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android / platform / external / pytorch / 7767dcfc8d / . / test / test_sparse.py
blob: 5af630c0acb4b3199ce26ffe84966e17a81a90fa [file] [log] [blame]
import torch
# TODO: remove this global setting
# Sparse tests use double as the default dtype
torch.set_default_dtype(torch.double)
import itertools
import functools
import operator
import random
import unittest
from torch.testing._internal.common_utils import TestCase, run_tests, skipIfRocm, do_test_dtypes, \
do_test_empty_full, load_tests, TEST_NUMPY, IS_WINDOWS
from torch.testing._internal.common_cuda import TEST_CUDA, _get_torch_cuda_version
from numbers import Number
from torch.autograd.gradcheck import gradcheck
from typing import Dict, Any
# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
def cpu_only(inner):
@functools.wraps(inner)
def outer(self, *args, **kwargs):
if self.is_cuda:
raise unittest.SkipTest("Test is CPU-only")
inner(self, *args, **kwargs)
return outer
def cuda_only(inner):
@functools.wraps(inner)
def outer(self, *args, **kwargs):
if not self.is_cuda:
raise unittest.SkipTest("Test is GPU-only")
inner(self, *args, **kwargs)
return outer
class TestSparse(TestCase):
def setUp(self):
# These parameters control the various ways we can run the test.
# We will subclass and override this method to implement CUDA
# tests
self.is_cuda = False
self.is_uncoalesced = False
self.device = 'cpu'
self.exact_dtype = True
self.value_dtype = torch.float64
self.index_tensor = lambda *args: torch.tensor(*args, dtype=torch.int64, device=self.device)
self.value_empty = lambda *args: torch.empty(*args, dtype=self.value_dtype, device=self.device)
self.value_tensor = lambda *args: torch.tensor(*args, dtype=self.value_dtype, device=self.device)
def sparse_empty_factory(*args, **kwargs):
kwargs['dtype'] = kwargs.get('dtype', self.value_dtype)
kwargs['layout'] = kwargs.get('layout', torch.sparse_coo)
kwargs['device'] = kwargs.get('device', self.device)
return torch.empty(*args, **kwargs)
self.sparse_empty = sparse_empty_factory
def sparse_tensor_factory(*args, **kwargs):
kwargs['dtype'] = kwargs.get('dtype', self.value_dtype)
kwargs['device'] = kwargs.get('device', self.device)
return torch.sparse_coo_tensor(*args, **kwargs)
self.sparse_tensor = sparse_tensor_factory
self.legacy_sparse_tensor = torch.sparse.DoubleTensor
super(TestSparse, self).setUp()
def _gen_sparse(self, sparse_dim, nnz, with_size):
if isinstance(with_size, Number):
with_size = [with_size] * sparse_dim
x, i, v = self.genSparseTensor(with_size, sparse_dim, nnz, self.is_uncoalesced, self.device)
if self.is_uncoalesced:
self.assert_uncoalesced(x)
return x, i, v
def assert_uncoalesced(self, x):
"""
Test if a CPU tensor is uncoalesced. This is used to ensure
correctness of the uncoalesced tensor generation algorithm.
"""
assert not x.is_coalesced()
existing_indices = set()
for i in range(x._nnz()):
index = str(x._indices()[:, i])
if index in existing_indices:
return True
else:
existing_indices.add(index)
def randn(self, *args, **kwargs):
"""
Variant of torch.randn that also works in the TEST_CUDA case.
"""
# TODO: Put this in torch.cuda.randn
return self.value_empty(*args, **kwargs).normal_()
def test_print(self):
shape_sparse_dim_nnz = [
((), 0, 2),
((0,), 0, 10),
((2,), 0, 3),
((100, 3), 1, 3),
((100, 20, 3), 2, 0),
((10, 0, 3), 0, 3),
((10, 0, 3), 0, 0),
]
printed = []
for shape, sparse_dim, nnz in shape_sparse_dim_nnz:
indices_shape = torch.Size((sparse_dim, nnz))
values_shape = torch.Size((nnz,) + shape[sparse_dim:])
printed.append("# shape: {}".format(torch.Size(shape)))
printed.append("# nnz: {}".format(nnz))
printed.append("# sparse_dim: {}".format(sparse_dim))
printed.append("# indices shape: {}".format(indices_shape))
printed.append("# values shape: {}".format(values_shape))
indices = torch.arange(indices_shape.numel(), dtype=self.index_tensor(0).dtype,
device=self.device).view(indices_shape)
for d in range(sparse_dim):
indices[d].clamp_(max=(shape[d] - 1)) # make it valid index
if self.is_uncoalesced and indices.numel() > 0:
indices[:, -1] = indices[:, 0] # make it uncoalesced
values_numel = values_shape.numel()
values = torch.arange(values_numel, dtype=self.value_dtype,
device=self.device).view(values_shape).div_(values_numel / 2.)
sp_tensor = self.sparse_tensor(indices, values, shape)
dtypes = [torch.int32]
if values.dtype == torch.double:
dtypes.append(torch.float)
else:
dtypes.append(torch.double)
for dtype in dtypes:
printed.append("########## {} ##########".format(dtype))
x = sp_tensor.detach().to(dtype)
printed.append("# sparse tensor")
printed.append(str(x))
if x.dtype.is_floating_point:
printed.append("# after requires_grad_")
printed.append(str(x.requires_grad_()))
printed.append("# after addition")
printed.append(str(x + x))
printed.append("# _indices")
printed.append(str(x._indices()))
printed.append("# _values")
printed.append(str(x._values()))
printed.append('')
self.assertExpected('\n'.join(printed))
def test_basic(self):
def test_shape(sparse_dims, nnz, with_size):
if isinstance(with_size, Number):
with_size = [with_size] * sparse_dims
x, i, v = self._gen_sparse(sparse_dims, nnz, with_size)
self.assertEqual(i, x._indices())
self.assertEqual(v, x._values())
self.assertEqual(x.ndimension(), len(with_size))
self.assertEqual(x.coalesce()._nnz(), nnz)
self.assertEqual(list(x.size()), with_size)
# Test .indices() and .values()
if self.is_uncoalesced:
with self.assertRaisesRegex(RuntimeError, "Cannot get indices on an uncoalesced tensor"):
x.indices()
with self.assertRaisesRegex(RuntimeError, "Cannot get values on an uncoalesced tensor"):
x.values()
else:
self.assertEqual(x.indices(), x._indices())
self.assertEqual(x.values(), x._values())
test_shape(3, 10, 100)
test_shape(3, 10, [100, 100, 100])
test_shape(3, 10, [100, 100, 100, 5, 5, 5, 0])
test_shape(3, 0, [0, 0, 100, 5, 5, 5, 0])
# Make sure that coalesce handles duplicate indices correctly
i = self.index_tensor([[9, 0, 0, 0, 8, 1, 1, 1, 2, 7, 2, 2, 3, 4, 6, 9]])
v = self.value_tensor([[idx**2, idx] for idx in range(i.size(1))])
x = self.sparse_tensor(i, v, torch.Size([10, 2]))
self.assertEqual(x.coalesce()._nnz(), 9)
# Make sure we can access empty indices / values
x = self.legacy_sparse_tensor()
self.assertEqual(x._indices().numel(), 0)
self.assertEqual(x._values().numel(), 0)
def test_coalesce(self):
def _test_coalesce(x):
tc = t.coalesce()
self.assertEqual(tc.to_dense(), t.to_dense())
self.assertTrue(tc.is_coalesced())
# Our code below doesn't work when nnz is 0, because
# then it's a 0D tensor, not a 2D tensor.
if t._nnz() == 0:
self.assertEqual(t._indices(), tc._indices())
self.assertEqual(t._values(), tc._values())
return tc
value_map: Dict[Any, Any] = {}
for idx, val in zip(t._indices().t(), t._values()):
idx_tup = tuple(idx.tolist())
if idx_tup in value_map:
value_map[idx_tup] += val
else:
value_map[idx_tup] = val.clone() if isinstance(val, torch.Tensor) else val
new_indices = sorted(list(value_map.keys()))
_new_values = [value_map[idx] for idx in new_indices]
if t._values().ndimension() < 2:
new_values = t._values().new(_new_values)
else:
new_values = torch.stack(_new_values)
new_indices = t._indices().new(new_indices).t()
tg = t.new(new_indices, new_values, t.size())
self.assertEqual(tc._indices(), tg._indices())
self.assertEqual(tc._values(), tg._values())
if t.is_coalesced():
self.assertEqual(tc._indices(), t._indices())
self.assertEqual(tc._values(), t._values())
for empty_i, empty_v, empty_nnz in itertools.product([True, False], repeat=3):
sparse_size = [] if empty_i else [2, 1]
dense_size = [1, 0, 2] if empty_v else [1, 2]
nnz = 0 if empty_nnz else 5
t, _, _ = self._gen_sparse(len(sparse_size), nnz, sparse_size + dense_size)
_test_coalesce(t) # this tests correctness
def test_ctor_size_checks(self):
indices = self.index_tensor([
[0, 0, 0],
[0, 3, 0],
[0, 0, 0],
[0, 0, 0],
])
values = self.value_tensor([2, 1, 3, 4])
# indices inconsistent with size
self.assertRaises(
RuntimeError,
lambda: self.sparse_tensor(indices, values, torch.Size([2, 1, 1])))
# values inconsistent with size
values = self.value_tensor([
[2, 1, 2, 1],
[1, 0, 5, 2],
])
self.assertRaises(
RuntimeError,
lambda: self.sparse_tensor(indices, values, torch.Size([2, 4, 2, 1])))
def test_to_dense(self):
def test_tensor(x, res):
x.to_dense() # Tests triple to_dense for memory corruption
x.to_dense()
x.to_dense()
# We dont have to_dense for half types, so we don't request
# exact_dtype if res.type is torch.float16.
dense_x = x.to_dense()
safe_dense_x = self.safeToDense(x)
if (res.dtype == torch.float16):
exact_dtype = False
else:
exact_dtype = True
dense_x = dense_x.to(res.dtype)
safe_dense_x = safe_dense_x.to(res.dtype)
self.assertEqual(res, dense_x, exact_dtype=exact_dtype)
self.assertEqual(res, safe_dense_x, exact_dtype=exact_dtype)
def fn(x):
return x.to_dense()
x.requires_grad_(True)
gradcheck(fn, (x,), check_sparse_nnz=True)
i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
# we don't have to_dense for half types on CPU because it is implemented
# with a slower add_ operation
for dtype in [torch.float16, torch.float32, torch.float64] if self.device != 'cpu' else [torch.float32, torch.float64]:
v = self.value_tensor([2, 1, 3, 4]).to(dtype=dtype)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5]))
res = self.value_tensor([
[[2, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 3, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 4]],
]).to(dtype=dtype)
test_tensor(x, res)
i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
v = self.value_empty(4, 0).to(dtype=dtype)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 0]))
res = self.value_empty(3, 4, 5, 0).to(dtype=dtype)
test_tensor(x, res)
# half tensors on cpu don't implement to_dense, so need to convert to float
def _to_dense_half_safe(self, tensor):
if(tensor.dtype == torch.half and tensor.device.type == 'cpu'):
return tensor.to(torch.float).to_dense().to(torch.half)
else:
return tensor.to_dense()
@skipIfRocm
def test_to_sparse(self):
shape = [10, 5, 19, 8]
max_nnz = 1
for dim, dim_sz in enumerate(shape, 1):
max_nnz *= dim_sz
rnnz = torch.randint(2, max_nnz, (1,)).item()
for nnz in [0, 1, rnnz]:
for dtype in [torch.float16, torch.float64, torch.int]:
expected, _, _ = self._gen_sparse(dim, nnz, shape)
expected = expected.to(dtype)
d = self._to_dense_half_safe(expected)
result = d.to_sparse(dim)
self.assertEqual(d, self._to_dense_half_safe(result)) # == not implemented for sparse tensors yet
self.assertEqual(expected.size(), result.size())
self.assertEqual(dim, result.sparse_dim())
sp, _, _ = self._gen_sparse(2, 10, [3, 3, 3])
self.assertRaises(RuntimeError, lambda: sp.to_sparse())
def test_scalar(self):
# tensor with value
a = self.sparse_tensor(self.index_tensor([]).unsqueeze(1), 12.3, [])
self.assertEqual(1, a._values().numel())
self.assertEqual(a, a.clone())
a_coalesced = a.coalesce()
self.assertTrue(a_coalesced.is_coalesced())
self.assertEqual(self.value_tensor(12.3), a.to_dense())
self.assertEqual(a, a.to_dense().to_sparse())
# tensor with multiple values
a = self.sparse_tensor(self.index_tensor([]).unsqueeze(1).expand(0, 2), [12.3, 12.3], [])
self.assertEqual(2, a._values().numel())
self.assertEqual(a, a.clone())
a_coalesced = a.coalesce()
self.assertTrue(a_coalesced.is_coalesced())
self.assertEqual(self.value_tensor(12.3 * 2), a.to_dense())
self.assertEqual(a, a.to_dense().to_sparse())
# tensor without value
a = self.sparse_empty(())
self.assertEqual(0, a._values().numel())
self.assertEqual(a, a.clone())
a_coalesced = a.coalesce()
self.assertTrue(a_coalesced.is_coalesced())
self.assertEqual(self.value_tensor(0), a.to_dense())
self.assertEqual(a, a.to_dense().to_sparse())
def test_shared(self):
i = self.index_tensor([[2]])
v = self.value_tensor([5])
x = self.sparse_tensor(i, v, torch.Size([3]))
v[0] = 6
self.assertEqual(self.value_tensor([0, 0, 6]), self.safeToDense(x))
i[0][0] = 0
self.assertEqual(self.value_tensor([6, 0, 0]), self.safeToDense(x))
i = self.index_tensor([[2]])
v = self.value_empty(1, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 0]))
i[0][0] = 0
self.assertEqual(self.value_empty(3, 0), self.safeToDense(x))
def test_to_dense_hybrid(self):
def test_tensor(x, res):
x.to_dense() # Tests double to_dense for memory corruption
x.to_dense()
x.to_dense()
self.assertEqual(res, x.to_dense())
self.assertEqual(res, self.safeToDense(x))
def fn(x):
return x.to_dense()
x.requires_grad_(True)
gradcheck(fn, (x,), check_sparse_nnz=True)
i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
])
v = self.value_tensor([[2, 3], [1, 2], [3, 4], [4, 5]])
x = self.sparse_tensor(i, v, torch.Size([3, 4, 2]))
res = self.value_tensor([
[[2, 3],
[0, 0],
[0, 0],
[0, 0]],
[[1, 2],
[0, 0],
[0, 0],
[0, 0]],
[[3, 4],
[0, 0],
[0, 0],
[4, 5]],
])
test_tensor(x, res)
i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
])
v = self.value_empty(4, 2, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 2, 0]))
res = self.value_empty(3, 4, 2, 0)
test_tensor(x, res)
def test_contig(self):
def test_tensor(x, exp_i, exp_v):
x = x.coalesce()
self.assertEqual(exp_i, x._indices())
self.assertEqual(exp_v, x._values())
i = self.index_tensor([
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
])
v = self.value_tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
x = self.sparse_tensor(i, v, torch.Size([100, 100]))
exp_i = self.index_tensor([
[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
])
exp_v = self.value_tensor([2, 1, 6, 4, 10, 3, 5, 9, 8, 7])
test_tensor(x, exp_i, exp_v)
i = self.index_tensor([
[2, 0, 2, 1],
[0, 0, 3, 0],
[1, 0, 4, 0],
])
v = self.value_tensor([3, 2, 4, 1])
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5]))
exp_i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
exp_v = self.value_tensor([2, 1, 3, 4])
test_tensor(x, exp_i, exp_v)
i = self.index_tensor([
[2, 0, 2, 1],
[0, 0, 3, 0],
[1, 0, 4, 0],
])
v = self.value_empty(4, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 0]))
exp_i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
exp_v = self.value_empty(4, 0)
test_tensor(x, exp_i, exp_v)
# Duplicate indices
i = self.index_tensor([
[0, 0, 2, 0],
[0, 0, 3, 0],
[0, 0, 4, 0],
])
v = self.value_tensor([3, 2, 4, 1])
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5]))
exp_i = self.index_tensor([
[0, 2],
[0, 3],
[0, 4],
])
exp_v = self.value_tensor([6, 4])
test_tensor(x, exp_i, exp_v)
i = self.index_tensor([
[0, 0, 2, 0],
[0, 0, 3, 0],
[0, 0, 4, 0],
])
v = self.value_empty(4, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 0]))
exp_i = self.index_tensor([
[0, 2],
[0, 3],
[0, 4],
])
exp_v = self.value_empty(2, 0)
test_tensor(x, exp_i, exp_v)
def test_contig_hybrid(self):
def test_tensor(x, exp_i, exp_v):
x = x.coalesce()
self.assertEqual(exp_i, x._indices())
self.assertEqual(exp_v, x._values())
i = self.index_tensor([
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
])
v = self.value_tensor([
[1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
[6, 7], [7, 8], [8, 9], [9, 10], [10, 11],
])
x = self.sparse_tensor(i, v, torch.Size([100, 100, 2]))
exp_i = self.index_tensor([
[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
])
exp_v = self.value_tensor([
[2, 3], [1, 2], [6, 7], [4, 5], [10, 11],
[3, 4], [5, 6], [9, 10], [8, 9], [7, 8],
])
test_tensor(x, exp_i, exp_v)
i = self.index_tensor([
[2, 0, 2, 1],
[0, 0, 3, 0],
[1, 0, 4, 0],
])
v = self.value_tensor([[3, 3, 3], [2, 2, 2], [4, 4, 4], [1, 1, 1]])
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3]))
exp_i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
exp_v = self.value_tensor([[2, 2, 2], [1, 1, 1], [3, 3, 3], [4, 4, 4]])
test_tensor(x, exp_i, exp_v)
i = self.index_tensor([
[2, 0, 2, 1],
[0, 0, 3, 0],
[1, 0, 4, 0],
])
v = self.value_empty(4, 3, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3, 0]))
exp_i = self.index_tensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
exp_v = self.value_empty(4, 3, 0)
test_tensor(x, exp_i, exp_v)
# Duplicate indices
i = self.index_tensor([
[0, 0, 2, 0],
[0, 0, 3, 0],
[0, 0, 4, 0],
])
v = self.value_tensor([[3, 2, 3], [2, 1, 1], [4, 3, 4], [1, 1, 1]])
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3]))
exp_i = self.index_tensor([
[0, 2],
[0, 3],
[0, 4],
])
exp_v = self.value_tensor([[6, 4, 5], [4, 3, 4]])
test_tensor(x, exp_i, exp_v)
i = self.index_tensor([
[0, 0, 2, 0],
[0, 0, 3, 0],
[0, 0, 4, 0],
])
v = self.value_empty(4, 3, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3, 0]))
exp_i = self.index_tensor([
[0, 2],
[0, 3],
[0, 4],
])
exp_v = self.value_empty(2, 3, 0)
test_tensor(x, exp_i, exp_v)
def test_clone(self):
def test_shape(sparse_dims, nnz, with_size):
x = self._gen_sparse(sparse_dims, nnz, with_size)[0]
if self.is_uncoalesced:
self.assertFalse(x.is_coalesced())
y = x.clone()
self.assertFalse(y.is_coalesced())
x = x.coalesce()
self.assertTrue(x.is_coalesced())
y = x.clone()
self.assertTrue(y.is_coalesced())
test_shape(4, 20, 5)
test_shape(3, 10, [100, 100, 100, 5, 5, 5, 0])
test_shape(3, 0, [0, 0, 100, 5, 5, 5, 0])
def test_Sparse_to_Sparse_copy_(self):
# This is for testing torch.copy_(SparseTensor, SparseTensor)
sparse_dims = 3
nnz = 10
sizes = [2, 3, 4, 5] # hybrid sparse
x1, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
x2, _, _ = self._gen_sparse(sparse_dims, nnz + 10, sizes)
# test copy
x2_dense = x2.to_dense()
x1.copy_(x2)
self.assertEqual(x2_dense, x1.to_dense())
# test type conversion (when x1.copy_(x2), x1.dtype should stay the same)
x1 = x1.to(torch.float32)
x2 = x2.to(torch.float16)
x1_dtype = x1.dtype
x1.copy_(x2)
self.assertEqual(x1_dtype, x1.dtype)
x2 = x2.to(torch.float64)
x1_dtype = x1.dtype
x1.copy_(x2)
self.assertEqual(x1_dtype, x1.dtype)
# test no broadcast
self.assertRaises(RuntimeError, lambda: x1.copy_(x2.narrow_copy(0, 0, 1)))
# test raise error on copy_() between dense and sparse Tensors
self.assertRaises(RuntimeError, lambda: x1.copy_(torch.randn(5, 5)))
# test autograd
x1, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
x2, _, _ = self._gen_sparse(sparse_dims, nnz + 10, sizes)
x2.requires_grad_(True)
x1.copy_(x2)
y = x1 * 2
x2_clone = x2.clone()
y.backward(x2_clone)
expected_grad = x2_clone * 2
self.assertEqual(expected_grad.to_dense(), x2.grad.to_dense())
self.assertEqual(None, x1.grad)
@unittest.skipIf(torch.cuda.device_count() < 2, "no multi-GPU")
@skipIfRocm
def test_Sparse_to_Sparse_copy_multi_gpu(self):
# This is for testing torch.copy_(SparseTensor, SparseTensor) across GPU devices
sparse_dims = 3
nnz = 10
sizes = [2, 3, 4, 5] # hybrid sparse
x1, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
x2, _, _ = self._gen_sparse(sparse_dims, nnz + 10, sizes)
x1 = x1.to('cuda:0')
def test_cross_device(x1, x2):
x1_device = x1.device
x1.copy_(x2)
self.assertEqual(x2.to('cuda:0').to_dense(), x1.to_dense())
self.assertEqual(x1_device, x1.device)
test_cross_device(x1, x2.to('cuda:1')) # test across gpu devices
test_cross_device(x1, x2.to('cpu')) # test between cpu and gpu
# test autograd
x2 = x2.to('cuda:1')
x2.requires_grad_(True)
x1.copy_(x2)
y = x1 * 2
x2_clone = x2.clone().to('cuda:0')
y.backward(x2_clone)
expected_grad = x2_clone * 2
self.assertEqual(expected_grad.to_dense(), x2.grad.to('cuda:0').to_dense())
self.assertEqual(None, x1.grad)
@cuda_only
def test_cuda_empty(self):
def test_tensor(x):
y = x.cuda(0)
self.assertEqual(x.sparse_dim(), y.sparse_dim())
self.assertEqual(x.dense_dim(), y.dense_dim())
x = y.cpu()
self.assertEqual(y.sparse_dim(), x.sparse_dim())
self.assertEqual(y.dense_dim(), x.dense_dim())
x = torch.sparse.FloatTensor(2, 3, 4)
test_tensor(x)
x = torch.sparse.HalfTensor(2, 3, 4)
test_tensor(x)
x = torch.cuda.sparse.HalfTensor(2, 3, 4)
test_tensor(x)
x = torch.sparse.FloatTensor(2, 3, 4, 0)
test_tensor(x)
def test_transpose(self):
def test_shape(sparse_dims, nnz, with_size):
x = self._gen_sparse(sparse_dims, nnz, with_size)[0]
y = self.safeToDense(x)
for i, j in itertools.combinations(range(4), 2):
x = x.transpose_(i, j)
y = y.transpose(i, j)
self.assertEqual(self.safeToDense(x), y)
x = x.transpose(i, j)
y = y.transpose(i, j)
self.assertEqual(self.safeToDense(x), y)
test_shape(4, 6, 3)
test_shape(4, 3, [7, 7, 7, 3, 3, 3, 0])
test_shape(4, 0, [0, 0, 7, 3, 3, 3, 0])
@cpu_only
def test_coalesce_transpose_mm(self):
def test_shape(di, dj, dk, nnz):
x, _, _ = self._gen_sparse(2, nnz, [dj, di])
y = torch.randn(dj, dk)
x_coalesced = x.coalesce()
self.assertTrue(x_coalesced.is_coalesced())
x_coalesced_t = x_coalesced.t()
# Transpose is `colasced`-preserving if the indices tensor is empty.
self.assertEqual(x_coalesced_t.is_coalesced(), di * nnz == 0)
res = torch.mm(x_coalesced_t, y)
expected = torch.mm(self.safeToDense(x_coalesced_t), y)
self.assertEqual(res, expected)
test_shape(10, 20, 30, 20)
test_shape(0, 20, 30, 0)
test_shape(10, 0, 30, 0)
test_shape(10, 20, 0, 0)
test_shape(10, 20, 0, 20)
def test_t_empty(self):
def test_in_place(x):
shape_original = x.shape
x.t_()
self.assertEqual(torch.Size([shape_original[1], shape_original[0]]), x.size())
self.assertEqual(0, x._indices().numel())
self.assertEqual(0, x._values().numel())
self.assertEqual(x.sparse_dim(), 2)
self.assertEqual(x.dense_dim(), 0)
def test_not_in_place(x):
shape_original = x.shape
y = x.t()
self.assertEqual(torch.Size([shape_original[1], shape_original[0]]), y.size())
self.assertEqual(0, y._indices().numel())
self.assertEqual(0, y._values().numel())
self.assertEqual(x.sparse_dim(), 2)
self.assertEqual(x.dense_dim(), 0)
x = self.sparse_empty(2, 3)
test_in_place(x)
test_not_in_place(x)
x = self.sparse_empty(2, 0)
test_in_place(x)
test_not_in_place(x)
def test_add_zeros(self):
def test_shape(sparse_dims, nnz, sizes):
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
zeros = torch.zeros(sizes, layout=torch.sparse_coo).to(x.device)
r1 = zeros + x
r2 = x + zeros
self.assertEqual(r1, x)
self.assertEqual(r2, x)
test_shape(1, 20, [1])
test_shape(4, 20, [3, 17, 19, 5])
test_shape(2, 20, [3, 17, 19, 5])
test_shape(2, 20, [3, 17, 19, 0])
def test_add_sub_nnz(self):
# nnz should not grow unbounded (gh-34964)
x = torch.randn(10, device=self.device).to_sparse()
x.add_(x)
x.add_(x)
self.assertLessEqual(x._nnz(), 10)
x.sub_(2 * x)
x.sub_(2 * x)
self.assertLessEqual(x._nnz(), 10)
def test_cat(self):
# shapes: list of tuples (sparse_dims, nnz, sizes)
def test_shapes(shapes, dim, fail_message=None):
inputs = [self._gen_sparse(shape[0], shape[1], shape[2])[0]
for shape in shapes]
if fail_message:
with self.assertRaisesRegex(RuntimeError, fail_message):
torch.cat(inputs, dim)
else:
result = torch.cat(inputs, dim)
dense_result = torch.cat([t.to_dense() for t in inputs], dim)
self.assertEqual(dense_result, result.to_dense())
test_shapes(
[(3, 10, [2, 3, 4]), (3, 10, [2, 1, 4]), (3, 10, [2, 4, 4])], 1)
# mismatched sizes
test_shapes([(3, 10, [2, 3, 4]), (3, 10, [2, 1, 4])], 0,
"All tensors must have the same shape: \\[2, 3, 4].*\\[2, 1, 4]")
# hybrid sparse/dense
test_shapes(
[(2, 10, [2, 3, 4]), (2, 10, [2, 1, 4]), (2, 10, [2, 4, 4])], 1)
# cat along dense dim
test_shapes([(2, 10, [2, 3, 4]), (2, 10, [2, 3, 7])], 2)
test_shapes([(1, 10, [2, 3, 4]), (1, 10, [2, 3, 4])], 1)
test_shapes([(1, 10, [2, 3, 4]), (1, 10, [2, 3, 4])], 2)
# mismatched dimensions
test_shapes([(2, 10, [2, 3, 4]), (3, 10, [2, 3, 4])], 0,
"All tensors must have the same.*2, 1, but tensor at position 1 has 3, 0.")
# wrapped dimension
test_shapes(
[(3, 10, [2, 3, 4]), (3, 10, [2, 1, 4]), (3, 10, [2, 4, 4])], -2)
# sparse with dense
sp = self._gen_sparse(3, 10, [2, 3, 4])[0]
dn = sp.to_dense()
with self.assertRaisesRegex(RuntimeError,
"Concatenating sparse tensors, but a dense tensor was found at position 1."):
torch.cat((sp, dn))
def test_unsqueeze(self):
def test_shape(sparse_dims, nnz, sizes, unsqueeze_dim, fail_message=None):
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
if fail_message:
with self.assertRaisesRegex(IndexError, fail_message):
torch.unsqueeze(x, unsqueeze_dim)
else:
result = torch.unsqueeze(x, unsqueeze_dim)
dense_result = torch.unsqueeze(x.to_dense(), unsqueeze_dim)
self.assertEqual(dense_result, result.to_dense())
# basic case
test_shape(3, 10, [5, 7, 11], 0)
# hybrid sparse/dense, unsqueeze along sparse dim
test_shape(3, 10, [5, 7, 11, 13, 17], 0)
test_shape(3, 10, [5, 7, 11, 13, 17], 3)
# unsqueeze along dense dimensions
test_shape(3, 10, [5, 7, 11, 13, 17], 4)
test_shape(3, 10, [5, 7, 11, 13, 17], 5)
# wrapped dimensions
test_shape(3, 10, [5, 7, 11, 13, 17], -1)
test_shape(3, 10, [5, 7, 11, 13, 17], -6)
# bounds
test_shape(3, 10, [5, 7, 11, 13, 17], -7, "Dimension out of range")
test_shape(3, 10, [5, 7, 11, 13, 17], 6, "Dimension out of range")
def test_select(self):
def test_shape(sparse_dims, nnz, sizes, select_dim, select_index, fail_message=None):
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
if fail_message:
with self.assertRaisesRegex(IndexError, fail_message):
torch.select(x, select_dim, select_index)
else:
result = torch.select(x, select_dim, select_index)
if result.is_sparse:
result = result.to_dense()
dense_result = torch.select(x.to_dense(), select_dim, select_index)
self.assertEqual(dense_result, result)
sizes = [5, 7, 11, 13, 17]
# hybrid sparse/dense, select sparse dim, result is dense
for i in range(sizes[0]):
test_shape(1, 10, sizes, 0, i)
test_shape(1, 10, sizes, 0, sizes[0] + 1, r'select[(][)][:] index \d out of range.*')
# hybrid sparse/dense, select sparse dim, result is sparse
for d in range(3):
for i in range(sizes[d]):
test_shape(3, 10, sizes, d, i)
# hybrid sparse/dense, select dense dim, result is sparse
for d in range(1, 3):
for i in range(sizes[d]):
test_shape(1, 10, sizes, d, i)
def test_index_select(self):
def test_shape(sparse_dims, nnz, sizes, select_dim, select_index, fail_message=None):
if isinstance(select_index, int):
select_index = [select_index]
if isinstance(select_index, list):
select_index = torch.tensor(select_index, device=self.device, dtype=torch.long)
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes)
if fail_message:
with self.assertRaisesRegex(IndexError, fail_message):
torch.index_select(x, select_dim, select_index)
else:
result = torch.index_select(x, select_dim, select_index)
if result.is_sparse:
result = result.to_dense()
dense_result = torch.index_select(x.to_dense(), select_dim, select_index)
self.assertEqual(dense_result, result)
sizes = [5, 7, 11, 13, 17]
for d in range(len(sizes)):
for index in [0, sizes[d] - 1, [0, sizes[d] // 2, sizes[d] - 1]]:
test_shape(1, 10, sizes, d, index)
test_shape(len(sizes) // 2, 10, sizes, d, index)
test_shape(len(sizes), 10, sizes, d, index)
@cpu_only
def test_mm(self):
def test_shape(di, dj, dk, nnz):
x, _, _ = self._gen_sparse(2, nnz, [di, dj])
t = torch.randn(di, dk)
y = torch.randn(dj, dk)
alpha = random.random()
beta = random.random()
res = torch.addmm(t, x, y, beta=beta, alpha=alpha)
expected = torch.addmm(t, self.safeToDense(x), y, beta=beta, alpha=alpha)
self.assertEqual(res, expected)
res = torch.addmm(t, x, y)
expected = torch.addmm(t, self.safeToDense(x), y)
self.assertEqual(res, expected)
res = torch.mm(x, y)
expected = torch.mm(self.safeToDense(x), y)
self.assertEqual(res, expected)
test_shape(10, 100, 100, 20)
test_shape(100, 1000, 200, 20)
test_shape(64, 10000, 300, 20)
test_shape(0, 100, 100, 0)
test_shape(10, 0, 100, 0)
test_shape(10, 100, 0, 0)
test_shape(10, 100, 0, 20)
@unittest.skipIf(
IS_WINDOWS and TEST_CUDA,
"bmm sparse-dense CUDA is not yet supported in Windows, at least up to CUDA 10.1"
)
@unittest.skipIf(
TEST_CUDA and _get_torch_cuda_version() < [10, 1],
"bmm sparse-dense requires CUDA 10.1 or greater"
)
def test_bmm(self):
def test_shape(num_mats, dim_i, dim_j, dim_k, nnz):
a_list = []
b_list = []
for mat_idx in range(num_mats):
a_mat = self._gen_sparse(2, nnz, [dim_i, dim_j])[0]
b_mat = torch.randn([dim_j, dim_k])
if self.is_cuda:
a_mat = a_mat.cuda()
b_mat = b_mat.cuda()
a_list.append(a_mat)
b_list.append(b_mat)
a = torch.stack(a_list)
b = torch.stack(b_list)
ab = a.bmm(b)
# Compare each matrix against result from mm()
for mat_idx in range(num_mats):
a_mat = a_list[mat_idx]
b_mat = b_list[mat_idx]
ab_mat_bmm = ab[mat_idx]
ab_mat_mm = a_mat.mm(b_mat)
self.assertEqual(ab_mat_bmm, ab_mat_mm)
test_shape(10, 10, 100, 99, 20)
test_shape(10, 100, 1000, 200, 20)
test_shape(10, 64, 10000, 300, 20)
test_shape(10, 0, 100, 99, 0)
test_shape(10, 10, 0, 100, 0)
test_shape(10, 10, 100, 0, 0)
test_shape(10, 10, 100, 0, 20)
test_shape(10, 10, 100, 0, 20)
a = torch.rand([10, 23, 32])
a[3] = torch.zeros(23, 32)
a[6] = torch.zeros(23, 32)
a = a.to_sparse()
b = torch.rand([10, 32, 10])
b[4] = torch.zeros(32, 10)
b[6] = torch.zeros(32, 10)
if self.is_cuda:
a = a.cuda()
b = b.cuda()
ab = a.bmm(b)
for mat_idx in range(ab.size(0)):
ab_mat = ab[mat_idx]
ab_mat_check = a[mat_idx].mm(b[mat_idx])
self.assertEqual(ab_mat, ab_mat_check)
ab_traspose_check = b.transpose(1, 2).to_sparse().bmm(
a.transpose(1, 2).to_dense()
).transpose(1, 2)
self.assertEqual(ab, ab_traspose_check)
@cuda_only
@unittest.skipIf(
IS_WINDOWS,
"bmm sparse-dense CUDA is not yet supported in Windows, at least up to CUDA 10.1"
)
@unittest.skipIf(
_get_torch_cuda_version() < [10, 1],
"bmm sparse-dense requires CUDA 10.1 or greater"
)
def test_bmm_deterministic(self):
def test_shape(num_mats, dim_i, dim_j, dim_k, nnz):
a_list = []
b_list = []
for mat_idx in range(num_mats):
a_list.append(self._gen_sparse(2, nnz, [dim_i, dim_j])[0])
b_list.append(torch.randn([dim_j, dim_k]))
a = torch.stack(a_list).cuda()
b = torch.stack(b_list).cuda()
ab_nondeterministic = torch._bmm(a, b, deterministic=False)
ab_deterministic = torch._bmm(a, b, deterministic=True)
diff_abs = (ab_deterministic - ab_nondeterministic).abs()
diff_rel = diff_abs / ab_deterministic.abs()
diff_rel[torch.isnan(diff_rel)] = 0
# deterministic and non-deterministic results should either be
# equal or within a small relative difference
equal_abs_or_rel = diff_abs.eq(0).logical_or(diff_rel.lt(0.001))
self.assertTrue(equal_abs_or_rel.all())
test_shape(10, 10, 100, 99, 20)
test_shape(10, 100, 1000, 200, 20)
test_shape(10, 64, 10000, 300, 20)
test_shape(10, 0, 100, 99, 0)
test_shape(10, 10, 0, 100, 0)
test_shape(10, 10, 100, 0, 0)
test_shape(10, 10, 100, 0, 20)
test_shape(10, 10, 100, 0, 20)
@cuda_only
@unittest.skipIf(
not IS_WINDOWS or _get_torch_cuda_version() >= [11, 0],
"this test ensures bmm sparse-dense CUDA gives an error when run on Windows with CUDA < 11.0"
)
def test_bmm_windows_error(self):
a = torch.rand(2, 2, 2).to_sparse().cuda()
b = torch.rand(2, 2, 2).cuda()
with self.assertRaisesRegex(
RuntimeError,
"bmm sparse-dense CUDA is not supported on Windows with cuda before 11.0"):
ab = a.bmm(b)
@cuda_only
@skipIfRocm
@unittest.skipIf(
_get_torch_cuda_version() >= [10, 1],
"this test ensures bmm gives error if CUDA version is less than 10.1"
)
def test_bmm_cuda_version_error(self):
a = torch.rand(2, 2, 2).to_sparse().cuda()
b = torch.rand(2, 2, 2).cuda()
with self.assertRaisesRegex(
RuntimeError,
"bmm sparse-dense requires CUDA 10.1 or greater"):
ab = a.bmm(b)
@cpu_only
def test_saddmm(self):
def test_shape(di, dj, dk, nnz):
x = self._gen_sparse(2, nnz, [di, dj])[0]
t = self._gen_sparse(2, nnz, [di, dk])[0]
y = torch.randn(dj, dk)
alpha = random.random()
beta = random.random()
res = torch.saddmm(t, x, y, beta=beta, alpha=alpha)
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y, beta=beta, alpha=alpha)
self.assertEqual(self.safeToDense(res), expected)
res = torch.saddmm(t, x, y)
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y)
self.assertEqual(self.safeToDense(res), expected)
res = torch.smm(x, y)
expected = torch.mm(self.safeToDense(x), y)
self.assertEqual(self.safeToDense(res), expected)
test_shape(7, 5, 3, 20)
test_shape(1000, 100, 100, 20)
test_shape(3000, 64, 300, 20)
test_shape(0, 100, 100, 0)
test_shape(1000, 0, 100, 0)
test_shape(1000, 100, 0, 0)
@cpu_only
def test_sspaddmm(self):
def test_shape(di, dj, dk, nnz):
x = self._gen_sparse(2, nnz, [di, dj])[0]
t = self._gen_sparse(2, nnz, [di, dk])[0]
y = torch.randn(dj, dk)
alpha = random.random()
beta = random.random()
res = t.sspaddmm(x, y, beta=beta, alpha=alpha)
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y, beta=beta, alpha=alpha)
self.assertEqual(self.safeToDense(res), expected)
res = t.sspaddmm(x, y)
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y)
self.assertEqual(self.safeToDense(res), expected)
test_shape(7, 5, 3, 20)
test_shape(1000, 100, 100, 20)
test_shape(3000, 64, 300, 20)
test_shape(0, 100, 100, 0)
test_shape(1000, 0, 100, 0)
test_shape(1000, 100, 0, 0)
# Test code from issue https://github.com/pytorch/pytorch/issues/45113
batch_size, input_size, hidden_size = 5, 3, 7
# Create coalesced sparse tensor as in the issue
weight = torch.randn(hidden_size, input_size).to_sparse()
self.assertTrue(weight.is_coalesced())
self.assertFalse(weight._indices().is_contiguous())
# Create un/coalesced sparse tensor
bias = torch.randn((hidden_size, 1)).to_sparse()
bias = torch.cat([bias] * batch_size, dim=1)
if not self.is_uncoalesced:
bias = bias.coalesce()
x = torch.randn(input_size, batch_size)
res = bias.sspaddmm(weight, x)
true_result = (bias.to_dense() + torch.matmul(weight.to_dense(), x)).to_sparse()
self.assertEqual(self.safeToDense(res), self.safeToDense(true_result))
def test_sparse_addmm(self):
def test_shape(m, n, p, nnz, broadcast):
if broadcast:
D1 = torch.randn((), device=self.device).requires_grad_(True)
else:
D1 = torch.randn(n, p, device=self.device).requires_grad_(True)
D2 = torch.randn(m, p, device=self.device).requires_grad_(True)
S = self._gen_sparse(2, nnz, [n, m])[0]
S_dense = S.to_dense().requires_grad_(True)
S.requires_grad_(True)
self.assertEqual(torch.sparse.addmm(D1, S, D2), torch.addmm(D1, S_dense, D2))
def fn(S, D1, D2):
return torch.sparse.addmm(D1, S, D2)
gradcheck(fn, (S, D1, D2), check_sparse_nnz=True)
test_shape(7, 8, 9, 20, False)
test_shape(7, 8, 9, 20, True)
def test_sparse_mm(self):
def test_shape(d1, d2, d3, nnz, transposed):
if transposed:
D = torch.randn(d3, d2,
device=self.device).t_().requires_grad_(True)
else:
D = torch.randn(d2, d3, device=self.device).requires_grad_(True)
S = self._gen_sparse(2, nnz, [d1, d2])[0]
S_dense = S.to_dense().requires_grad_(True)
S.requires_grad_(True)
self.assertEqual(torch.sparse.mm(S, D), torch.mm(S_dense, D))
def fn(S, D):
return torch.sparse.mm(S, D)
gradcheck(fn, (S, D), check_sparse_nnz=True)
test_shape(7, 8, 9, 20, False)
test_shape(7, 8, 9, 20, True)
def test_dsmm(self):
def test_shape(di, dj, dk, nnz):
x = self._gen_sparse(2, nnz, [di, dj])[0]
y = self.randn(dj, dk)
res = torch.dsmm(x, y)
expected = torch.mm(self.safeToDense(x), y)
self.assertEqual(res, expected)
test_shape(7, 5, 3, 20)
test_shape(1000, 100, 100, 20)
test_shape(3000, 64, 300, 20)
test_shape(0, 100, 100, 0)
test_shape(1000, 0, 100, 0)
test_shape(1000, 100, 0, 0)
test_shape(1000, 100, 0, 20)
@skipIfRocm
def test_hsmm(self):
def test_shape(di, dj, dk, nnz):
x = self._gen_sparse(2, nnz, [di, dj])[0]
y = self.randn(dj, dk)
res = torch.hsmm(x, y)
expected = torch.mm(self.safeToDense(x), y)
self.assertEqual(res.to_dense(), expected)
test_shape(7, 5, 3, 20)
test_shape(1000, 100, 100, 20)
test_shape(3000, 64, 300, 20)
test_shape(0, 100, 100, 0)
test_shape(1000, 0, 100, 0)
test_shape(1000, 100, 0, 0)
test_shape(1000, 100, 0, 20)
def _test_spadd_shape(self, nnz, shape_i, shape_v=None):
shape = shape_i + (shape_v or [])
x, _, _ = self._gen_sparse(len(shape_i), nnz, shape)
y = self.randn(*shape)
r = random.random()
res = torch.add(y, x, alpha=r)
expected = y + r * self.safeToDense(x)
self.assertEqual(res, expected)
# Non contiguous dense tensor
s = list(shape)
s[0] = shape[-1]
s[-1] = shape[0]
y = self.randn(*s)
y.transpose_(0, len(s) - 1)
r = random.random()
res = torch.add(y, x, alpha=r)
expected = y + r * self.safeToDense(x)
self.assertEqual(res, expected)
x, i, v = self._gen_sparse(len(shape_i), nnz, shape)
nnz = i.size(1)
# Non contiguous sparse indices tensor
x_ = self.sparse_tensor(i[:, ::2], v[:int(nnz / 2)], x.shape)
res = torch.add(y, x_, alpha=r)
expected = y + r * self.safeToDense(x_)
self.assertEqual(res, expected)
# Non contiguous sparse values tensor
x_ = self.sparse_tensor(i[:, :int(nnz / 2)], v[::2], x.shape)
res = torch.add(y, x_, alpha=r)
expected = y + r * self.safeToDense(x_)
self.assertEqual(res, expected)
# Non contiguous sparse indices and values tensors
x_ = self.sparse_tensor(i[:, 1::2], v[1::2], x.shape)
res = torch.add(y, x_, alpha=r)
expected = y + r * self.safeToDense(x_)
self.assertEqual(res, expected)
def test_spadd(self):
self._test_spadd_shape(10, [5, 6])
self._test_spadd_shape(10, [10, 10, 10])
self._test_spadd_shape(10, [50, 30, 20])
self._test_spadd_shape(10, [5, 5, 5, 5, 5, 5])
self._test_spadd_shape(0, [0, 30, 20])
self._test_spadd_shape(0, [50, 0, 20])
self._test_spadd_shape(0, [50, 30, 0])
def test_spadd_hybrid(self):
self._test_spadd_shape(10, [5, 6], [2, 3])
self._test_spadd_shape(10, [10, 10, 10], [3])
self._test_spadd_shape(10, [50, 30, 20], [2])
self._test_spadd_shape(10, [5, 5, 5, 5, 5, 5], [2])
self._test_spadd_shape(0, [0, 30, 20], [2, 0])
self._test_spadd_shape(0, [50, 0, 20], [2, 0])
self._test_spadd_shape(0, [50, 30, 0], [2, 0])
self._test_spadd_shape(10, [50, 30, 20], [2, 0])
@cuda_only
def test_sparse_add_out_bfloat16(self):
# fp32
x, _, _ = self._gen_sparse(3, 5, 10)
y, _, _ = self._gen_sparse(3, 5, 10)
x = x.float().cuda()
y = y.float().cuda()
res_fp32 = torch.add(x, y)
# bfloat16
x = x.bfloat16()
y = y.bfloat16()
res_bf16 = torch.add(x, y)
res_bf16 = res_bf16.float() # to compare with reference
self.assertEqual(res_fp32, res_bf16, atol=1e-2, rtol=0)
def test_norm(self):
def test_shape(sparse_dims, nnz, with_size):
x, _, _ = self._gen_sparse(sparse_dims, nnz, with_size)
y = x.coalesce()
self.assertEqual(x.norm(), y._values().norm())
test_shape(3, 10, 100)
test_shape(4, 10, [100, 100, 100, 5, 5, 5, 0])
test_shape(4, 0, [0, 0, 100, 5, 5, 5, 0])
# Unsupported arguments should error
kwarg_error_pairs = [
({'keepdim': True},
RuntimeError, r'norm_sparse currently does not support keepdim=True'),
({'dim': 0},
RuntimeError, r'norm_sparse currently only supports full reductions'),
({'dtype': torch.double, 'p': 'fro'},
ValueError, r'dtype argument is not supported in frobenius norm'),
({'dtype': torch.double, 'p': 0},
RuntimeError, r"norm_sparse currently does not support 'dtype' argument")
]
x = self._gen_sparse(3, 10, 100)[0]
for kwargs, err, msg in kwarg_error_pairs:
with self.assertRaisesRegex(err, msg):
x.norm(**kwargs)
@skipIfRocm
def test_sparse_sum(self):
def run_tests(S, td=None):
D = S.coalesce().to_dense().detach().requires_grad_(True)
mask = (D == 0)
if td is None:
S_sum = torch.sparse.sum(S)
D_sum = D.sum()
self.assertEqual(S_sum, D_sum)
def fn(S):
res = torch.sparse.sum(S)
if res.is_sparse:
res = res.to_dense()
return res
gradcheck(fn, (S,), check_sparse_nnz=True)
else:
S_sum = torch.sparse.sum(S, td)
D_sum = D.sum(td)
self.assertEqual(S_sum.to_dense() if S_sum.is_sparse else S_sum, D_sum)
def fn(S):
res = torch.sparse.sum(S, td)
if res.is_sparse:
res = res.to_dense()
return res
gradcheck(fn, (S,), check_sparse_nnz=True)
nnz = 10
sparse_dims = 2
with_size = [5, 5, 1, 4] # use a dense dim = 1 to test for squeeze
test_dims = []
for i in range(1, 5):
test_dims += itertools.combinations(range(len(with_size)), i)
# https://github.com/pytorch/pytorch/issues/16501
x = torch.tensor([[1., 0., 0., 1.],
[0., 1., 0., 0.],
[0., 1., 1., 0.],
[0., 1., 0., 2.]]).to_sparse()
self.assertEqual(torch.sparse.sum(x, dim=0), torch.sparse.sum(x, dim=-2))
self.assertEqual(torch.sum(x.to_dense(), dim=0), torch.sparse.sum(x, dim=0).to_dense())
# not support SparseTensor.sum()
S = self._gen_sparse(sparse_dims, nnz, with_size)[0]
self.assertRaises(RuntimeError, lambda: S.sum())
# dim out of range
self.assertRaises(IndexError, lambda: torch.sparse.sum(S, 5))
# dim 0 appears multiple times in the list of dims
self.assertRaises(RuntimeError, lambda: torch.sparse.sum(S, [0, 0]))
# sum an empty tensor
empty_S = torch.sparse_coo_tensor(size=with_size)
self.assertRaises(RuntimeError, lambda: torch.sparse.sum(empty_S, [0]))
self.assertEqual(torch.sparse.sum(empty_S), torch.tensor(0, dtype=torch.float64))
empty_S.requires_grad_(True)
empty_S_sum = torch.sparse.sum(empty_S)
empty_S_sum.backward()
self.assertEqual(empty_S.grad.to_dense(), empty_S.clone().detach().to_dense())
# test values().sum()
S = self._gen_sparse(sparse_dims, nnz, with_size)[0]
run_tests(S.requires_grad_(True))
for test_dim in test_dims:
S = self._gen_sparse(sparse_dims, nnz, with_size)[0]
run_tests(S.requires_grad_(True), test_dim)
def _test_basic_ops_shape(self, nnz_x1, nnz_x2, shape_i, shape_v=None):
shape = shape_i + (shape_v or [])
x1, _, _ = self._gen_sparse(len(shape_i), nnz_x1, shape)
x2, _, _ = self._gen_sparse(len(shape_i), nnz_x2, shape)
y1 = x1 + x2
y2 = x1.clone()
y2.add_(x2)
expected = self.safeToDense(x1) + self.safeToDense(x2)
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
y1 = x1 - x2
y2 = x1.clone()
y2.sub_(x2)
expected = self.safeToDense(x1) - self.safeToDense(x2)
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
y1 = x1 * x2
y2 = x1.clone()
y2.mul_(x2)
expected = self.safeToDense(x1) * self.safeToDense(x2)
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
y1 = x1 * 37.5
y2 = x1.clone()
y2.mul_(37.5)
expected = self.safeToDense(x1) * 37.5
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
y1 = x1 / 37.5
y2 = x1.clone()
y2.div_(37.5)
expected = self.safeToDense(x1) / 37.5
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
y1 = x1 // 37.5
y2 = x1.clone()
y2.floor_divide_(37.5)
expected = self.safeToDense(x1) // 37.5
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
# TODO: add back inplace support
y1 = x1 ** 2
y2 = x1.clone()
y2 = y2.pow(2)
expected = self.safeToDense(x1) ** 2
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
y = x1.clone()
y.zero_()
expected = torch.zeros(x1.size())
self.assertEqual(self.safeToDense(y), expected)
self.assertEqual(x1.is_coalesced(), not self.is_uncoalesced)
y = x1.coalesce()
z = x1.coalesce()
self.assertEqual(x1.is_coalesced(), not self.is_uncoalesced)
self.assertTrue(y.is_coalesced())
self.assertEqual(x1, y)
y._values().add_(1)
if not x1.is_coalesced():
# check that coalesce is out of place if the original tensor is not
# coalesced.
self.assertEqual(z._values() + 1, y._values())
else:
# check that coalesce is in-place if the original tensor is
# coalesced.
self.assertEqual(z._values(), y._values())
def test_basic_ops(self):
self._test_basic_ops_shape(9, 12, [5, 6])
self._test_basic_ops_shape(9, 12, [10, 10, 10])
self._test_basic_ops_shape(9, 12, [50, 30, 20])
self._test_basic_ops_shape(9, 12, [5, 5, 5, 5, 5, 5])
self._test_basic_ops_shape(0, 12, [10, 10, 10])
self._test_basic_ops_shape(9, 0, [10, 10, 10])
self._test_basic_ops_shape(0, 0, [10, 10, 10])
self._test_basic_ops_shape(0, 0, [10, 10, 0])
def test_basic_ops_hybrid(self):
self._test_basic_ops_shape(9, 12, [5, 6], [2, 3])
self._test_basic_ops_shape(9, 12, [10, 10, 10], [3])
self._test_basic_ops_shape(9, 12, [50, 30, 20], [2])
self._test_basic_ops_shape(9, 12, [5, 5, 5, 5, 5, 5], [2])
self._test_basic_ops_shape(0, 12, [10, 10, 10], [2])
self._test_basic_ops_shape(9, 0, [10, 10, 10], [2])
self._test_basic_ops_shape(0, 0, [10, 10, 10], [2])
self._test_basic_ops_shape(9, 12, [10, 10, 10], [2, 0])
self._test_basic_ops_shape(0, 12, [10, 10, 10], [2, 0])
self._test_basic_ops_shape(9, 0, [10, 10, 10], [2, 0])
self._test_basic_ops_shape(0, 0, [10, 10, 10], [2, 0])
self._test_basic_ops_shape(0, 0, [10, 10, 0], [2, 0])
def test_add_dense_sparse_mismatch(self):
def test_shape(dense_size, sparse_dims_shape, dense_dims_shape, sparse_size):
x = torch.zeros(dense_size, dtype=self.value_dtype, device=self.device)
sparse_y = self.sparse_tensor(torch.zeros(sparse_dims_shape, dtype=torch.int64, device=self.device),
torch.randn(dense_dims_shape, dtype=self.value_dtype, device=self.device),
torch.Size(sparse_size))
with self.assertRaisesRegex(
RuntimeError,
"add: expected 'self' and 'other' to have same size"):
x + sparse_y
test_shape([3, 4], [1, 4], [4, 4, 4], [3, 4, 4])
test_shape([3, 4, 0], [1, 4], [4, 4, 4, 0], [3, 4, 4, 0])
def test_add_noncontiguous(self):
indices = self.index_tensor([[1, 2], [0, 2]])
values = self.value_tensor([1.]).expand(2, 3, 4, 5)
x = self.sparse_tensor(indices, values)
assert not x._values().is_contiguous()
y = x + x
expected = self.safeToDense(x) + self.safeToDense(x)
self.assertEqual(self.safeToDense(y), expected)
def _test_sparse_mask_shape(self, nnz_x1, nnz_x2, shape_i, shape_v=None):
shape = shape_i + (shape_v or [])
x1, _, _ = self._gen_sparse(len(shape_i), nnz_x1, shape)
x2, _, _ = self._gen_sparse(len(shape_i), nnz_x2, shape)
y1 = x1 + x2
y2 = x1.clone()
y2.add_(x2)
expected = self.safeToDense(x1) + self.safeToDense(x2)
self.assertEqual(self.safeToDense(y1), expected)
self.assertEqual(self.safeToDense(y2), expected)
def _test_sparse_mask_fixed(self):
i = self.index_tensor([
[1, 3, 0, 4],
[2, 1, 2, 3],
])
v = self.value_tensor([1, 2, 3, 4])
x = self.sparse_tensor(i, v, torch.Size([5, 4])).coalesce()
dense = self.value_tensor([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20],
])
exp_v = self.value_tensor([7, 14, 3, 20])
res = dense.sparse_mask(x)
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4]))
self.assertEqual(res, expected)
i = self.index_tensor([
[1, 3, 0, 4],
[2, 1, 2, 3],
])
v = self.value_empty(4, 0)
x = self.sparse_tensor(i, v, torch.Size([5, 4, 0])).coalesce()
dense = self.value_empty(5, 4, 0)
exp_v = self.value_empty(4, 0)
res = dense.sparse_mask(x)
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4, 0]))
self.assertEqual(res, expected)
def test_sparse_mask(self):
self._test_sparse_mask_fixed()
self._test_sparse_mask_shape(9, 12, [5, 6])
self._test_sparse_mask_shape(9, 12, [10, 10, 10])
self._test_sparse_mask_shape(9, 12, [50, 30, 20])
self._test_sparse_mask_shape(9, 12, [5, 5, 5, 5, 5, 5])
self._test_sparse_mask_shape(0, 12, [10, 10, 10])
self._test_sparse_mask_shape(9, 0, [10, 10, 10])
self._test_sparse_mask_shape(0, 0, [10, 10, 10])
self._test_sparse_mask_shape(0, 0, [10, 10, 0])
def _test_sparse_mask_hybrid_fixed(self):
i = self.index_tensor([
[1, 3, 0, 4],
[2, 1, 2, 3],
])
v = self.value_tensor([[1, 2], [2, 3], [3, 4], [4, 5]])
# TODO: This is also testing that, if coalesce is a no-op,
# the indices don't get permuted. I don't know if we actually
# want to give this invariant.
x = self.sparse_tensor(i, v, torch.Size([5, 4, 2])).coalesce()
dense = self.value_tensor([
[[1, 3], [2, 2], [3, 3], [4, 2]],
[[5, 7], [6, 7], [7, 9], [8, 9]],
[[9, 2], [10, 4], [11, 1], [12, 3]],
[[13, 5], [14, 1], [15, 1], [16, 6]],
[[17, 7], [18, 2], [19, 7], [20, 1]],
])
res = dense.sparse_mask(x)
exp_v = self.value_tensor([[7, 9], [14, 1], [3, 3], [20, 1]])
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4, 2]))
self.assertEqual(res, expected)
i = self.index_tensor([
[1, 3, 0, 4],
[2, 1, 2, 3],
])
v = self.value_empty(4, 2, 0)
x = self.sparse_tensor(i, v, torch.Size([5, 4, 2, 0])).coalesce()
dense = self.value_empty(5, 4, 2, 0)
res = dense.sparse_mask(x)
exp_v = self.value_empty(4, 2, 0)
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4, 2, 0]))
self.assertEqual(res, expected)
def test_sparse_mask_hybrid(self):
self._test_sparse_mask_hybrid_fixed()
self._test_sparse_mask_shape(9, 12, [5, 6], [2, 3])
self._test_sparse_mask_shape(9, 12, [10, 10, 10], [3])
self._test_sparse_mask_shape(9, 12, [50, 30, 20], [2])
self._test_sparse_mask_shape(9, 12, [5, 5, 5, 5, 5, 5], [2])
self._test_sparse_mask_shape(0, 12, [10, 10, 10], [2])
self._test_sparse_mask_shape(9, 0, [10, 10, 10], [2])
self._test_sparse_mask_shape(0, 0, [10, 10, 10], [2])
self._test_sparse_mask_shape(9, 12, [10, 10, 10], [2, 0])
self._test_sparse_mask_shape(0, 12, [10, 10, 10], [2, 0])
self._test_sparse_mask_shape(9, 0, [10, 10, 10], [2, 0])
self._test_sparse_mask_shape(0, 0, [10, 10, 10], [2, 0])
self._test_sparse_mask_shape(0, 0, [10, 10, 0], [2, 0])
def _test_zeros(self, nnzs, shape, out_shape_i, out_shape_v=None):
out_shape = out_shape_i + (out_shape_v or [])
for nnz in nnzs:
out, _, _ = self._gen_sparse(len(out_shape_i), nnz, out_shape)
torch.zeros(*shape, out=out)
self.assertEqual(tuple(out.size()), tuple(shape))
self.assertTrue(out._indices().numel() == out._values().numel() == 0)
self.assertEqual(out._nnz(), 0)
self.assertEqual(out.sparse_dim(), len(shape))
self.assertEqual(out.dense_dim(), 0)
def test_zeros(self):
def test_shape(i_shapes, v_shapes, shape, nnzs):
for i_dim in range(1, len(i_shapes) + 1):
for v_dim in range(len(v_shapes) + 1):
self._test_zeros(nnzs, shape, i_shapes[:i_dim], v_shapes[:v_dim])
test_shape([2, 3, 4], [3, 4, 5, 6], [2, 3, 4], [9, 12])
test_shape([0, 3, 4], [3, 4, 5, 6], [2, 3, 4], [0])
test_shape([2, 3, 4], [0, 4, 5, 6], [2, 3, 4], [9, 12])
test_shape([2, 3, 4], [3, 4, 5, 6], [2, 3, 0], [9, 12])
test_shape([0, 3, 4], [3, 4, 5, 6], [2, 3, 0], [0])
test_shape([2, 3, 4], [0, 4, 5, 6], [2, 3, 0], [9, 12])
def _test_zeros_like(self, nnzs, template_shape_i, template_shape_v=None):
template_shape_v = template_shape_v or []
template_shape = template_shape_i + template_shape_v
for nnz in nnzs:
t, _, _ = self._gen_sparse(len(template_shape_i), nnz, template_shape)
res = torch.zeros_like(t)
self.assertEqual(tuple(res.size()), tuple(template_shape))
self.assertTrue(res._indices().numel() == res._values().numel() == 0)
self.assertEqual(res._nnz(), 0)
self.assertEqual(res.sparse_dim(), len(template_shape_i))
self.assertEqual(res.dense_dim(), len(template_shape_v))
def test_zeros_like(self):
def test_shape(i_shapes, v_shapes, nnzs):
for i_dim in range(1, len(i_shapes) + 1):
for v_dim in range(len(v_shapes) + 1):
self._test_zeros_like(nnzs, i_shapes[:i_dim], v_shapes[:v_dim])
test_shape([2, 3, 4], [3, 4, 5, 6], [9, 12])
test_shape([0, 3, 4], [3, 4, 5, 6], [0])
test_shape([2, 3, 4], [0, 4, 5, 6], [9, 12])
test_shape([2, 3, 4], [3, 4, 5, 6], [9, 12])
test_shape([0, 3, 4], [3, 4, 5, 6], [0])
test_shape([2, 3, 4], [0, 4, 5, 6], [9, 12])
sparse_tensor, _, _ = self._gen_sparse(len([2, 3]), 9, [2, 3] + [5, 6])
data = (sparse_tensor, sparse_tensor, sparse_tensor, sparse_tensor.unsqueeze(0))
mem_formats = [torch.channels_last, torch.contiguous_format, torch.preserve_format, torch.channels_last_3d]
for x, mem_format in zip(data, mem_formats):
with self.assertRaisesRegex(RuntimeError, "memory format option is only supported by strided tensors"):
result = torch.zeros_like(x, memory_format=mem_format)
result = torch.zeros_like(x, layout=torch.strided, memory_format=mem_format)
self.assertTrue(result.layout == torch.strided)
with self.assertRaisesRegex(
RuntimeError, r"Could not run 'aten::empty_strided' with arguments from the 'Sparse(CPU|CUDA)' backend"
):
dense_tensor = sparse_tensor.to_dense()
result = torch.zeros_like(dense_tensor, layout=torch.sparse_coo)
def _assert_sparse_invars(self, t):
# SparseTensor has the following invariants:
# - sparse_dim + dense_dim = len(SparseTensor.shape)
# - SparseTensor._indices().shape = (sparse_dim, nnz)
# - SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])
self.assertEqual(t.sparse_dim() + t.dense_dim(), len(t.shape))
self.assertEqual(tuple(t._indices().shape), (t.sparse_dim(), t._nnz()))
self.assertEqual(tuple(t._values().shape), (t._nnz(), ) + t.shape[t.sparse_dim():])
def _test_empty_like(self, sparse_tensor):
result = torch.empty_like(sparse_tensor)
self.assertTrue(result.is_sparse)
self._assert_sparse_invars(result)
self.assertEqual(result.shape, sparse_tensor.shape)
self.assertEqual(result.dtype, sparse_tensor.dtype)
self.assertEqual(result.device, sparse_tensor.device)
self.assertEqual(result.sparse_dim(), sparse_tensor.sparse_dim())
self.assertEqual(result.dense_dim(), sparse_tensor.dense_dim())
sparse_tensor, _, _ = self._gen_sparse(len([2, 3]), 9, [2, 3] + [5, 6])
data = (sparse_tensor, sparse_tensor, sparse_tensor, sparse_tensor.unsqueeze(0))
mem_formats = [torch.channels_last, torch.contiguous_format, torch.preserve_format, torch.channels_last_3d]
for x, mem_format in zip(data, mem_formats):
with self.assertRaisesRegex(RuntimeError, "memory format option is only supported by strided tensors"):
result = torch.empty_like(x, memory_format=mem_format)
result = torch.empty_like(x, layout=torch.strided, memory_format=mem_format)
self.assertTrue(result.layout == torch.strided)
with self.assertRaisesRegex(
RuntimeError, r"Could not run 'aten::empty_strided' with arguments from the 'Sparse(CPU|CUDA)' backend"
):
dense_tensor = sparse_tensor.to_dense()
result = torch.empty_like(dense_tensor, layout=torch.sparse_coo)
def test_empty_like(self):
# tests https://github.com/pytorch/pytorch/issues/43699
if not self.is_uncoalesced:
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0, 1, 2]]),
values=torch.tensor([3.0, -4.0, 5.0]),
size=[3, ],
device=self.device
).coalesce()
self._test_empty_like(input_coalesced)
# hybrid sparse input
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[1, 3], [2, 4]]),
values=torch.tensor([[-1.0, 3.0], [-5.0, 7.0]]),
size=[4, 5, 2],
device=self.device
).coalesce()
self._test_empty_like(input_coalesced)
if self.is_uncoalesced:
# test uncoalesced input
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
values=torch.tensor([2.0, -3.0, -4.0, 1.0, -1.0, 1.5]),
size=[3, ],
device=self.device
)
self._test_empty_like(input_uncoalesced)
# test on empty sparse tensor
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.zeros([2, 0]),
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
device=self.device
)
self._test_empty_like(input_uncoalesced)
def _test_narrow(self, input, narrow_args):
expected = input.to_dense().narrow(*narrow_args)
self.assertEqual(expected, input.narrow_copy(*narrow_args).to_dense())
def _all_narrow_combs(self, shape):
for dim, dim_sz in enumerate(shape):
for start in range(dim_sz):
for length in range(dim_sz - start):
yield [dim, start, length]
def test_narrow(self):
shape = [3, 3, 4, 2]
input, _, _ = self._gen_sparse(4, 19, shape)
for narrow_args in self._all_narrow_combs(shape):
self._test_narrow(input, narrow_args)
self.assertRaises(RuntimeError, lambda: input.narrow_copy(-1, 0, 3)) # dim < 0
self.assertRaises(RuntimeError, lambda: input.narrow_copy(10, 0, 3)) # dim > input.dim()
self.assertRaises(RuntimeError, lambda: input.narrow_copy(0, shape[0] + 1, 3)) # start > size of dim
self.assertRaises(RuntimeError, lambda: input.narrow_copy(0, 2, shape[0])) # start+length > size of dim
with_dense, _, _ = self._gen_sparse(2, 7, shape)
for narrow_args in self._all_narrow_combs(shape):
self._test_narrow(with_dense, narrow_args)
self.assertRaises(RuntimeError, lambda: with_dense.narrow_copy(10, 0, 3)) # dim > sparseDim + denseDim
def _test_log1p_tensor(self, sparse_tensor):
def is_integral(dtype):
return dtype in torch.testing.get_all_int_dtypes()
dense_tensor = sparse_tensor.to_dense()
expected_output = dense_tensor.log1p()
is_integral_dtype = is_integral(sparse_tensor.dtype)
self.assertEqual(expected_output, sparse_tensor.log1p().to_dense())
if is_integral_dtype:
with self.assertRaisesRegex(RuntimeError, "log1p: result type cannot be Integral, got:"):
sparse_tensor.coalesce().log1p_()
else:
self.assertEqual(expected_output, sparse_tensor.coalesce().log1p_().to_dense())
if self.is_uncoalesced and not is_integral_dtype:
# test in-place op on uncoalesced input
with self.assertRaisesRegex(RuntimeError, "in-place on uncoalesced tensors is not supported"):
sparse_tensor.log1p_()
elif self.is_uncoalesced and is_integral_dtype:
with self.assertRaisesRegex(RuntimeError, "log1p: result type cannot be Integral, got"):
sparse_tensor.log1p_()
if not is_integral_dtype:
sparse_tensor.requires_grad_()
self.assertTrue(sparse_tensor.requires_grad)
# test autograd
x = sparse_tensor.clone()
y = sparse_tensor.log1p()
with self.assertRaisesRegex(RuntimeError, "log1p of a sparse tensor is made to be non-differentiable"):
y.backward(x)
else:
with self.assertRaisesRegex(RuntimeError, "only Tensors of floating point dtype can require gradients"):
sparse_tensor.requires_grad_()
def test_log1p(self):
for dtype in torch.testing.get_all_dtypes(include_bool=False, include_half=False,
include_bfloat16=False, include_complex=False):
if not self.is_uncoalesced:
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0], [1], [2]]).transpose(1, 0),
values=torch.tensor([3.0, 4.0, 5.0]),
size=[3, ],
device=self.device,
dtype=dtype
).coalesce()
self._test_log1p_tensor(input_coalesced)
# hybrid sparse input
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[1, 3], [2, 4]]),
values=torch.tensor([[1.0, 3.0], [5.0, 7.0]]),
size=[4, 5, 2],
device=self.device,
dtype=dtype
).coalesce()
self._test_log1p_tensor(input_coalesced)
if self.is_uncoalesced:
# test uncoalesced input
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
values=torch.tensor([2.0, 3.0, 4.0, 1.0, 1.0, 1.0]),
size=[3, ],
device=self.device,
dtype=dtype
)
self._test_log1p_tensor(input_uncoalesced)
# test on empty sparse tensor
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.zeros([2, 0]),
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
device=self.device,
dtype=dtype
)
self._test_log1p_tensor(input_uncoalesced)
def _test_neg_negative(self, sparse_tensor):
dense_tensor = sparse_tensor.to_dense()
expected_output = dense_tensor.neg()
ops = (
torch.neg, torch.Tensor.neg, torch.Tensor.neg_,
torch.negative, torch.Tensor.negative, torch.Tensor.negative_,
operator.neg
)
for op in ops:
sparse_tensor_copy = sparse_tensor.clone()
self.assertEqual(expected_output, op(sparse_tensor_copy).to_dense())
if op in (torch.neg, torch.negative):
sparse_tensor_out = torch.zeros_like(sparse_tensor)
op(sparse_tensor, out=sparse_tensor_out)
self.assertEqual(expected_output, sparse_tensor_out.to_dense())
def test_neg_negative(self):
if not self.is_uncoalesced:
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0, 1, 2]]),
values=torch.tensor([3.0, -4.0, 5.0]),
size=[3, ],
device=self.device
).coalesce()
self._test_neg_negative(input_coalesced)
# hybrid sparse input
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[1, 3], [2, 4]]),
values=torch.tensor([[-1.0, 3.0], [-5.0, 7.0]]),
size=[4, 5, 2],
device=self.device
).coalesce()
self._test_neg_negative(input_coalesced)
if self.is_uncoalesced:
# test uncoalesced input
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
values=torch.tensor([2.0, -3.0, -4.0, 1.0, -1.0, 1.5]),
size=[3, ],
device=self.device
)
self._test_neg_negative(input_uncoalesced)
# test on empty sparse tensor
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.zeros([2, 0]),
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
device=self.device
)
self._test_neg_negative(input_uncoalesced)
def _test_asin_arcsin(self, sparse_tensor):
def is_integral(dtype):
return dtype in torch.testing.get_all_int_dtypes()
is_integral_dtype = is_integral(sparse_tensor.dtype)
dense_tensor = sparse_tensor.to_dense()
expected_output = dense_tensor.asin()
ops = (
torch.asin, torch.Tensor.asin,
torch.arcsin, torch.Tensor.arcsin,
)
for op in ops:
self.assertEqual(expected_output, op(sparse_tensor).to_dense())
if op in (torch.asin, torch.arcsin):
sparse_tensor_out = torch.zeros_like(sparse_tensor)
if not is_integral_dtype:
op(sparse_tensor, out=sparse_tensor_out)
self.assertEqual(expected_output, sparse_tensor_out.to_dense())
else:
with self.assertRaisesRegex(RuntimeError, "asin: result type cannot be Integral"):
op(sparse_tensor, out=sparse_tensor_out)
for op in (torch.Tensor.asin_, torch.Tensor.arcsin_):
if is_integral_dtype:
# test coalesce on integral dtype tensor
with self.assertRaisesRegex(RuntimeError, "asin: result type cannot be Integral"):
op(sparse_tensor.clone().coalesce()).to_dense()
else:
self.assertEqual(expected_output, op(sparse_tensor.clone().coalesce()).to_dense())
if self.is_uncoalesced and not is_integral_dtype:
# test in-place op on uncoalesced input
with self.assertRaisesRegex(RuntimeError, "in-place on uncoalesced tensors is not supported"):
op(sparse_tensor)
elif self.is_uncoalesced:
# test in-place op on integral dtype tensor
with self.assertRaisesRegex(RuntimeError, "asin: result type cannot be Integral"):
op(sparse_tensor)
def test_asin_arcsin(self):
for dtype in torch.testing.get_all_dtypes(include_bool=False, include_half=False,
include_bfloat16=False, include_complex=False):
if not self.is_uncoalesced:
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0, 1, 2, 3]]),
values=torch.tensor([0.5, -0.5, 0.7, -0.7]),
size=[4, ],
dtype=dtype,
device=self.device
).coalesce()
self._test_asin_arcsin(input_coalesced)
# hybrid sparse input
input_coalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[1, 3], [2, 4]]),
values=torch.tensor([[-0.1, 0.24], [-0.44, 0.1]]),
size=[4, 5, 2],
dtype=dtype,
device=self.device
).coalesce()
self._test_asin_arcsin(input_coalesced)
if self.is_uncoalesced:
# test uncoalesced input
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
values=torch.tensor([0.3, -0.3, -0.4, 0.3, -0.5, 0.15]),
size=[3, ],
dtype=dtype,
device=self.device
)
self._test_asin_arcsin(input_uncoalesced)
# test on empty sparse tensor
input_uncoalesced = torch.sparse_coo_tensor(
indices=torch.zeros([2, 0]),
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
dtype=dtype,
device=self.device
)
self._test_asin_arcsin(input_uncoalesced)
def test_mv(self):
def test_shape(di, dj, dk, nnz):
x, _, _ = self._gen_sparse(2, nnz, [di, dj])
t = torch.randn(dk, device=self.device)
res = x.matmul(t)
expected = self.safeToDense(x).matmul(t)
self.assertEqual(res, expected)
test_shape(10, 100, 100, 20)
test_shape(100, 1000, 1000, 20)
test_shape(64, 10000, 10000, 20)
test_shape(0, 100, 100, 0)
test_shape(10, 0, 0, 0)
test_shape(10, 100, 100, 0)
test_shape(10, 100, 100, 20)
with self.assertRaisesRegex(RuntimeError, r"mv: expected self\.size\(-1\) == vec\.size\(-1\)"):
test_shape(10, 100, 10, 20)
with self.assertRaisesRegex(RuntimeError, "mv: two tensor dim should be 2 and 1"):
x, _, _ = self._gen_sparse(2, 20, [10, 100])
y, _, _ = self._gen_sparse(2, 20, [10, 100])
res = x.mv(y)
def test_sparse_add_coalesce(self):
i = self.index_tensor([[1, 2, 1]])
v = self.value_tensor([3, 4, 5])
x = self.sparse_tensor(i, v, torch.Size([3]))
y = self.sparse_tensor(i, v, torch.Size([3]))
z = x + y
self.assertFalse(z._indices().numel() != 2 and z.is_coalesced())
i = self.index_tensor([[1, 2, 1]])
v = self.value_empty(3, 0)
x = self.sparse_tensor(i, v, torch.Size([3, 0]))
y = self.sparse_tensor(i, v, torch.Size([3, 0]))
z = x + y
self.assertFalse(z._indices().numel() != 2 and z.is_coalesced())
@cuda_only
def test_storage_not_null(self):
x = torch.cuda.sparse.FloatTensor(2)
self.assertNotEqual(x.get_device(), -1)
x = torch.cuda.sparse.FloatTensor(2, 0)
self.assertNotEqual(x.get_device(), -1)
@cuda_only
@unittest.skipIf(torch.cuda.device_count() < 2, "only one GPU detected")
def test_same_gpu(self):
def check_device(x, device_id):
self.assertEqual(x.get_device(), device_id)
self.assertEqual(x._values().get_device(), device_id)
self.assertEqual(x._indices().get_device(), device_id)
i = self.index_tensor([[2]]).cuda(1)
v = self.value_tensor([5]).cuda(1)
x = self.sparse_tensor(i, v, torch.Size([3]), device=1)
check_device(x, 1)
i = self.index_tensor([[2]]).cuda(1)
v = self.value_empty(1, 0).cuda(1)
x = self.sparse_tensor(i, v, torch.Size([3, 0]), device=1)
check_device(x, 1)
x = self.sparse_empty(3, device=1)
check_device(x, 1)
x = self.sparse_empty(3, 0, device=1)
check_device(x, 1)
i = self.index_tensor([[2]]).cuda(1)
v = self.value_tensor([5]).cuda(0)
# NB: non-legacy constructor allows this and moves indices
self.assertRaises(RuntimeError, lambda: self.legacy_sparse_tensor(i, v, torch.Size([3])))
i = self.index_tensor([[2]]).cuda(1)
v = self.value_empty(1, 0).cuda(0)
# NB: non-legacy constructor allows this and moves indices
self.assertRaises(RuntimeError, lambda: self.legacy_sparse_tensor(i, v, torch.Size([3, 0])))
def _test_new_device(self, size, device):
with torch.cuda.device(device):
x = torch.cuda.sparse.DoubleTensor(*size)
self.assertEqual(x.get_device(), device)
x1 = x.new()
x2 = x.new(2, 3)
self.assertEqual(x1.get_device(), device)
self.assertEqual(x2.get_device(), device)
@cuda_only
def test_new_device_single_gpu(self):
self._test_new_device((), 0)
self._test_new_device((30, 20), 0)
self._test_new_device((30, 20, 10), 0)
self._test_new_device((30, 20, 10, 0), 0)
@cuda_only
@unittest.skipIf(torch.cuda.device_count() < 2, "only one GPU detected")
def test_new_device_multi_gpu(self):
self._test_new_device((), 1)
self._test_new_device((30, 20), 1)
self._test_new_device((30, 20, 10), 1)
self._test_new_device((30, 20, 10, 0), 1)
def test_new(self):
def test_shape(sparse_dims, nnz, with_size):
x, indices, values = self._gen_sparse(sparse_dims, nnz, with_size)
if not x.is_cuda:
# CUDA sparse tensors currently requires the size to be
# specified if nDimV > 0
out = x.new(indices, values).coalesce()
x_c = x.coalesce()
self.assertEqual((out.indices(), out.values()), (x_c.indices(), x_c.values()))
self.assertEqual(x.new(indices, values, x.size()), x)
test_shape(3, 10, 100)
test_shape(3, 0, [100, 100, 0])
@cpu_only # not really, but we only really want to run this once
def test_factory(self):
for test_empty_tensor in [True, False]:
if test_empty_tensor:
default_size = torch.Size([1, 3, 0])
size = torch.Size([3, 3, 0])
else:
default_size = torch.Size([1, 3])
size = torch.Size([3, 3])
for include_size in [True, False]:
for use_tensor_idx in [True, False]:
for use_tensor_val in [True, False]:
for use_cuda in ([False] if not torch.cuda.is_available() else [True, False]):
for dtype in [torch.float64, torch.float16]:
# have to include size with cuda sparse tensors
include_size = include_size or use_cuda
long_dtype = torch.int64
device = torch.device('cpu') if not use_cuda else \
torch.device(torch.cuda.device_count() - 1)
indices = torch.tensor(([0], [2]), dtype=long_dtype) if use_tensor_idx else ([0], [2])
if test_empty_tensor:
values = self.value_empty(1, 0).to(dtype)
else:
if use_tensor_val:
values = torch.tensor([1.], dtype=dtype)
else:
values = 1.
if include_size:
sparse_tensor = torch.sparse_coo_tensor(indices, values, size, dtype=dtype,
device=device, requires_grad=True)
else:
sparse_tensor = torch.sparse_coo_tensor(indices, values, dtype=dtype,
device=device, requires_grad=True)
self.assertEqual(indices, sparse_tensor._indices())
self.assertEqual(values, sparse_tensor._values())
self.assertEqual(size if include_size else default_size, sparse_tensor.size())
self.assertEqual(dtype, sparse_tensor.dtype)
if use_cuda:
self.assertEqual(device, sparse_tensor._values().device)
self.assertEqual(True, sparse_tensor.requires_grad)
def test_factory_size_check(self):
indices = self.index_tensor([[1, 2],
[0, 2]])
values = self.value_tensor([.5, .5])
sizes = torch.Size([2, 3])
with self.assertRaisesRegex(RuntimeError, "size is inconsistent with indices"):
torch.sparse_coo_tensor(indices, values, sizes)
indices.fill_(-1)
with self.assertRaisesRegex(RuntimeError, "found negative index"):
torch.sparse_coo_tensor(indices, values, sizes)
indices = self.index_tensor([[1, 2],
[0, 2]])
values = self.value_empty(2, 1, 0)
sizes = torch.Size([2, 3, 1, 0])
with self.assertRaisesRegex(RuntimeError, "size is inconsistent with indices"):
torch.sparse_coo_tensor(indices, values, sizes)
indices = self.index_tensor([[1, 2],
[0, 2]])
values = self.value_empty(2, 2, 2)
sizes = torch.Size([0, 0, 2, 2])
with self.assertRaisesRegex(RuntimeError, "size is inconsistent with indices"):
torch.sparse_coo_tensor(indices, values, sizes)
indices = self.index_tensor([[1, 2],
[0, 2]])
values = self.value_tensor([[1, 1, 1], [1, 1, 1]])
sizes = torch.Size([3, 3, 2])
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
torch.sparse_coo_tensor(indices, values, sizes)
indices = self.index_tensor([[1, 2],
[0, 2]])
values = self.value_empty(2, 1, 0)
sizes = torch.Size([3, 3, 2, 0])
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
torch.sparse_coo_tensor(indices, values, sizes)
def test_factory_default(self):
tensor = self.legacy_sparse_tensor()
expected_indices = self.index_tensor([[]])
expected_size = torch.Size([0])
self.assertEqual(tensor._indices(), expected_indices)
self.assertEqual(tensor.shape, expected_size)
def test_factory_empty_indices(self):
device = 'cuda' if self.is_cuda else 'cpu'
tensor = self.legacy_sparse_tensor()
expected_indices = torch.empty((1, 0), dtype=torch.long, device=device)
self.assertEqual(tensor._indices(), expected_indices)
tensor = torch.sparse_coo_tensor(torch.Size([2, 0]), device=device)
expected_indices = torch.empty((2, 0), dtype=torch.long, device=device)
self.assertEqual(tensor._indices(), expected_indices)
tensor = torch.sparse_coo_tensor(torch.Size([2, 2, 0]), device=device)
expected_indices = torch.empty((3, 0), dtype=torch.long, device=device)
self.assertEqual(tensor._indices(), expected_indices)
tensor = torch.sparse_coo_tensor(torch.Size([2, 2, 0, 0]), device=device)
expected_indices = torch.empty((4, 0), dtype=torch.long, device=device)
self.assertEqual(tensor._indices(), expected_indices)
def test_factory_nnz(self):
indices = self.index_tensor([[0]]) # (sparse_dim, nnz): (1, 1)
values = self.value_tensor([[1, 1], [1, 1]]) # (nnz, ...): (2, 2)
sizes = torch.Size([2, 2])
with self.assertRaisesRegex(RuntimeError, "indices and values must have same nnz"):
torch.sparse_coo_tensor(indices, values, sizes)
indices = self.index_tensor([[0]]) # (sparse_dim, nnz): (1, 1)
values = self.value_empty(2, 0) # (nnz, ...): (2, 0)
sizes = torch.Size([2, 0])
with self.assertRaisesRegex(RuntimeError, "indices and values must have same nnz"):
torch.sparse_coo_tensor(indices, values, sizes)
def test_factory_nnz_zero(self):
def test_shape(i_shape, v_shape, size, expected_size):
device = 'cuda' if self.is_cuda else 'cpu'
if size:
t = torch.sparse_coo_tensor(torch.empty(i_shape), torch.empty(v_shape), torch.Size(size), device=device)
else:
t = torch.sparse_coo_tensor(torch.empty(i_shape), torch.empty(v_shape), device=device)
expected_indices = torch.empty(i_shape, device=device, dtype=torch.int64)
expected_values = torch.empty(v_shape, device=device, dtype=torch.float64)
expected_size = torch.Size(expected_size)
self.assertEqual(t._indices(), expected_indices)
self.assertEqual(t._values(), expected_values)
self.assertEqual(t.size(), expected_size)
test_shape([1, 0], [0, 2, 4, 0], None, [0, 2, 4, 0])
test_shape([3, 0], [0, 2, 4, 0], None, [0, 0, 0, 2, 4, 0])
test_shape([1, 0], [0, 2, 4, 0], [0, 2, 4, 0], [0, 2, 4, 0])
test_shape([3, 0], [0, 2, 4, 0], [0, 0, 0, 2, 4, 0], [0, 0, 0, 2, 4, 0])
test_shape([3, 0], [0, 2, 4, 0], [1, 2, 3, 2, 4, 0], [1, 2, 3, 2, 4, 0])
def test_factory_dense_dim(self):
indices = self.index_tensor([[0]])
values = self.value_tensor([[[1, 1, 1], [1, 1, 1]]])
sizes = torch.Size([1, 3, 4])
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
torch.sparse_coo_tensor(indices, values, sizes)
indices = self.index_tensor([[0]])
values = self.value_empty(1, 2, 3, 0)
sizes = torch.Size([1, 3, 4, 0])
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
torch.sparse_coo_tensor(indices, values, sizes)
@cpu_only
def test_factory_type_inference(self):
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.tensor([1.], dtype=torch.float16))
self.assertEqual(torch.float16, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.tensor([1.], dtype=torch.float32))
self.assertEqual(torch.float32, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.tensor([1.], dtype=torch.float64))
self.assertEqual(torch.float64, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.tensor([1]))
self.assertEqual(torch.int64, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.HalfTensor(1, 0))
self.assertEqual(torch.float16, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.FloatTensor(1, 0))
self.assertEqual(torch.float32, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.DoubleTensor(1, 0))
self.assertEqual(torch.float64, t.dtype)
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.LongTensor(1, 0))
self.assertEqual(torch.int64, t.dtype)
@cuda_only
def test_factory_device_type_inference(self):
# both indices/values are CUDA
cpu_cuda = ('cpu', 'cuda')
cpu_cuda_none = cpu_cuda + (None,)
for indices_device, values_device, device in itertools.product(cpu_cuda,
cpu_cuda,
cpu_cuda_none):
indices = torch.tensor(([0], [2]), device=indices_device)
values = torch.tensor([1.], device=values_device)
empty_values = self.value_empty(1, 0).to(values_device)
shape = (1, 3)
empty_shape = (1, 3, 0)
if device is None and indices_device != values_device:
with self.assertRaises(RuntimeError):
torch.sparse_coo_tensor(indices, values, shape, device=device)
with self.assertRaises(RuntimeError):
torch.sparse_coo_tensor(indices, empty_values, empty_shape, device=device)
else:
t = torch.sparse_coo_tensor(indices, values, shape, device=device)
t_empty = torch.sparse_coo_tensor(indices, empty_values, empty_shape, device=device)
should_be_cuda = (device == 'cuda' or (device is None and values_device == 'cuda'))
self.assertEqual(should_be_cuda, t.is_cuda)
self.assertEqual(t.is_cuda, t_empty.is_cuda)
@cpu_only
def test_factory_copy(self):
def test_tensor(indices, values, indices_equal, values_equal):
sparse_tensor = torch.sparse_coo_tensor(indices, values, dtype=torch.float64)
if indices_equal:
self.assertEqual(indices.data_ptr(), sparse_tensor._indices().data_ptr())
else:
self.assertNotEqual(indices.data_ptr(), sparse_tensor._indices().data_ptr())
if values_equal:
self.assertEqual(values.data_ptr(), sparse_tensor._values().data_ptr())
else:
self.assertNotEqual(values.data_ptr(), sparse_tensor._values().data_ptr())
# both correct
indices = torch.tensor(([0], [2]), dtype=torch.int64)
values = torch.tensor([1.], dtype=torch.float64)
test_tensor(indices, values, True, True)
indices = torch.tensor(([0], [2]), dtype=torch.int64)
values = torch.DoubleTensor(1, 0)
test_tensor(indices, values, True, True)
# only indices correct
indices = torch.tensor(([0], [2]), dtype=torch.int64)
values = torch.tensor([1.], dtype=torch.float32)
test_tensor(indices, values, True, False)
indices = torch.tensor(([0], [2]), dtype=torch.int64)
values = torch.tensor([1.], dtype=torch.float16)
test_tensor(indices, values, True, False)
indices = torch.tensor(([0], [2]), dtype=torch.int64)
values = torch.FloatTensor(1, 0)
test_tensor(indices, values, True, True) # An empty tensor's data_ptr is always equal to 0
# only values correct
indices = torch.tensor(([0], [2]), dtype=torch.int32)
values = torch.tensor([1.], dtype=torch.float64)
test_tensor(indices, values, False, True)
indices = torch.tensor(([0], [2]), dtype=torch.int32)
values = torch.DoubleTensor(1, 0)
test_tensor(indices, values, False, True)
# neither correct
indices = torch.tensor(([0], [2]), dtype=torch.int32)
values = torch.tensor([1.], dtype=torch.float32)
test_tensor(indices, values, False, False)
indices = torch.tensor(([0], [2]), dtype=torch.int32)
values = torch.FloatTensor(1, 0)
test_tensor(indices, values, False, True) # An empty tensor's data_ptr is always equal to 0
@cpu_only # just run once, we test both cpu and cuda
def test_constructor_device_legacy(self):
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
v = torch.tensor([3., 4., 5.])
size = torch.Size([2, 3])
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(i, v, device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(i, v, size, device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(torch.Size([2, 3, 4]), device='cuda'))
x = torch.sparse_coo_tensor(i, v, size, device='cpu')
self.assertRaises(RuntimeError, lambda: x.new(device='cuda'))
self.assertRaises(RuntimeError, lambda: x.new(i, v, device='cuda'))
self.assertRaises(RuntimeError, lambda: x.new(i, v, size, device='cuda'))
self.assertRaises(RuntimeError, lambda: x.new(torch.Size([2, 3, 4]), device='cuda'))
if torch.cuda.is_available():
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(i, v, device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(i, v, size, device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(torch.Size([2, 3, 4]), device='cpu'))
x = torch.sparse_coo_tensor(i, v, size, device='cuda')
self.assertRaises(RuntimeError, lambda: x.new(device='cpu'))
self.assertRaises(RuntimeError, lambda: x.new(i, v, device='cpu'))
self.assertRaises(RuntimeError, lambda: x.new(i, v, size, device='cpu'))
self.assertRaises(RuntimeError, lambda: x.new(torch.Size([2, 3, 4]), device='cpu'))
def test_legacy_constructor(self):
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
v = torch.tensor([3., 4., 5.])
size = torch.Size([2, 3])
self.assertRaises(TypeError, lambda: torch.sparse.FloatTensor(v.storage()))
self.assertRaises(TypeError, lambda: torch.sparse.FloatTensor(v))
self.assertEqual(torch.sparse_coo, torch.sparse.FloatTensor(torch.Size([2, 3])).layout)
self.assertRaises(TypeError, lambda: torch.sparse.FloatTensor([6]))
def test_legacy_new(self):
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
v = torch.tensor([3., 4., 5.])
size = torch.Size([2, 3])
s = torch.sparse_coo_tensor(i, v, size)
self.assertEqual(torch.sparse_coo, s.new(device='cpu').layout)
self.assertRaises(TypeError, lambda: s.new(v.storage()))
self.assertRaises(TypeError, lambda: s.new(v))
self.assertEqual(torch.sparse_coo, s.new(torch.Size([2, 3])).layout)
self.assertRaises(TypeError, lambda: s.new([6]))
@cpu_only # not really, but we only really want to run this once
def test_dtypes(self):
all_sparse_dtypes = torch.testing.get_all_dtypes()
do_test_dtypes(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cpu'))
if torch.cuda.is_available():
do_test_dtypes(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cuda:0'))
@cpu_only # not really, but we only really want to run this once
def test_empty_full(self):
all_sparse_dtypes = torch.testing.get_all_dtypes()
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cpu'))
if torch.cuda.device_count() > 0:
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, None)
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cuda:0'))
def test_is_sparse(self):
x = torch.randn(3, 3)
self.assertFalse(x.is_sparse)
x = torch.randn(3, 3, 0)
self.assertFalse(x.is_sparse)
x = self.legacy_sparse_tensor()
self.assertTrue(x.is_sparse)
x = self.sparse_empty(1, 0)
self.assertTrue(x.is_sparse)
def test_resize_as(self):
def do_test(t):
y = t.new().resize_as_(t).zero_()
self.assertEqual(y.shape, t.shape)
# Check that y can be added to t. Currently, this requires that
# sparse_dim and dense_dim match.
self.assertEqual(t, t + y)
do_test(self.legacy_sparse_tensor())
do_test(self.sparse_empty(3, 0))
do_test(self.sparse_empty(3, 3))
def _test_resize_shape(self, x_i, x_v, x_size, y_i, y_v, y_size):
x_v_numel = torch.zeros(x_v).numel()
y_v_numel = torch.zeros(y_v).numel()
x = torch.sparse_coo_tensor(torch.zeros(x_i),
torch.arange(x_v_numel).resize_(x_v).to(torch.float),
torch.Size(x_size))
x_dense = x.to_dense()
y = torch.sparse_coo_tensor(torch.zeros(y_i),
torch.ones(y_v).to(torch.float),
torch.Size(y_size))
y_dense = y.to_dense()
x.resize_as_(y)
x_dense.resize_as_(y_dense)
self.assertEqual(x.shape, y.shape)
self.assertEqual(x.sparse_dim(), y.sparse_dim())
self.assertEqual(x.dense_dim(), y.dense_dim())
self.assertEqual(x.shape, x_dense.shape)
self.assertEqual(y.shape, y_dense.shape)
# Here we make sure that the original data are preserved after resizing
self.assertEqual(x.to_dense().view(-1)[0:x_v_numel].view(x_v),
x_dense.view(-1)[0:x_v_numel].view(x_v))
def test_resize(self):
# 1. Expand the size of some dense dimensions [Supported]
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 4], [2, 2, 4])
self._test_resize_shape([1, 1], [1, 2, 0], [2, 2, 0],
[1, 1], [1, 2, 4], [2, 2, 4])
# 2. Expand the size of some sparse dimensions [Supported]
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 3], [4, 2, 3])
# 3. Change the shapes of both sparse and dense dimensions when nnz is zero [Supported]
self._test_resize_shape([1, 0], [0, 2, 3], [2, 2, 3],
[2, 0], [0, 2, 4, 5], [1, 1, 2, 4, 5])
self._test_resize_shape([1, 0], [0, 2, 3], [2, 2, 3],
[2, 0], [0, 2, 4, 0], [1, 1, 2, 4, 0])
# 4. Add dims to dense dimensions [Not Supported]
with self.assertRaisesRegex(RuntimeError, "changing the number of dense dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 3, 4], [2, 2, 3, 4])
with self.assertRaisesRegex(RuntimeError, "changing the number of dense dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 3, 0], [2, 2, 3, 0])
# 5. Remove dims from dense dimensions [Not Supported]
with self.assertRaisesRegex(RuntimeError, "changing the number of dense dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2], [2, 2])
# 6. Change the number of sparse dimensions on a non-empty sparse tensor [Not Supported]
with self.assertRaisesRegex(RuntimeError, "changing the number of sparse dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[2, 1], [1, 2, 3], [1, 2, 2, 3])
# 7. Shrink the size of some sparse dimensions on a non-empty sparse tensor [Not Supported]
with self.assertRaisesRegex(RuntimeError, "shrinking the size of sparse dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 3], [1, 2, 3])
# 8. Shrink the size of some dense dimensions on a non-empty sparse tensor [Not Supported]
with self.assertRaisesRegex(RuntimeError, "shrinking the size of dense dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 2], [2, 2, 2])
with self.assertRaisesRegex(RuntimeError, "shrinking the size of dense dimensions"):
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
[1, 1], [1, 2, 0], [2, 2, 0])
def test_is_nonzero(self):
self.assertTrue(torch.sparse_coo_tensor(([0],), 1., (1,)).is_nonzero())
self.assertFalse(torch.sparse_coo_tensor(([0],), 0., (1,)).is_nonzero())
self.assertFalse(torch.sparse_coo_tensor(([0], [0]), 0., (1, 1)).is_nonzero())
self.assertFalse(torch.sparse_coo_tensor(([0, 0],), (0., 0.), (1,)).is_nonzero())
self.assertFalse(torch.sparse_coo_tensor(([0, 0],), (-1., 1.), (1,)).is_nonzero())
self.assertTrue(torch.sparse_coo_tensor(torch.zeros(0, 1), 12.3, []).is_nonzero()) # scalar sparse tensor
with self.assertRaisesRegex(RuntimeError, "Boolean value of Tensor with no values is ambiguous"):
torch.sparse_coo_tensor(([0, 1],), self.value_empty(2, 0), (4, 0)).is_nonzero()
def test_allow_tensor_metadata_change(self):
def do_test(t):
with self.assertRaisesRegex(
RuntimeError,
"raw_resize_ is not allowed on a Tensor created from .data or .detach()"):
t.transpose_(0, 1)
with self.assertRaisesRegex(
RuntimeError,
"resize_ is not allowed on a Tensor created from .data or .detach()"):
t.resize_as_(self.sparse_empty(3, 3))
with self.assertRaisesRegex(
RuntimeError,
"resize_and_clear_ is not allowed on a Tensor created from .data or .detach()"):
t.mul_(t)
with self.assertRaisesRegex(
RuntimeError,
"set_coalesced is not allowed on a Tensor created from .data or .detach()"):
t._coalesced_(True)
with self.assertRaisesRegex(
RuntimeError,
"set_indices_and_values_unsafe is not allowed on a Tensor created from .data or .detach()"):
a = self.sparse_tensor(torch.tensor([[0, 1, 1], [2, 0, 2]]), torch.tensor([3., 4., 5.])).data
a.add_(a)
with self.assertRaisesRegex(
RuntimeError,
"resize_and_clear_ is not allowed on a Tensor created from .data or .detach()"):
a.zero_()
with self.assertRaisesRegex(
RuntimeError,
"resize_ is not allowed on a Tensor created from .data or .detach()"):
a.copy_(self.sparse_empty(3, 3))
do_test(self.sparse_empty(3, 0).data)
do_test(self.sparse_empty(3, 0).detach())
def test_change_tensor_metadata(self):
i = self.index_tensor([[0], [1]])
v = self.value_tensor([[3, 4, 5]])
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
i.resize_(2, 3)
v.resize_(4, 5)
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
i = self.index_tensor([[0], [1]])
v = self.value_tensor([[3, 4, 5]])
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
i.resize_as_(self.index_tensor([0, 1]))
v.resize_as_(self.value_tensor([3, 4, 5]))
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
i = self.index_tensor([[0], [1]])
v = self.value_tensor([[3, 4, 5]])
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
i.as_strided_((2, 1), (1, 1))
v.as_strided_((1, 3), (1, 1))
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
i = self.index_tensor([[0], [1]])
v = self.value_tensor([[3, 4, 5]])
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
i.set_(self.index_tensor([0, 1]))
v.set_(self.value_tensor([3, 4, 5]))
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
i = self.index_tensor([[0], [1]])
v = self.value_tensor([[3, 4, 5]])
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
i.transpose_(0, 1)
v.transpose_(0, 1)
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
def test_pickle(self):
import pickle
shape_sparse_dim_nnz = [
((), 0, 2),
((0,), 0, 10),
((2,), 0, 3),
((100, 3), 1, 3),
((100, 20, 3), 2, 0),
((10, 0, 3), 0, 3),
((10, 0, 3), 0, 0),
]
for shape, sparse_dim, nnz in shape_sparse_dim_nnz:
indices_shape = torch.Size((sparse_dim, nnz))
values_shape = torch.Size((nnz,) + shape[sparse_dim:])
indices = torch.arange(indices_shape.numel(), dtype=self.index_tensor(0).dtype,
device=self.device).view(indices_shape)
for d in range(sparse_dim):
indices[d].clamp_(max=(shape[d] - 1)) # make it valid index
if self.is_uncoalesced and indices.numel() > 0:
indices[:, -1] = indices[:, 0] # make it uncoalesced
values_numel = values_shape.numel()
values = torch.arange(values_numel, dtype=self.value_dtype,
device=self.device).view(values_shape).div_(values_numel / 2.)
sp_tensor = self.sparse_tensor(indices, values, shape)
serialized = pickle.dumps(sp_tensor)
sp_tensor_loaded = pickle.loads(serialized)
self.assertEqual(sp_tensor, sp_tensor_loaded)
def test_any(self):
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([False, False]))
t_any = torch.tensor(False)
self.assertEqual(torch.any(t), t_any)
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([True, False]))
t_any = torch.tensor(True)
self.assertEqual(torch.any(t), t_any)
def test_isnan(self):
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([1, 4]))
t_nan = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([False, False]))
self.assertEqual(torch.isnan(t).int(), t_nan.int())
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([1, float("nan")]))
t_nan = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([False, True]))
self.assertEqual(torch.isnan(t).int(), t_nan.int())
def test_div_by_sparse_error(self):
self.assertRaisesRegex(RuntimeError, 'Sparse division requires',
lambda: torch.tensor(1., device=self.device).to_sparse()
/ torch.tensor(1., device=self.device).to_sparse())
def test_floor_divide_by_sparse_error(self):
self.assertRaisesRegex(RuntimeError, 'Sparse floor division requires',
lambda: torch.tensor(1., device=self.device).to_sparse()
// torch.tensor(1., device=self.device).to_sparse())
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_sparse_to_numpy(self):
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([1, 4]))
self.assertRaises(TypeError, lambda: t.numpy())
@skipIfRocm
def test_softmax(self):
import torch.nn.functional as F
def to_dense(sparse, fill_value=None):
"""
Return dense tensor from a sparse tensor using given fill value.
"""
if fill_value is None or fill_value == 0:
return sparse.to_dense()
sparse = sparse.coalesce()
dense = torch.full(sparse.shape, fill_value, dtype=sparse.dtype, device=sparse.device)
for idx, value in zip(sparse._indices().t(), sparse._values()):
dense[tuple(idx)] = value
return dense
def softmax_to_dense(sparse, dim):
"""Dense softmax of a sparse tensor. Useful only for testing softmax
correctness.
When computing softmax of a sparse tensor, the value of
unspecified items is negative infinity rather than zero so
that
softmax(sparse.to_dense(fill_value=-inf), dim) == softmax(sparse, dim).to_dense()
holds for non-empty lines. One empty lines, the softmax
values are defined as 0 in order to preserve the sparsity
of result.
Note that in PyTorch, ``to_dense`` method does not
implement the ``fill_value`` keyword argument.
"""
dtype = sparse.dtype
device = sparse.device
dense = to_dense(sparse, fill_value=-float('inf'))
r = F.softmax(dense, dim)
# softmax on empty lines results nan, replace with zeros to match the definition
r[r != r] = 0
return r
def sparse_softmax(sparse, dim):
"""Pure Python softmax of a sparse tensor. Assuming -inf for
unspecified sparse tensor data. This is a prototype of
sparse softmax algorithm in Python.
"""
dtype = sparse.dtype
device = sparse.device
# softmax is non-linear operation, so sparse tensors must
# be coalesced.
sparse = sparse.coalesce()
inf = float('inf')
indices = sparse._indices()
values = sparse._values()
if dim < sparse.sparse_dim():
nnz = sparse._nnz()
# compute pool indices
size = sparse.size()
strides = torch.ones((sparse.sparse_dim(), 1), dtype=indices.dtype, device=indices.device)
for i in reversed(range(sparse.sparse_dim() - 1)):
strides[i, 0] = strides[i + 1, 0] * size[i + 1]
strides[dim, 0] = 0
pool = (indices * strides).sum(dim=0)
i2p = {}
for i in range(nnz):
c = int(pool[i])
if c not in i2p:
i2p[c] = len(i2p)
pool[i] = i2p[c]
# compute max
dense_size = tuple(size[sparse.sparse_dim():])
mx = torch.empty((pool.max() + 1,) + dense_size, dtype=dtype, device=device)
mx[:] = -inf
for n in range(nnz):
p = pool[n]
mx[p] = torch.max(mx[p], values[n])
# apply exp to (v - mx) and sum the results
exp_values = torch.empty_like(values)
exp_sums = torch.zeros_like(mx)
for n in range(nnz):
p = pool[n]
v = exp_values[n] = (values[n] - mx[p]).exp()
exp_sums[p] = exp_sums[p] + v
# normalize with the sum of exponents
for n in range(nnz):
p = pool[n]
exp_values[n] = exp_values[n] / exp_sums[p]
return torch.sparse_coo_tensor(indices,
exp_values,
sparse.size(),
dtype=dtype, device=device)
elif dim < sparse.sparse_dim() + sparse.dense_dim():
return torch.sparse_coo_tensor(indices,
F.softmax(values, dim - sparse.sparse_dim() + 1),
sparse.size(),
dtype=dtype, device=device)
else:
raise ValueError(
'`dim(=%s)` must be smaller than `sparse_dim(=%s) + dense_dim(=%s)`'
% (dim, sparse.sparse_dim(), sparse.dense_dim()))
def softmax_jacobian_analytic(x, dim):
"""Return Jacobian of softmax using analytic formula
D_jS_i = S_i * (1[i==j] - S_j).
where S = softmax(x, dim), x is dense tensor, i,j in
range(x.shape[dim]).
"""
y = F.softmax(x, dim)
y[y != y] = 0 # replace nan-s with zeros
J = torch.zeros((x.shape[dim],) + tuple(x.shape), dtype=x.dtype, device=x.device)
si = [slice(None)] * len(y.shape)
sj = [slice(None)] * len(y.shape)
s = [slice(None)] * len(J.shape)
for i in range(y.shape[dim]):
si[dim] = i
s[dim + 1] = i
yi = y[tuple(si)]
for j in range(y.shape[dim]):
sj[dim] = j
s[0] = j
if i == j:
J[tuple(s)] = yi * (1 - yi)
else:
yj = y[tuple(sj)]
J[tuple(s)] = - yi * yj
sj[dim] = slice(None)
si[dim] = slice(None)
s[dim + 1] = slice(None)
return J
def softmax_jacobian_autograd(x, dim, log=False):
"""Return Jacobian of softmax using PyTorch autograd feature.
x can be dense or sparse tensor.
"""
import itertools
if x.is_sparse:
x = x.coalesce()
dtype = x.dtype
device = x.device
shape = tuple(x.shape)
J = torch.zeros((shape[dim],) + shape, dtype=dtype, device=device)
for i in range(shape[dim]):
if x.is_sparse:
sparse_dim = x.sparse_dim()
dense_dim = x.dense_dim()
if dim < sparse_dim:
ranges = []
for j, sz in enumerate(shape[:sparse_dim]):
if dim == j:
ranges.append([i])
else:
ranges.append(list(range(sz)))
indices = torch.tensor(list(itertools.product(*ranges)), dtype=torch.long, device=device).t()
values = torch.ones((indices.shape[1],) + shape[sparse_dim:], dtype=dtype, device=device)
else:
ranges = []
for j, sz in enumerate(shape[:sparse_dim]):
ranges.append(list(range(sz)))
indices = torch.tensor(list(itertools.product(*ranges)), dtype=torch.long, device=device).t()
values = torch.zeros((indices.shape[1],) + shape[sparse_dim:], dtype=dtype, device=device)
sv = [slice(None)] * (dense_dim + 1)
sv[dim - sparse_dim + 1] = i
values[tuple(sv)] = 1
v = torch.sparse_coo_tensor(indices, values, shape, dtype=dtype, device=device)
else:
v = torch.zeros_like(x)
sv = [slice(None)] * len(v.shape)
sv[dim] = i
v[tuple(sv)] = 1
x_ = x.clone()
x_.requires_grad_(True)
if log:
if x_.is_sparse:
y = torch.sparse.log_softmax(x_, dim)
else:
y = F.log_softmax(x_, dim)
else:
if x_.is_sparse:
y = torch.sparse.softmax(x_, dim)
else:
y = F.softmax(x_, dim)
# replace nan-s with zeros
y.data[y != y] = 0
y.backward(v)
g = x_.grad
if not g.is_sparse:
# replace nan-s with zeros
g.data[g != g] = 0
J[i] = g.to_dense() if g.is_sparse else g
return J
def test_op(sparse_dims, nnz, with_size):
if isinstance(with_size, Number):
with_size = [with_size] * sparse_dims
x, i, v = self._gen_sparse(sparse_dims, nnz, with_size)
def sparse_log(x):
return torch.sparse_coo_tensor(x._indices(), x._values().log(),
x.size(), dtype=x.dtype, device=x.device)
for dim in range(x.sparse_dim() + x.dense_dim()):
# Check sparse softmax definition
# check Python sparse softmax
y = sparse_softmax(x, dim)
r1 = softmax_to_dense(x, dim)
r2 = y.to_dense()
self.assertEqual(r1, r2)
# check C++ sparse softmax
y1 = torch.sparse.softmax(x, dim)
self.assertEqual(y, y1)
# check C++ sparse log_softmax
ly1 = torch.sparse.log_softmax(x, dim)
self.assertEqual(ly1, sparse_log(y1))
# Check autograd support on sparse softmax
# check softmax Jacobian definition for dense input
x1 = to_dense(x, fill_value=float('-inf'))
J = softmax_jacobian_analytic(x1, dim)
assert J.shape[0] == x.shape[dim]
assert J.shape[dim + 1] == x.shape[dim]
# check softmax Jacobian from autograd, dense input
J2 = softmax_jacobian_autograd(x1, dim)
self.assertEqual(J, J2)
# check softmax Jacobian from autograd, sparse input
J3 = softmax_jacobian_autograd(x, dim)
self.assertEqual(J, J3)
'''
y = softmax(x, dim)
z = log(y) = log_softmax(x, dim)
Dy/Dx = J
Dz/Dx = Dz/Dy Dy/Dx = 1/y * J
=> J = J_log * y
'''
# log_softmax Jacobian from autograd, dense input
J2_log = softmax_jacobian_autograd(x1, dim, log=True)
# log_softmax Jacobian from autograd, sparse input
J3_log = softmax_jacobian_autograd(x, dim, log=True)
J = J.transpose(0, dim + 1)
J2_log = J2_log.transpose(0, dim + 1)
J3_log = J3_log.transpose(0, dim + 1)
self.assertEqual(J, J2_log * r1)
self.assertEqual(J, J3_log * r1)
if dim == 0:
# check dtype argument
other_dtype = torch.float32
y2 = torch.sparse.softmax(x, dim, dtype=other_dtype)
self.assertEqual(y2.dtype, other_dtype)
self.assertEqual(y2, y1.type(other_dtype))
ly2 = torch.sparse.log_softmax(x, dim, dtype=other_dtype)
self.assertEqual(ly2.dtype, other_dtype)
self.assertEqual(ly2, ly1.type(other_dtype))
test_op(1, 10, [3])
test_op(1, 10, [2, 3])
test_op(1, 10, [3, 2])
test_op(2, 10, [2, 3, 4])
test_op(2, 10, [3, 4])
test_op(2, 5, [5, 4])
test_op(2, 10, [3, 4, 2])
test_op(3, 10, [3, 4, 2])
test_op(3, 100, [3, 4, 2])
test_op(3, 100, [3, 4, 2, 3])
test_op(3, 100, [3, 4, 2, 3, 5, 2])
test_op(4, 100, [3, 4, 2, 3, 5, 2])
class TestUncoalescedSparse(TestSparse):
def setUp(self):
super(TestUncoalescedSparse, self).setUp()
self.is_uncoalesced = True
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
class TestCudaSparse(TestSparse):
def setUp(self):
super(TestCudaSparse, self).setUp()
self.is_cuda = True
self.device = 'cuda'
self.legacy_sparse_tensor = torch.cuda.sparse.DoubleTensor
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
class TestCudaUncoalescedSparse(TestCudaSparse):
def setUp(self):
super(TestCudaUncoalescedSparse, self).setUp()
self.is_uncoalesced = True
class TestSparseOneOff(TestCase):
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_cuda_from_cpu(self):
with self.assertRaisesRegex(
RuntimeError,
"backend of indices \\(CUDA\\) must match backend of values \\(CPU\\)"):
torch.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
torch.randn(4, 4, 4),
[3, 4, 4])
with self.assertRaisesRegex(
RuntimeError,
"backend of indices \\(CUDA\\) must match backend of values \\(CPU\\)"):
torch.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
torch.randn(4, 4, 4, 0),
[3, 4, 4, 0])
with self.assertRaisesRegex(
RuntimeError,
"backend of indices \\(CUDA\\) must match backend of values \\(CPU\\)"):
torch.sparse.FloatTensor(torch.LongTensor(1, 0).cuda(),
torch.randn(0, 4, 4, 0),
[0, 4, 4, 0])
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_cuda_sparse_cpu_dense_add(self):
x = torch.zeros(3, 4, 4)
sparse_y = torch.cuda.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
torch.randn(4, 4, 4).cuda(),
[3, 4, 4])
with self.assertRaisesRegex(RuntimeError, "add: expected 'self' to be a CUDA tensor, but got a CPU tensor"):
x + sparse_y
x = torch.zeros(3, 4, 4, 0)
sparse_y = torch.cuda.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
torch.randn(4, 4, 4, 0).cuda(),
[3, 4, 4, 0])
with self.assertRaisesRegex(RuntimeError, "add: expected 'self' to be a CUDA tensor, but got a CPU tensor"):
x + sparse_y
x = torch.zeros(0, 4, 4, 0)
sparse_y = torch.cuda.sparse.FloatTensor(torch.LongTensor(1, 0).cuda(),
torch.randn(0, 4, 4, 0).cuda(),
[0, 4, 4, 0])
with self.assertRaisesRegex(RuntimeError, "add: expected 'self' to be a CUDA tensor, but got a CPU tensor"):
x + sparse_y
if __name__ == '__main__':
run_tests()