https://docs.dgl.ai/install/index.html#install-from-source
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def cmake_args(self):
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args = []
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if "+cuda" in self.spec:
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args.append("-DUSE_CUDA=ON")
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# Prevent defaulting to old compute_ and sm_ despite defining cuda_arch
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args.append("-DCUDA_ARCH_NAME=Manual")
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cuda_arch_list = " ".join(list(self.spec.variants["cuda_arch"].value))
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args.append("-DCUDA_ARCH_BIN={0}".format(cuda_arch_list))
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args.append("_DCUDA_ARCH_PTX={0}".format(cuda_arch_list))
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else:
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args.append("-DUSE_CUDA=OFF")
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if "+openmp" in self.spec:
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args.append("-DUSE_OPENMP=ON")
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if self.spec.satisfies("%apple-clang"):
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args.extend(
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[
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"-DOpenMP_CXX_FLAGS=" + self.spec["llvm-openmp"].headers.include_flags,
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"-DOpenMP_CXX_LIB_NAMES=" + self.spec["llvm-openmp"].libs.names[0],
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"-DOpenMP_C_FLAGS=" + self.spec["llvm-openmp"].headers.include_flags,
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"-DOpenMP_C_LIB_NAMES=" + self.spec["llvm-openmp"].libs.names[0],
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"-DOpenMP_omp_LIBRARY=" + self.spec["llvm-openmp"].libs[0],
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]
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)
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else:
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args.append("-DUSE_OPENMP=OFF")
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if self.run_tests:
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args.append("-DBUILD_CPP_TEST=ON")
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else:
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args.append("-DBUILD_CPP_TEST=OFF")
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return args
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def install(self, spec, prefix):
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with working_dir("python"):
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args = std_pip_args + ["--prefix=" + prefix, "."]
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pip(*args)
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# Older versions do not install correctly
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if self.spec.satisfies("@:0.4.3"):
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# Work around installation bug: https://github.com/dmlc/dgl/issues/1379
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install_tree(prefix.dgl, prefix.lib)
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def setup_run_environment(self, env):
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# https://docs.dgl.ai/install/backend.html
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backend = self.spec.variants["backend"].value
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env.set("DGLBACKEND", backend)
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@property
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def import_modules(self):
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modules = [
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"dgl",
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"dgl.nn",
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"dgl.runtime",
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"dgl.backend",
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"dgl.function",
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"dgl.contrib",
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"dgl._ffi",
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"dgl.data",
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"dgl.runtime.ir",
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"dgl.backend.numpy",
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"dgl.contrib.sampling",
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"dgl._ffi._cy2",
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"dgl._ffi._cy3",
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"dgl._ffi._ctypes",
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]
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if "backend=pytorch" in self.spec:
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modules.extend(["dgl.nn.pytorch", "dgl.nn.pytorch.conv", "dgl.backend.pytorch"])
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elif "backend=mxnet" in self.spec:
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modules.extend(["dgl.nn.mxnet", "dgl.nn.mxnet.conv", "dgl.backend.mxnet"])
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elif "backend=tensorflow" in self.spec:
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modules.extend(
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["dgl.nn.tensorflow", "dgl.nn.tensorflow.conv", "dgl.backend.tensorflow"]
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)
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return modules
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