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from itertools import product
import pytest
import torch
from torch_sparse.storage import SparseStorage
from torch_sparse.testing import devices, dtypes, tensor
@pytest.mark.parametrize('device', devices)
def test_ind2ptr(device):
row = tensor([2, 2, 4, 5, 5, 6], torch.long, device)
rowptr = torch.ops.torch_sparse.ind2ptr(row, 8)
assert rowptr.tolist() == [0, 0, 0, 2, 2, 3, 5, 6, 6]
row = torch.ops.torch_sparse.ptr2ind(rowptr, 6)
assert row.tolist() == [2, 2, 4, 5, 5, 6]
row = tensor([], torch.long, device)
rowptr = torch.ops.torch_sparse.ind2ptr(row, 8)
assert rowptr.tolist() == [0, 0, 0, 0, 0, 0, 0, 0, 0]
row = torch.ops.torch_sparse.ptr2ind(rowptr, 0)
assert row.tolist() == []
@pytest.mark.parametrize('dtype,device', product(dtypes, devices))
def test_storage(dtype, device):
row, col = tensor([[0, 0, 1, 1], [0, 1, 0, 1]], torch.long, device)
storage = SparseStorage(row=row, col=col)
assert storage.row().tolist() == [0, 0, 1, 1]
assert storage.col().tolist() == [0, 1, 0, 1]
assert storage.value() is None
assert storage.sparse_sizes() == (2, 2)
row, col = tensor([[0, 0, 1, 1], [1, 0, 1, 0]], torch.long, device)
value = tensor([2, 1, 4, 3], dtype, device)
storage = SparseStorage(row=row, col=col, value=value)
assert storage.row().tolist() == [0, 0, 1, 1]
assert storage.col().tolist() == [0, 1, 0, 1]
assert storage.value().tolist() == [1, 2, 3, 4]
assert storage.sparse_sizes() == (2, 2)
@pytest.mark.parametrize('dtype,device', product(dtypes, devices))
def test_caching(dtype, device):
row, col = tensor([[0, 0, 1, 1], [0, 1, 0, 1]], torch.long, device)
storage = SparseStorage(row=row, col=col)
assert storage._row.tolist() == row.tolist()
assert storage._col.tolist() == col.tolist()
assert storage._value is None
assert storage._rowcount is None
assert storage._rowptr is None
assert storage._colcount is None
assert storage._colptr is None
assert storage._csr2csc is None
assert storage.num_cached_keys() == 0
storage.fill_cache_()
assert storage._rowcount.tolist() == [2, 2]
assert storage._rowptr.tolist() == [0, 2, 4]
assert storage._colcount.tolist() == [2, 2]
assert storage._colptr.tolist() == [0, 2, 4]
assert storage._csr2csc.tolist() == [0, 2, 1, 3]
assert storage._csc2csr.tolist() == [0, 2, 1, 3]
assert storage.num_cached_keys() == 5
storage = SparseStorage(row=row, rowptr=storage._rowptr, col=col,
value=storage._value,
sparse_sizes=storage._sparse_sizes,
rowcount=storage._rowcount, colptr=storage._colptr,
colcount=storage._colcount,
csr2csc=storage._csr2csc, csc2csr=storage._csc2csr)
assert storage._rowcount.tolist() == [2, 2]
assert storage._rowptr.tolist() == [0, 2, 4]
assert storage._colcount.tolist() == [2, 2]
assert storage._colptr.tolist() == [0, 2, 4]
assert storage._csr2csc.tolist() == [0, 2, 1, 3]
assert storage._csc2csr.tolist() == [0, 2, 1, 3]
assert storage.num_cached_keys() == 5
storage.clear_cache_()
assert storage._rowcount is None
assert storage._rowptr is not None
assert storage._colcount is None
assert storage._colptr is None
assert storage._csr2csc is None
assert storage.num_cached_keys() == 0
@pytest.mark.parametrize('dtype,device', product(dtypes, devices))
def test_utility(dtype, device):
row, col = tensor([[0, 0, 1, 1], [1, 0, 1, 0]], torch.long, device)
value = tensor([1, 2, 3, 4], dtype, device)
storage = SparseStorage(row=row, col=col, value=value)
assert storage.has_value()
storage.set_value_(value, layout='csc')
assert storage.value().tolist() == [1, 3, 2, 4]
storage.set_value_(value, layout='coo')
assert storage.value().tolist() == [1, 2, 3, 4]
storage = storage.set_value(value, layout='csc')
assert storage.value().tolist() == [1, 3, 2, 4]
storage = storage.set_value(value, layout='coo')
assert storage.value().tolist() == [1, 2, 3, 4]
storage = storage.sparse_resize((3, 3))
assert storage.sparse_sizes() == (3, 3)
new_storage = storage.copy()
assert new_storage != storage
assert new_storage.col().data_ptr() == storage.col().data_ptr()
new_storage = storage.clone()
assert new_storage != storage
assert new_storage.col().data_ptr() != storage.col().data_ptr()
@pytest.mark.parametrize('dtype,device', product(dtypes, devices))
def test_coalesce(dtype, device):
row, col = tensor([[0, 0, 0, 1, 1], [0, 1, 1, 0, 1]], torch.long, device)
value = tensor([1, 1, 1, 3, 4], dtype, device)
storage = SparseStorage(row=row, col=col, value=value)
assert storage.row().tolist() == row.tolist()
assert storage.col().tolist() == col.tolist()
assert storage.value().tolist() == value.tolist()
assert not storage.is_coalesced()
storage = storage.coalesce()
assert storage.is_coalesced()
assert storage.row().tolist() == [0, 0, 1, 1]
assert storage.col().tolist() == [0, 1, 0, 1]
assert storage.value().tolist() == [1, 2, 3, 4]
@pytest.mark.parametrize('dtype,device', product(dtypes, devices))
def test_sparse_reshape(dtype, device):
row, col = tensor([[0, 1, 2, 3], [0, 1, 2, 3]], torch.long, device)
storage = SparseStorage(row=row, col=col)
storage = storage.sparse_reshape(2, 8)
assert storage.sparse_sizes() == (2, 8)
assert storage.row().tolist() == [0, 0, 1, 1]
assert storage.col().tolist() == [0, 5, 2, 7]
storage = storage.sparse_reshape(-1, 4)
assert storage.sparse_sizes() == (4, 4)
assert storage.row().tolist() == [0, 1, 2, 3]
assert storage.col().tolist() == [0, 1, 2, 3]
storage = storage.sparse_reshape(2, -1)
assert storage.sparse_sizes() == (2, 8)
assert storage.row().tolist() == [0, 0, 1, 1]
assert storage.col().tolist() == [0, 5, 2, 7]
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