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{
lib,
stdenv,
buildPythonPackage,
fetchFromGitHub,
# build-system
flit-core,
# dependencies
aiohttp,
fsspec,
jinja2,
numpy,
psutil,
pyparsing,
requests,
torch,
tqdm,
xxhash,
# optional-dependencies
# benchmark
matplotlib,
networkx,
pandas,
protobuf,
wandb,
# dev
ipython,
matplotlib-inline,
pre-commit,
torch-geometric,
# full
ase,
graphviz,
h5py,
numba,
opt-einsum,
pynndescent,
rdflib,
rdkit,
scikit-image,
scikit-learn,
scipy,
statsmodels,
sympy,
tabulate,
torchmetrics,
trimesh,
# graphgym
pytorch-lightning,
yacs,
# modelhub
huggingface-hub,
# rag
# pcst-fast,
datasets,
transformers,
sentencepiece,
accelerate,
# test
onnx,
onnxruntime,
pytest,
pytest-cov-stub,
# tests
pytestCheckHook,
writableTmpDirAsHomeHook,
pythonAtLeast,
}:
buildPythonPackage (finalAttrs: {
pname = "torch-geometric";
version = "2.7.0";
pyproject = true;
src = fetchFromGitHub {
owner = "pyg-team";
repo = "pytorch_geometric";
tag = finalAttrs.version;
hash = "sha256-xlOzpoYRoEfIRWSQoZbEPvUW43AMr3rCgIYnxwG/z3A=";
};
build-system = [
flit-core
];
dependencies = [
aiohttp
fsspec
jinja2
numpy
psutil
pyparsing
requests
torch
tqdm
xxhash
];
optional-dependencies = {
benchmark = [
matplotlib
networkx
pandas
protobuf
wandb
];
dev = [
ipython
matplotlib-inline
pre-commit
torch-geometric
];
full = [
ase
# captum
graphviz
h5py
matplotlib
networkx
numba
opt-einsum
pandas
# pgmpy
pynndescent
# pytorch-memlab
rdflib
rdkit
scikit-image
scikit-learn
scipy
statsmodels
sympy
tabulate
torch-geometric
torchmetrics
trimesh
];
graphgym = [
protobuf
pytorch-lightning
yacs
];
modelhub = [
huggingface-hub
];
rag = [
# pcst-fast (unpackaged)
datasets
transformers
pandas
sentencepiece
accelerate
torchmetrics
];
test = [
onnx
onnxruntime
# onnxscript (unpackaged)
pytest
pytest-cov-stub
];
};
pythonImportsCheck = [ "torch_geometric" ];
nativeCheckInputs = [
pytestCheckHook
writableTmpDirAsHomeHook
];
pytestFlags = [
# DeprecationWarning: Failing to pass a value to the 'type_params' parameter of
# 'typing._eval_type' is deprecated, as it leads to incorrect behaviour when calling
# typing._eval_type on a stringified annotation that references a PEP 695 type parameter.
# It will be disallowed in Python 3.15.
"-Wignore::DeprecationWarning"
];
disabledTests = [
# RuntimeError: addmm: computation on CPU is not implemented for SparseCsr + SparseCsr @ SparseCsr without MKL.
# PyTorch built with MKL has better support for addmm with sparse CPU tensors.
"test_asap"
"test_graph_unet"
# AttributeError: type object 'Any' has no attribute '_name'
"test_type_repr"
# AttributeError: module 'torch.fx._symbolic_trace' has no attribute 'List'
"test_set_clear_mask"
"test_sequential_to_hetero"
"test_to_fixed_size"
"test_to_hetero_basic"
"test_to_hetero_with_gcn"
"test_to_hetero_with_basic_model"
"test_to_hetero_and_rgcn_equal_output"
"test_graph_level_to_hetero"
"test_hetero_transformer_self_loop_error"
"test_to_hetero_validate"
"test_to_hetero_on_static_graphs"
"test_to_hetero_with_bases"
"test_to_hetero_with_bases_and_rgcn_equal_output"
"test_to_hetero_with_bases_validate"
"test_to_hetero_with_bases_on_static_graphs"
"test_to_hetero_with_bases_save"
# Failed: DID NOT WARN.
"test_to_hetero_validate"
"test_to_hetero_with_bases_validate"
# Failed: DID NOT RAISE
"test_scatter_backward"
]
++ lib.optionals stdenv.hostPlatform.isDarwin [
# This test uses `torch.jit` which might not be working on darwin:
# RuntimeError: required keyword attribute 'value' has the wrong type
"test_traceable_my_conv_with_self_loops"
# RuntimeError: no response from torch_shm_manager
"test_data_loader"
"test_data_share_memory"
"test_dataloader"
"test_edge_index_dataloader"
"test_heterogeneous_dataloader"
"test_index_dataloader"
"test_multiprocessing"
"test_share_memory"
"test_storage_tensor_methods"
# NotImplementedError: The operator 'aten::logspace.out' is not currently implemented for the MPS device.
"test_positional_encoding"
]
++ lib.optionals (pythonAtLeast "3.13") [
# RuntimeError: Dynamo is not supported on Python 3.13+
"test_compile"
# RuntimeError: Python 3.13+ not yet supported for torch.compile
"test_compile_graph_breaks"
"test_compile_multi_aggr_sage_conv"
"test_compile_hetero_conv_graph_breaks"
# AttributeError: module 'typing' has no attribute 'io'. Did you mean: 'IO'?
"test_packaging"
# RuntimeError: Boolean value of Tensor with more than one value is ambiguous
"test_feature_store"
]
++ lib.optionals (pythonAtLeast "3.14") [
# TypeError: cannot pickle 'sqlite3.Connection' object
"test_dataloader_on_disk_dataset"
# AssertionError: assert False
# assert utils.supports_bipartite_graphs('SAGEConv')
"test_gnn_cheatsheet"
# AttributeError: readonly attribute
"test_fill_config_store"
"test_register"
"test_to_dataclass"
# AttributeError: '...' object has no attribute '__annotations__'
"test_degree_scaler_aggregation"
"test_explain_message"
"test_fused_aggregation"
"test_gcn_conv_with_decomposed_layers"
"test_hetero_dict_linear_jit"
"test_hetero_linear_basic"
"test_jit"
"test_mlp"
"test_multi_agg"
"test_my_commented_conv"
"test_my_conv_jit"
"test_my_conv_jit_save"
"test_my_default_arg_conv"
"test_my_edge_conv_jit"
"test_my_kwargs_conv"
"test_my_multiple_aggr_conv_jit"
"test_pickle"
"test_sequential_jit"
"test_torch_script"
"test_traceable_my_conv_with_self_loops"
"test_tuple_output_jit"
];
disabledTestPaths =
lib.optionals stdenv.hostPlatform.isDarwin [
# MPS (Metal) tests are failing when using `libtorch_cpu`.
# Crashes in `structured_cat_out_mps`
"test/nn/models/test_deep_graph_infomax.py::test_infomax_predefined_model[mps]"
"test/nn/norm/test_instance_norm.py::test_instance_norm[True-mps]"
"test/nn/norm/test_instance_norm.py::test_instance_norm[False-mps]"
"test/nn/norm/test_layer_norm.py::test_layer_norm[graph-True-mps]"
"test/nn/norm/test_layer_norm.py::test_layer_norm[graph-False-mps]"
"test/nn/norm/test_layer_norm.py::test_layer_norm[node-True-mps]"
"test/nn/norm/test_layer_norm.py::test_layer_norm[node-False-mps]"
"test/utils/test_scatter.py::test_group_cat[mps]"
]
++ lib.optionals (pythonAtLeast "3.14") [
# AttributeError: '...' object has no attribute '__annotations__'
"test/nn/aggr/test_aggr_utils.py"
];
meta = {
description = "Graph Neural Network Library for PyTorch";
homepage = "https://github.com/pyg-team/pytorch_geometric";
changelog = "https://github.com/pyg-team/pytorch_geometric/blob/${finalAttrs.src.tag}/CHANGELOG.md";
license = lib.licenses.mit;
maintainers = with lib.maintainers; [ GaetanLepage ];
};
})
|
