frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-21 13:44:15 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
bafb2e5cc2703b47e4d397aff87649f67bd9f2ca
pytorch/test/test_sparse.py
Edward Z. Yang bafb2e5cc2 Implement sparse pow. (#1387)
2017-04-28 23:06:09 +02:00

606 lines
19 KiB
Python
Raw Blame History

import torch
from torch import sparse
import itertools
import random
import unittest
from common import TestCase, run_tests
from common_nn import TEST_CUDA
from numbers import Number
# triplet := (index type, value type, sparse type)
cpu_triplet = (
torch.LongTensor,
torch.DoubleTensor,
torch.sparse.DoubleTensor)
if TEST_CUDA:
cuda_triplet = (
torch.cuda.LongTensor,
torch.cuda.DoubleTensor,
torch.cuda.sparse.DoubleTensor)
class TestSparse(TestCase):
@staticmethod
def _gen_sparse(d, nnz, with_size, is_cuda=False):
if isinstance(with_size, Number):
v = torch.randn(nnz)
i = (torch.rand(d, nnz) * with_size).type(torch.LongTensor)
x = torch.sparse.DoubleTensor(i, v)
else:
v_size = [nnz] + list(with_size[d:])
v = torch.randn(*v_size)
i = torch.rand(d, nnz) * \
torch.Tensor(with_size[:d]).repeat(nnz, 1).transpose(0, 1)
i = i.type(torch.LongTensor)
x = torch.sparse.DoubleTensor(i, v, torch.Size(with_size))
if is_cuda:
return x.cuda(), i.cuda(), v.cuda()
else:
return x, i.clone(), v.clone()
def _test_basic(self, is_cuda):
x, i, v = self._gen_sparse(3, 10, 100, is_cuda)
self.assertEqual(i, x.indices())
self.assertEqual(v, x.values())
x, i, v = self._gen_sparse(3, 10, [100, 100, 100], is_cuda)
self.assertEqual(i, x.indices())
self.assertEqual(v, x.values())
self.assertEqual(x.ndimension(), 3)
self.assertEqual(x.nnz(), 10)
for i in range(3):
self.assertEqual(x.size(i), 100)
SparseTensor = (cuda_triplet if is_cuda else cpu_triplet)[2]
# Make sure we can access empty indices / values
x = SparseTensor()
self.assertEqual(x.indices().numel(), 0)
self.assertEqual(x.values().numel(), 0)
def test_basic(self):
self._test_basic(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_basic_cuda(self):
self._test_basic(True)
def _test_to_dense(self, is_cuda):
IndexTensor, ValueTensor, SparseTensor = \
cuda_triplet if is_cuda else cpu_triplet
i = IndexTensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
v = ValueTensor([2, 1, 3, 4])
x = SparseTensor(i, v, torch.Size([3, 4, 5]))
res = ValueTensor([
[[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]],
])
x.to_dense() # Tests double to_dense for memory corruption
x.to_dense()
x.to_dense()
self.assertEqual(res, x.to_dense())
def test_to_dense(self):
self._test_to_dense(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_to_dense_cuda(self):
self._test_to_dense(True)
def _test_to_dense_hybrid(self, is_cuda):
IndexTensor, ValueTensor, SparseTensor = \
cuda_triplet if is_cuda else cpu_triplet
i = IndexTensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
])
v = ValueTensor([[2, 3], [1, 2], [3, 4], [4, 5]])
x = SparseTensor(i, v, torch.Size([3, 4, 2]))
res = ValueTensor([
[[2, 3],
[0, 0],
[0, 0],
[0, 0]],
[[1, 2],
[0, 0],
[0, 0],
[0, 0]],
[[3, 4],
[0, 0],
[0, 0],
[4, 5]],
])
x.to_dense() # Tests double to_dense for memory corruption
x.to_dense()
x.to_dense()
self.assertEqual(res, x.to_dense())
def test_to_dense_hybrid(self):
self._test_to_dense_hybrid(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_to_dense_hybrid_cuda(self):
self._test_to_dense_hybrid(True)
def _test_contig(self, is_cuda):
IndexTensor, ValueTensor, SparseTensor = \
cuda_triplet if is_cuda else cpu_triplet
i = IndexTensor([
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
])
v = ValueTensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
x = SparseTensor(i, v, torch.Size([100, 100]))
exp_i = IndexTensor([
[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
])
exp_v = ValueTensor([2, 1, 6, 4, 10, 3, 5, 9, 8, 7])
x.contiguous()
self.assertEqual(exp_i, x.indices())
self.assertEqual(exp_v, x.values())
i = IndexTensor([
[2, 0, 2, 1],
[0, 0, 3, 0],
[1, 0, 4, 0],
])
v = ValueTensor([3, 2, 4, 1])
x = SparseTensor(i, v, torch.Size([3, 4, 5]))
exp_i = IndexTensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
exp_v = ValueTensor([2, 1, 3, 4])
x.contiguous()
self.assertEqual(exp_i, x.indices())
self.assertEqual(exp_v, x.values())
# Duplicate indices
i = IndexTensor([
[0, 0, 2, 0],
[0, 0, 3, 0],
[0, 0, 4, 0],
])
v = ValueTensor([3, 2, 4, 1])
x = SparseTensor(i, v, torch.Size([3, 4, 5]))
exp_i = IndexTensor([
[0, 2],
[0, 3],
[0, 4],
])
exp_v = ValueTensor([6, 4])
x.contiguous()
self.assertEqual(exp_i, x.indices())
self.assertEqual(exp_v, x.values())
def test_contig(self):
self._test_contig(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_contig_cuda(self):
self._test_contig(True)
def _test_contig_hybrid(self, is_cuda):
IndexTensor, ValueTensor, SparseTensor = \
cuda_triplet if is_cuda else cpu_triplet
i = IndexTensor([
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
])
v = ValueTensor([
[1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
[6, 7], [7, 8], [8, 9], [9, 10], [10, 11],
])
x = SparseTensor(i, v, torch.Size([100, 100, 2]))
exp_i = IndexTensor([
[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
])
exp_v = ValueTensor([
[2, 3], [1, 2], [6, 7], [4, 5], [10, 11],
[3, 4], [5, 6], [9, 10], [8, 9], [7, 8],
])
x.contiguous()
self.assertEqual(exp_i, x.indices())
self.assertEqual(exp_v, x.values())
i = IndexTensor([
[2, 0, 2, 1],
[0, 0, 3, 0],
[1, 0, 4, 0],
])
v = ValueTensor([[3, 3, 3], [2, 2, 2], [4, 4, 4], [1, 1, 1]])
x = SparseTensor(i, v, torch.Size([3, 4, 5, 3]))
exp_i = IndexTensor([
[0, 1, 2, 2],
[0, 0, 0, 3],
[0, 0, 1, 4],
])
exp_v = ValueTensor([[2, 2, 2], [1, 1, 1], [3, 3, 3], [4, 4, 4]])
x.contiguous()
self.assertEqual(exp_i, x.indices())
self.assertEqual(exp_v, x.values())
# Duplicate indices
i = IndexTensor([
[0, 0, 2, 0],
[0, 0, 3, 0],
[0, 0, 4, 0],
])
v = ValueTensor([[3, 2, 3], [2, 1, 1], [4, 3, 4], [1, 1, 1]])
x = SparseTensor(i, v, torch.Size([3, 4, 5, 3]))
exp_i = IndexTensor([
[0, 2],
[0, 3],
[0, 4],
])
exp_v = ValueTensor([[6, 4, 5], [4, 3, 4]])
x.contiguous()
self.assertEqual(exp_i, x.indices())
self.assertEqual(exp_v, x.values())
def test_contig_hybrid(self):
self._test_contig_hybrid(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_contig_hybrid_cuda(self):
self._test_contig_hybrid(True)
def _test_transpose(self, is_cuda):
x = self._gen_sparse(4, 20, 5, is_cuda=is_cuda)[0]
y = x.to_dense()
for i, j in itertools.combinations(range(4), 2):
x = x.transpose_(i, j)
y = y.transpose(i, j)
self.assertEqual(x.to_dense(), y)
x = x.transpose(i, j)
y = y.transpose(i, j)
self.assertEqual(x.to_dense(), y)
def test_transpose(self):
self._test_transpose(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_transpose_cuda(self):
self._test_transpose(True)
def test_mm(self):
def test_shape(di, dj, dk):
x, _, _ = self._gen_sparse(2, 20, [di, dj])
t = torch.randn(di, dk)
y = torch.randn(dj, dk)
alpha = random.random()
beta = random.random()
res = torch.addmm(alpha, t, beta, x, y)
expected = torch.addmm(alpha, t, beta, x.to_dense(), y)
self.assertEqual(res, expected)
res = torch.addmm(t, x, y)
expected = torch.addmm(t, x.to_dense(), y)
self.assertEqual(res, expected)
res = torch.mm(x, y)
expected = torch.mm(x.to_dense(), y)
self.assertEqual(res, expected)
test_shape(10, 100, 100)
test_shape(100, 1000, 200)
test_shape(64, 10000, 300)
def test_saddmm(self):
def test_shape(di, dj, dk):
x = self._gen_sparse(2, 20, [di, dj])[0]
t = self._gen_sparse(2, 20, [di, dk])[0]
y = torch.randn(dj, dk)
alpha = random.random()
beta = random.random()
res = torch.saddmm(alpha, t, beta, x, y)
expected = torch.addmm(alpha, t.to_dense(), beta, x.to_dense(), y)
self.assertEqual(res.to_dense(), expected)
res = torch.saddmm(t, x, y)
expected = torch.addmm(t.to_dense(), x.to_dense(), y)
self.assertEqual(res.to_dense(), expected)
res = torch.smm(x, y)
expected = torch.mm(x.to_dense(), y)
self.assertEqual(res.to_dense(), expected)
test_shape(7, 5, 3)
test_shape(1000, 100, 100)
test_shape(3000, 64, 300)
def _test_dsmm(self, is_cuda):
def test_shape(di, dj, dk):
x = self._gen_sparse(2, 20, [di, dj], is_cuda)[0]
y = torch.randn(dj, dk)
if is_cuda:
y = y.cuda()
res = torch.dsmm(x, y)
expected = torch.mm(x.to_dense(), y)
self.assertEqual(res, expected)
test_shape(7, 5, 3)
test_shape(1000, 100, 100)
test_shape(3000, 64, 300)
def test_dsmm(self):
self._test_dsmm(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_dsmm_cuda(self):
self._test_dsmm(True)
def _test_hsmm(self, is_cuda):
def test_shape(di, dj, dk):
x = self._gen_sparse(2, 20, [di, dj], is_cuda)[0]
y = torch.randn(dj, dk)
if is_cuda:
y = y.cuda()
res = torch.hsmm(x, y)
expected = torch.mm(x.to_dense(), y)
self.assertEqual(res.to_dense(), expected)
test_shape(7, 5, 3)
test_shape(1000, 100, 100)
test_shape(3000, 64, 300)
def test_hsmm(self):
self._test_hsmm(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_hsmm_cuda(self):
self._test_hsmm(True)
def _test_spadd_shape(self, is_cuda, shape_i, shape_v=None):
shape = shape_i + (shape_v or [])
x, _, _ = self._gen_sparse(len(shape_i), 10, shape, is_cuda)
y = torch.randn(*shape)
if is_cuda:
y = y.cuda()
r = random.random()
res = torch.add(y, r, x)
expected = y + r * x.to_dense()
self.assertEqual(res, expected)
# Non contiguous dense tensor
s = list(shape)
s[0] = shape[-1]
s[-1] = shape[0]
y = torch.randn(*s)
if is_cuda:
y = y.cuda()
y.transpose_(0, len(s) - 1)
r = random.random()
res = torch.add(y, r, x)
expected = y + r * x.to_dense()
self.assertEqual(res, expected)
def _test_spadd(self, is_cuda):
self._test_spadd_shape(is_cuda, [5, 6])
self._test_spadd_shape(is_cuda, [10, 10, 10])
self._test_spadd_shape(is_cuda, [50, 30, 20])
self._test_spadd_shape(is_cuda, [5, 5, 5, 5, 5, 5])
def test_spadd(self):
self._test_spadd(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_spadd_cuda(self):
self._test_spadd(True)
def _test_spadd_hybrid(self, is_cuda):
self._test_spadd_shape(is_cuda, [5, 6], [2, 3])
self._test_spadd_shape(is_cuda, [10, 10, 10], [3])
self._test_spadd_shape(is_cuda, [50, 30, 20], [2])
self._test_spadd_shape(is_cuda, [5, 5, 5, 5, 5, 5], [2])
def test_spadd_hybrid(self):
self._test_spadd_hybrid(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_spadd_hybrid_cuda(self):
self._test_spadd_hybrid(True)
def _test_basic_ops_shape(self, is_cuda, shape_i, shape_v=None):
shape = shape_i + (shape_v or [])
x1, _, _ = self._gen_sparse(len(shape_i), 9, shape, is_cuda)
x2, _, _ = self._gen_sparse(len(shape_i), 12, shape, is_cuda)
y1 = x1 + x2
y2 = x1.clone()
y2.add_(x2)
expected = x1.to_dense() + x2.to_dense()
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
y1 = x1 - x2
y2 = x1.clone()
y2.sub_(x2)
expected = x1.to_dense() - x2.to_dense()
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
y1 = x1 * x2
y2 = x1.clone()
y2.mul_(x2)
expected = x1.to_dense() * x2.to_dense()
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
y1 = x1 * 37.5
y2 = x1.clone()
y2.mul_(37.5)
expected = x1.to_dense() * 37.5
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
y1 = x1 / 37.5
y2 = x1.clone()
y2.div_(37.5)
expected = x1.to_dense() / 37.5
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
y1 = x1 ** 2
y2 = x1.clone()
y2.pow_(2)
expected = x1.to_dense() ** 2
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
y = x1.clone()
y.zero_()
expected = torch.zeros(x1.size())
self.assertEqual(y.to_dense(), expected)
def _test_basic_ops(self, is_cuda):
self._test_basic_ops_shape(is_cuda, [5, 6])
self._test_basic_ops_shape(is_cuda, [10, 10, 10])
self._test_basic_ops_shape(is_cuda, [50, 30, 20])
self._test_basic_ops_shape(is_cuda, [5, 5, 5, 5, 5, 5])
def test_basic_ops(self):
self._test_basic_ops(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_basic_ops_cuda(self):
self._test_basic_ops(True)
def _test_basic_ops_hybrid(self, is_cuda):
self._test_basic_ops_shape(is_cuda, [5, 6], [2, 3])
self._test_basic_ops_shape(is_cuda, [10, 10, 10], [3])
self._test_basic_ops_shape(is_cuda, [50, 30, 20], [2])
self._test_basic_ops_shape(is_cuda, [5, 5, 5, 5, 5, 5], [2])
def test_basic_ops_hybrid(self):
self._test_basic_ops_hybrid(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_basic_ops_hybrid_cuda(self):
self._test_basic_ops_hybrid(True)
def _test_sparse_mask_shape(self, is_cuda, shape_i, shape_v=None):
shape = shape_i + (shape_v or [])
x1, _, _ = self._gen_sparse(len(shape_i), 9, shape, is_cuda)
x2, _, _ = self._gen_sparse(len(shape_i), 12, shape, is_cuda)
y1 = x1 + x2
y2 = x1.clone()
y2.add_(x2)
expected = x1.to_dense() + x2.to_dense()
self.assertEqual(y1.to_dense(), expected)
self.assertEqual(y2.to_dense(), expected)
def _test_sparse_mask_fixed(self, is_cuda):
IndexTensor, ValueTensor, SparseTensor = \
cuda_triplet if is_cuda else cpu_triplet
i = IndexTensor([
[1, 3, 3, 0, 4],
[2, 1, 1, 2, 3],
])
v = ValueTensor([1, 2, 3, 4, 5])
x = SparseTensor(i, v, torch.Size([5, 4]))
dense = ValueTensor([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20],
])
exp_v = ValueTensor([7, 14, 14, 3, 20])
res = dense.sparse_mask(x)
expected = SparseTensor(i, exp_v, torch.Size([5, 4]))
self.assertEqual(res, expected)
def _test_sparse_mask(self, is_cuda):
self._test_sparse_mask_fixed(is_cuda)
self._test_sparse_mask_shape(is_cuda, [5, 6])
self._test_sparse_mask_shape(is_cuda, [10, 10, 10])
self._test_sparse_mask_shape(is_cuda, [50, 30, 20])
self._test_sparse_mask_shape(is_cuda, [5, 5, 5, 5, 5, 5])
def test_sparse_mask(self):
self._test_sparse_mask(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_sparse_mask_cuda(self):
self._test_sparse_mask(True)
def _test_sparse_mask_hybrid_fixed(self, is_cuda):
IndexTensor, ValueTensor, SparseTensor = \
cuda_triplet if is_cuda else cpu_triplet
i = IndexTensor([
[1, 3, 3, 0, 4],
[2, 1, 1, 2, 3],
])
v = ValueTensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
x = SparseTensor(i, v, torch.Size([5, 4, 2]))
dense = ValueTensor([
[[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 = ValueTensor([[7, 9], [14, 1], [14, 1], [3, 3], [20, 1]])
expected = SparseTensor(i, exp_v, torch.Size([5, 4, 2]))
self.assertEqual(res, expected)
def _test_sparse_mask_hybrid(self, is_cuda):
self._test_sparse_mask_hybrid_fixed(is_cuda)
self._test_sparse_mask_shape(is_cuda, [5, 6], [2, 3])
self._test_sparse_mask_shape(is_cuda, [10, 10, 10], [3])
self._test_sparse_mask_shape(is_cuda, [50, 30, 20], [2])
self._test_sparse_mask_shape(is_cuda, [5, 5, 5, 5, 5, 5], [2])
def test_sparse_mask_hybrid(self):
self._test_sparse_mask_hybrid(False)
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_sparse_mask_hybrid_cuda(self):
self._test_sparse_mask_hybrid(True)
if __name__ == '__main__':
run_tests()
Reference in New Issue View Git Blame Copy Permalink