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b30c803662
Summary: Follow-up to #13577 The idea is to take each values tensor, concatenate it with zeros before and after itself (along the dimension corresponding to the one we're catting the tensors along), to get a tensor corresponding to the values for that tensor in the result. Then we concatenate all of those together to get the final values tensor. (Hopefully, this will be more clear from the example in the comments). The indices are more straightforward: since we aren't concatenating along a sparse dimension, they don't change at all, so all we need to do are concatenate the indices from the different tensors together. Pull Request resolved: https://github.com/pytorch/pytorch/pull/13761 Differential Revision: D13160343 Pulled By: umanwizard fbshipit-source-id: 13d7adecd369e0eebdf5bce3d90a51029b66bd1d
1821 lines
73 KiB
Python
1821 lines
73 KiB
Python
import torch
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from torch import sparse
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import itertools
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import functools
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import random
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import unittest
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from common_utils import TestCase, run_tests, skipIfRocm, do_test_dtypes, do_test_empty_full, load_tests
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from common_cuda import TEST_CUDA
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from numbers import Number
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# load_tests from common_utils is used to automatically filter tests for
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# sharding on sandcastle. This line silences flake warnings
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load_tests = load_tests
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def cpu_only(inner):
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@functools.wraps(inner)
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def outer(self, *args, **kwargs):
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if self.is_cuda:
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raise unittest.SkipTest("Test is CPU-only")
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inner(self, *args, **kwargs)
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return outer
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def cuda_only(inner):
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@functools.wraps(inner)
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def outer(self, *args, **kwargs):
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if not self.is_cuda:
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raise unittest.SkipTest("Test is GPU-only")
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inner(self, *args, **kwargs)
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return outer
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class TestSparse(TestCase):
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def setUp(self):
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# These parameters control the various ways we can run the test.
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# We will subclass and override this method to implement CUDA
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# tests
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self.is_cuda = False
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self.is_uncoalesced = False
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self.device = 'cpu'
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self.IndexTensor = torch.LongTensor
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self.ValueTensor = torch.DoubleTensor
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self.value_dtype = torch.float64
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self.SparseTensor = torch.sparse.DoubleTensor
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super(TestSparse, self).setUp()
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def _gen_sparse(self, sparse_dim, nnz, with_size):
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if isinstance(with_size, Number):
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with_size = [with_size] * sparse_dim
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x, i, v = self.genSparseTensor(with_size, sparse_dim, nnz, self.is_uncoalesced, self.device)
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if self.is_uncoalesced:
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self.assert_uncoalesced(x)
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return x, i, v
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def assert_uncoalesced(self, x):
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"""
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Test if a CPU tensor is uncoalesced. This is used to ensure
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correctness of the uncoalesced tensor generation algorithm.
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"""
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assert not x.is_coalesced()
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existing_indices = set()
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for i in range(x._nnz()):
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index = str(x._indices()[:, i])
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if index in existing_indices:
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return True
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else:
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existing_indices.add(index)
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def randn(self, *args, **kwargs):
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"""
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Variant of torch.randn that also works in the TEST_CUDA case.
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"""
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# TODO: Put this in torch.cuda.randn
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return self.ValueTensor(*args, **kwargs).normal_()
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@skipIfRocm # ROCm stack doesn't like the x + x call
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def test_print(self):
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shape_sparse_dim_nnz = [
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((), 0, 2),
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((0,), 0, 10),
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((2,), 0, 3),
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((100, 3), 1, 3),
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((100, 20, 3), 2, 0),
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((10, 0, 3), 0, 3),
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((10, 0, 3), 0, 0),
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]
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printed = []
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for shape, sparse_dim, nnz in shape_sparse_dim_nnz:
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indices_shape = torch.Size((sparse_dim, nnz))
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values_shape = torch.Size((nnz,) + shape[sparse_dim:])
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printed.append("# shape: {}".format(torch.Size(shape)))
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printed.append("# nnz: {}".format(nnz))
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printed.append("# sparse_dim: {}".format(sparse_dim))
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printed.append("# indices shape: {}".format(indices_shape))
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printed.append("# values shape: {}".format(values_shape))
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indices = torch.arange(indices_shape.numel(), dtype=self.IndexTensor.dtype,
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device=self.device).view(indices_shape)
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for d in range(sparse_dim):
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indices[d].clamp_(max=(shape[d] - 1)) # make it valid index
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if self.is_uncoalesced and indices.numel() > 0:
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indices[:, -1] = indices[:, 0] # make it uncoalesced
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values_numel = values_shape.numel()
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values = torch.arange(values_numel, dtype=self.ValueTensor.dtype,
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device=self.device).view(values_shape).div_(values_numel / 2.)
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sp_tensor = self.SparseTensor(indices, values, shape)
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dtypes = [torch.int32]
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if values.dtype == torch.double:
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dtypes.append(torch.float)
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else:
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dtypes.append(torch.double)
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for dtype in dtypes:
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printed.append("########## {} ##########".format(dtype))
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x = sp_tensor.detach().to(dtype)
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printed.append("# sparse tensor")
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printed.append(str(x))
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if x.dtype.is_floating_point:
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printed.append("# after requires_grad_")
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printed.append(str(x.requires_grad_()))
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printed.append("# after addition")
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printed.append(str(x + x))
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printed.append("# _indices")
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printed.append(str(x._indices()))
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printed.append("# _values")
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printed.append(str(x._values()))
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printed.append('')
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self.assertExpected('\n'.join(printed))
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@skipIfRocm
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def test_basic(self):
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def test_shape(sparse_dims, nnz, with_size):
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if isinstance(with_size, Number):
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with_size = [with_size] * sparse_dims
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x, i, v = self._gen_sparse(sparse_dims, nnz, with_size)
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self.assertEqual(i, x._indices())
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self.assertEqual(v, x._values())
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self.assertEqual(x.ndimension(), len(with_size))
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self.assertEqual(self.safeCoalesce(x)._nnz(), nnz)
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self.assertEqual(list(x.size()), with_size)
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# Test .indices() and .values()
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if self.is_uncoalesced:
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with self.assertRaisesRegex(RuntimeError, "Cannot get indices on an uncoalesced tensor"):
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x.indices()
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with self.assertRaisesRegex(RuntimeError, "Cannot get values on an uncoalesced tensor"):
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x.values()
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else:
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self.assertEqual(x.indices(), x._indices())
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self.assertEqual(x.values(), x._values())
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test_shape(3, 10, 100)
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test_shape(3, 10, [100, 100, 100])
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test_shape(3, 10, [100, 100, 100, 5, 5, 5, 0])
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test_shape(3, 0, [0, 0, 100, 5, 5, 5, 0])
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# Make sure that coalesce handles duplicate indices correctly
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i = self.IndexTensor([[9, 0, 0, 0, 8, 1, 1, 1, 2, 7, 2, 2, 3, 4, 6, 9]])
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v = self.ValueTensor([[idx**2, idx] for idx in range(i.size(1))])
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x = self.SparseTensor(i, v, torch.Size([10, 2]))
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self.assertEqual(self.safeCoalesce(x)._nnz(), 9)
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# Make sure we can access empty indices / values
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x = self.SparseTensor()
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self.assertEqual(x._indices().numel(), 0)
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self.assertEqual(x._values().numel(), 0)
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@skipIfRocm
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def test_coalecce(self):
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for empty_i, empty_v, empty_nnz in itertools.product([True, False], repeat=3):
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sparse_size = [] if empty_i else [2, 1]
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dense_size = [1, 0, 2] if empty_v else [1, 2]
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nnz = 0 if empty_nnz else 5
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t, _, _ = self._gen_sparse(len(sparse_size), nnz, sparse_size + dense_size)
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self.safeCoalesce(t) # this tests correctness
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def test_ctor_size_checks(self):
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indices = self.IndexTensor([
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[0, 0, 0],
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[0, 3, 0],
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[0, 0, 0],
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[0, 0, 0],
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])
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values = self.ValueTensor([2, 1, 3, 4])
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# indices inconsistent with size
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self.assertRaises(
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RuntimeError,
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lambda: self.SparseTensor(indices, values, torch.Size([2, 1, 1])))
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# values inconsistent with size
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values = self.ValueTensor([
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[2, 1, 2, 1],
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[1, 0, 5, 2],
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])
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self.assertRaises(
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RuntimeError,
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lambda: self.SparseTensor(indices, values, torch.Size([2, 4, 2, 1])))
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@skipIfRocm
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def test_to_dense(self):
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def test_tensor(x, res):
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x.to_dense() # Tests triple to_dense for memory corruption
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x.to_dense()
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x.to_dense()
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self.assertEqual(res, x.to_dense())
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self.assertEqual(res, self.safeToDense(x))
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i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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[0, 0, 1, 4],
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])
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v = self.ValueTensor([2, 1, 3, 4])
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5]))
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res = self.ValueTensor([
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[[2, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]],
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[[1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]],
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[[0, 3, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 4]],
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])
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test_tensor(x, res)
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i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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[0, 0, 1, 4],
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])
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v = self.ValueTensor(4, 0)
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 0]))
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res = self.ValueTensor(3, 4, 5, 0)
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test_tensor(x, res)
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@skipIfRocm # see https://github.com/pytorch/pytorch/pull/12171#issuecomment-431069849
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def test_to_sparse(self):
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shape = [10, 5, 19, 8]
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max_nnz = 1
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for dim, dim_sz in enumerate(shape, 1):
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max_nnz *= dim_sz
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rnnz = torch.randint(2, max_nnz, (1,)).item()
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for nnz in [0, 1, rnnz]:
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expected, _, _ = self._gen_sparse(dim, nnz, shape)
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d = expected.to_dense()
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result = d.to_sparse(dim)
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self.assertEqual(d, result.to_dense()) # == not implemented for sparse tensors yet
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self.assertEqual(expected.size(), result.size())
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self.assertEqual(dim, result.sparse_dim())
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sp, _, _ = self._gen_sparse(2, 10, [3, 3, 3])
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self.assertRaises(RuntimeError, lambda: sp.to_sparse())
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@skipIfRocm
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def test_shared(self):
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i = self.IndexTensor([[2]])
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v = self.ValueTensor([5])
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x = self.SparseTensor(i, v, torch.Size([3]))
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v[0] = 6
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self.assertEqual(self.ValueTensor([0, 0, 6]), self.safeToDense(x))
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i[0][0] = 0
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self.assertEqual(self.ValueTensor([6, 0, 0]), self.safeToDense(x))
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i = self.IndexTensor([[2]])
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v = self.ValueTensor(1, 0)
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x = self.SparseTensor(i, v, torch.Size([3, 0]))
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i[0][0] = 0
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self.assertEqual(self.ValueTensor(3, 0), self.safeToDense(x))
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@skipIfRocm
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def test_to_dense_hybrid(self):
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def test_tensor(x, res):
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x.to_dense() # Tests double to_dense for memory corruption
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x.to_dense()
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x.to_dense()
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self.assertEqual(res, x.to_dense())
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self.assertEqual(res, self.safeToDense(x))
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i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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])
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v = self.ValueTensor([[2, 3], [1, 2], [3, 4], [4, 5]])
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x = self.SparseTensor(i, v, torch.Size([3, 4, 2]))
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res = self.ValueTensor([
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[[2, 3],
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[0, 0],
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[0, 0],
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[0, 0]],
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[[1, 2],
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[0, 0],
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[0, 0],
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[0, 0]],
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[[3, 4],
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[0, 0],
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[0, 0],
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[4, 5]],
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])
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test_tensor(x, res)
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i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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])
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v = self.ValueTensor(4, 2, 0)
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x = self.SparseTensor(i, v, torch.Size([3, 4, 2, 0]))
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res = self.ValueTensor(3, 4, 2, 0)
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test_tensor(x, res)
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@skipIfRocm
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def test_contig(self):
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def test_tensor(x, exp_i, exp_v):
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x = self.safeCoalesce(x)
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self.assertEqual(exp_i, x._indices())
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self.assertEqual(exp_v, x._values())
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i = self.IndexTensor([
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[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
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[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
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])
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v = self.ValueTensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
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x = self.SparseTensor(i, v, torch.Size([100, 100]))
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exp_i = self.IndexTensor([
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[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
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[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
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])
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exp_v = self.ValueTensor([2, 1, 6, 4, 10, 3, 5, 9, 8, 7])
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test_tensor(x, exp_i, exp_v)
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i = self.IndexTensor([
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[2, 0, 2, 1],
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[0, 0, 3, 0],
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[1, 0, 4, 0],
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])
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v = self.ValueTensor([3, 2, 4, 1])
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5]))
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exp_i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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[0, 0, 1, 4],
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])
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exp_v = self.ValueTensor([2, 1, 3, 4])
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test_tensor(x, exp_i, exp_v)
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i = self.IndexTensor([
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[2, 0, 2, 1],
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[0, 0, 3, 0],
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[1, 0, 4, 0],
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])
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v = self.ValueTensor(4, 0)
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 0]))
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exp_i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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[0, 0, 1, 4],
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])
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exp_v = self.ValueTensor(4, 0)
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test_tensor(x, exp_i, exp_v)
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# Duplicate indices
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i = self.IndexTensor([
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[0, 0, 2, 0],
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[0, 0, 3, 0],
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[0, 0, 4, 0],
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])
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v = self.ValueTensor([3, 2, 4, 1])
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5]))
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exp_i = self.IndexTensor([
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[0, 2],
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[0, 3],
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[0, 4],
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])
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exp_v = self.ValueTensor([6, 4])
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test_tensor(x, exp_i, exp_v)
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i = self.IndexTensor([
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[0, 0, 2, 0],
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[0, 0, 3, 0],
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[0, 0, 4, 0],
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])
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v = self.ValueTensor(4, 0)
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 0]))
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exp_i = self.IndexTensor([
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[0, 2],
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[0, 3],
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[0, 4],
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])
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exp_v = self.ValueTensor(2, 0)
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test_tensor(x, exp_i, exp_v)
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@skipIfRocm
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def test_contig_hybrid(self):
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def test_tensor(x, exp_i, exp_v):
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x = self.safeCoalesce(x)
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self.assertEqual(exp_i, x._indices())
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self.assertEqual(exp_v, x._values())
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i = self.IndexTensor([
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[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
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[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
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])
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v = self.ValueTensor([
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[1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
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[6, 7], [7, 8], [8, 9], [9, 10], [10, 11],
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])
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x = self.SparseTensor(i, v, torch.Size([100, 100, 2]))
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exp_i = self.IndexTensor([
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[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
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[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
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])
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exp_v = self.ValueTensor([
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[2, 3], [1, 2], [6, 7], [4, 5], [10, 11],
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[3, 4], [5, 6], [9, 10], [8, 9], [7, 8],
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])
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test_tensor(x, exp_i, exp_v)
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i = self.IndexTensor([
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[2, 0, 2, 1],
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[0, 0, 3, 0],
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[1, 0, 4, 0],
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])
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v = self.ValueTensor([[3, 3, 3], [2, 2, 2], [4, 4, 4], [1, 1, 1]])
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x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 3]))
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exp_i = self.IndexTensor([
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[0, 1, 2, 2],
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[0, 0, 0, 3],
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[0, 0, 1, 4],
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])
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exp_v = self.ValueTensor([[2, 2, 2], [1, 1, 1], [3, 3, 3], [4, 4, 4]])
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test_tensor(x, exp_i, exp_v)
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i = self.IndexTensor([
|
|
[2, 0, 2, 1],
|
|
[0, 0, 3, 0],
|
|
[1, 0, 4, 0],
|
|
])
|
|
v = self.ValueTensor(4, 3, 0)
|
|
x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 3, 0]))
|
|
exp_i = self.IndexTensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
[0, 0, 1, 4],
|
|
])
|
|
exp_v = self.ValueTensor(4, 3, 0)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
# Duplicate indices
|
|
i = self.IndexTensor([
|
|
[0, 0, 2, 0],
|
|
[0, 0, 3, 0],
|
|
[0, 0, 4, 0],
|
|
])
|
|
v = self.ValueTensor([[3, 2, 3], [2, 1, 1], [4, 3, 4], [1, 1, 1]])
|
|
x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 3]))
|
|
exp_i = self.IndexTensor([
|
|
[0, 2],
|
|
[0, 3],
|
|
[0, 4],
|
|
])
|
|
exp_v = self.ValueTensor([[6, 4, 5], [4, 3, 4]])
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.IndexTensor([
|
|
[0, 0, 2, 0],
|
|
[0, 0, 3, 0],
|
|
[0, 0, 4, 0],
|
|
])
|
|
v = self.ValueTensor(4, 3, 0)
|
|
x = self.SparseTensor(i, v, torch.Size([3, 4, 5, 3, 0]))
|
|
exp_i = self.IndexTensor([
|
|
[0, 2],
|
|
[0, 3],
|
|
[0, 4],
|
|
])
|
|
exp_v = self.ValueTensor(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])
|
|
|
|
@skipIfRocm
|
|
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.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.FloatTensor(2, 3, 4, 0)
|
|
test_tensor(x)
|
|
|
|
@skipIfRocm
|
|
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.SparseTensor(2, 3)
|
|
test_in_place(x)
|
|
test_not_in_place(x)
|
|
|
|
x = self.SparseTensor(2, 0)
|
|
test_in_place(x)
|
|
test_not_in_place(x)
|
|
|
|
@skipIfRocm
|
|
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])
|
|
|
|
@skipIfRocm
|
|
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))
|
|
|
|
@skipIfRocm
|
|
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(RuntimeError, 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")
|
|
|
|
@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(alpha, t, beta, x, y)
|
|
expected = torch.addmm(alpha, t, beta, self.safeToDense(x), y)
|
|
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)
|
|
|
|
@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(alpha, t, beta, x, y)
|
|
expected = torch.addmm(alpha, self.safeToDense(t), beta, self.safeToDense(x), y)
|
|
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)
|
|
|
|
@skipIfRocm
|
|
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, r, x)
|
|
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, r, x)
|
|
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.SparseTensor(i[:, ::2], v[:int(nnz / 2)], x.shape)
|
|
res = torch.add(y, r, x_)
|
|
expected = y + r * self.safeToDense(x_)
|
|
self.assertEqual(res, expected)
|
|
|
|
# Non contiguous sparse values tensor
|
|
x_ = self.SparseTensor(i[:, :int(nnz / 2)], v[::2], x.shape)
|
|
res = torch.add(y, r, x_)
|
|
expected = y + r * self.safeToDense(x_)
|
|
self.assertEqual(res, expected)
|
|
|
|
# Non contiguous sparse indices and values tensors
|
|
x_ = self.SparseTensor(i[:, 1::2], v[1::2], x.shape)
|
|
res = torch.add(y, r, x_)
|
|
expected = y + r * self.safeToDense(x_)
|
|
self.assertEqual(res, expected)
|
|
|
|
@skipIfRocm
|
|
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])
|
|
|
|
@skipIfRocm
|
|
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])
|
|
|
|
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])
|
|
|
|
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)
|
|
|
|
# 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())
|
|
|
|
@skipIfRocm
|
|
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])
|
|
|
|
@skipIfRocm
|
|
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])
|
|
|
|
@skipIfRocm
|
|
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.SparseTensor(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_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.IndexTensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
])
|
|
v = self.ValueTensor([1, 2, 3, 4])
|
|
x = self.SparseTensor(i, v, torch.Size([5, 4])).coalesce()
|
|
dense = self.ValueTensor([
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
[17, 18, 19, 20],
|
|
])
|
|
exp_v = self.ValueTensor([7, 14, 3, 20])
|
|
res = dense.sparse_mask(x)
|
|
expected = self.SparseTensor(i, exp_v, torch.Size([5, 4]))
|
|
self.assertEqual(res, expected)
|
|
|
|
i = self.IndexTensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
])
|
|
v = self.ValueTensor(4, 0)
|
|
x = self.SparseTensor(i, v, torch.Size([5, 4, 0])).coalesce()
|
|
dense = self.ValueTensor(5, 4, 0)
|
|
exp_v = self.ValueTensor(4, 0)
|
|
res = dense.sparse_mask(x)
|
|
expected = self.SparseTensor(i, exp_v, torch.Size([5, 4, 0]))
|
|
self.assertEqual(res, expected)
|
|
|
|
@skipIfRocm
|
|
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.IndexTensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
])
|
|
v = self.ValueTensor([[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.SparseTensor(i, v, torch.Size([5, 4, 2])).coalesce()
|
|
dense = self.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 = self.ValueTensor([[7, 9], [14, 1], [3, 3], [20, 1]])
|
|
expected = self.SparseTensor(i, exp_v, torch.Size([5, 4, 2]))
|
|
self.assertEqual(res, expected)
|
|
|
|
i = self.IndexTensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
])
|
|
v = self.ValueTensor(4, 2, 0)
|
|
x = self.SparseTensor(i, v, torch.Size([5, 4, 2, 0])).coalesce()
|
|
dense = self.ValueTensor(5, 4, 2, 0)
|
|
res = dense.sparse_mask(x)
|
|
exp_v = self.ValueTensor(4, 2, 0)
|
|
expected = self.SparseTensor(i, exp_v, torch.Size([5, 4, 2, 0]))
|
|
self.assertEqual(res, expected)
|
|
|
|
@skipIfRocm
|
|
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])
|
|
|
|
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]
|
|
|
|
@skipIfRocm
|
|
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, input, dense_tensor):
|
|
expected_output = dense_tensor.log1p()
|
|
self.assertEqual(expected_output, input.log1p().to_dense())
|
|
self.assertEqual(expected_output, input.coalesce().log1p_().to_dense())
|
|
|
|
# test in-place op on uncoalesced input
|
|
with self.assertRaisesRegex(RuntimeError, "in-place on uncoalesced tensors is not supported yet"):
|
|
input.log1p_()
|
|
|
|
input.requires_grad_()
|
|
self.assertTrue(input.requires_grad)
|
|
|
|
# test autograd
|
|
x = input.clone()
|
|
y = input.log1p()
|
|
with self.assertRaisesRegex(RuntimeError, "log1p of a sparse tensor is made to be non-differentiable"):
|
|
y.backward(x)
|
|
|
|
@skipIfRocm
|
|
def test_log1p(self):
|
|
input = torch.sparse_coo_tensor(
|
|
torch.LongTensor([[0], [1], [2]]).transpose(1, 0).clone().detach(),
|
|
torch.FloatTensor([3, 4, 5]),
|
|
torch.Size([3]),
|
|
device=self.device)
|
|
self._test_log1p_tensor(input, torch.as_tensor([3., 4., 5.]))
|
|
|
|
# test uncoalesced input
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
torch.LongTensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0).clone().detach(),
|
|
torch.FloatTensor([2, 3, 4, 1, 1, 1]),
|
|
torch.Size([3]),
|
|
device=self.device)
|
|
self._test_log1p_tensor(input_uncoalesced, torch.as_tensor([3., 4., 5.]))
|
|
|
|
input = torch.sparse_coo_tensor(
|
|
torch.zeros([2, 0]),
|
|
torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
|
|
torch.Size([0, 0, 5, 5, 5, 5, 5, 5, 0]),
|
|
device=self.device)
|
|
self._test_log1p_tensor(input, torch.zeros([0, 0, 5, 5, 5, 5, 5, 5, 0]))
|
|
|
|
input = torch.sparse_coo_tensor(
|
|
torch.zeros([1, 5]),
|
|
torch.zeros([5, 6, 0]),
|
|
torch.Size([5, 6, 0]),
|
|
device=self.device)
|
|
self._test_log1p_tensor(input, torch.zeros([5, 6, 0]))
|
|
|
|
@skipIfRocm
|
|
def test_sparse_add_coalesce(self):
|
|
i = self.IndexTensor([[1, 2, 1]])
|
|
v = self.ValueTensor([3, 4, 5])
|
|
x = self.SparseTensor(i, v, torch.Size([3]))
|
|
y = self.SparseTensor(i, v, torch.Size([3]))
|
|
z = x + y
|
|
|
|
self.assertFalse(z._indices().numel() != 2 and z.is_coalesced())
|
|
|
|
i = self.IndexTensor([[1, 2, 1]])
|
|
v = self.ValueTensor(3, 0)
|
|
x = self.SparseTensor(i, v, torch.Size([3, 0]))
|
|
y = self.SparseTensor(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.IndexTensor([[2]]).cuda(1)
|
|
v = self.ValueTensor([5]).cuda(1)
|
|
x = self.SparseTensor(i, v, torch.Size([3]), device=1)
|
|
check_device(x, 1)
|
|
|
|
i = self.IndexTensor([[2]]).cuda(1)
|
|
v = self.ValueTensor(1, 0).cuda(1)
|
|
x = self.SparseTensor(i, v, torch.Size([3, 0]), device=1)
|
|
check_device(x, 1)
|
|
|
|
x = self.SparseTensor(3, device=1)
|
|
check_device(x, 1)
|
|
|
|
x = self.SparseTensor(3, 0, device=1)
|
|
check_device(x, 1)
|
|
|
|
i = self.IndexTensor([[2]]).cuda(1)
|
|
v = self.ValueTensor([5]).cuda(0)
|
|
self.assertRaises(RuntimeError, lambda: self.SparseTensor(i, v, torch.Size([3])))
|
|
|
|
i = self.IndexTensor([[2]]).cuda(1)
|
|
v = self.ValueTensor(1, 0).cuda(0)
|
|
self.assertRaises(RuntimeError, lambda: self.SparseTensor(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)
|
|
|
|
@skipIfRocm
|
|
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
|
|
self.assertEqual(x.new(indices, values), x)
|
|
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]):
|
|
# have to include size with cuda sparse tensors
|
|
include_size = include_size or use_cuda
|
|
dtype = torch.float64
|
|
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.ValueTensor(1, 0)
|
|
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.IndexTensor([[1, 2],
|
|
[0, 2]])
|
|
values = self.ValueTensor([.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.IndexTensor([[1, 2],
|
|
[0, 2]])
|
|
values = self.ValueTensor(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.IndexTensor([[1, 2],
|
|
[0, 2]])
|
|
values = self.ValueTensor(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.IndexTensor([[1, 2],
|
|
[0, 2]])
|
|
values = self.ValueTensor([[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.IndexTensor([[1, 2],
|
|
[0, 2]])
|
|
values = self.ValueTensor(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.SparseTensor()
|
|
expected_indices = self.IndexTensor(1, 0)
|
|
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.SparseTensor()
|
|
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.IndexTensor([[0]]) # (sparse_dim, nnz): (1, 1)
|
|
values = self.ValueTensor([[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.IndexTensor([[0]]) # (sparse_dim, nnz): (1, 1)
|
|
values = self.ValueTensor(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)
|
|
expected_values = torch.empty(v_shape, device=device)
|
|
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])
|
|
|
|
@skipIfRocm
|
|
def test_factory_dense_dim(self):
|
|
indices = self.IndexTensor([[0]])
|
|
values = self.ValueTensor([[[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.IndexTensor([[0]])
|
|
values = self.ValueTensor(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.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.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
|
|
shape = (1, 3)
|
|
for indices_device in ['cuda', 'cpu']:
|
|
for values_device in ['cuda', 'cpu']:
|
|
for sparse_device in ['cuda', 'cpu', None]:
|
|
for test_empty_tensor in [True, False]:
|
|
if test_empty_tensor:
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2]), device=indices_device),
|
|
self.ValueTensor(1, 0).to(values_device),
|
|
(1, 3, 0), device=sparse_device)
|
|
else:
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2]), device=indices_device),
|
|
torch.tensor([1.], device=values_device),
|
|
(1, 3), device=sparse_device)
|
|
should_be_cuda = sparse_device == 'cuda' or (sparse_device is None and values_device == 'cuda')
|
|
self.assertEqual(should_be_cuda, t.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.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'))
|
|
|
|
@cpu_only # not really, but we only really want to run this once
|
|
def test_dtypes(self):
|
|
all_sparse_dtypes = [dtype for dtype in torch.testing.get_all_dtypes() if dtype != torch.float16]
|
|
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 = [dtype for dtype in torch.testing.get_all_dtypes() if dtype != torch.float16]
|
|
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cpu'))
|
|
if torch.cuda.device_count() > 0:
|
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do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, None)
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do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cuda:0'))
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|
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def test_is_sparse(self):
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x = torch.randn(3, 3)
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self.assertFalse(x.is_sparse)
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x = torch.randn(3, 3, 0)
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self.assertFalse(x.is_sparse)
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|
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x = self.SparseTensor()
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self.assertTrue(x.is_sparse)
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|
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x = self.SparseTensor(1, 0)
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self.assertTrue(x.is_sparse)
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|
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@skipIfRocm
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def test_resize_as(self):
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def do_test(t):
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y = t.new().resize_as_(t).zero_()
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self.assertEqual(y.shape, t.shape)
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# Check that y can be added to t. Currently, this requires that
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# sparse_dim and dense_dim match.
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self.assertEqual(t, t + y)
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|
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do_test(self.SparseTensor())
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do_test(self.SparseTensor(3, 0))
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do_test(self.SparseTensor(3, 3))
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|
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def _test_resize_shape(self, x_i, x_v, x_size, y_i, y_v, y_size):
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x_v_numel = torch.zeros(x_v).numel()
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y_v_numel = torch.zeros(y_v).numel()
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x = torch.sparse_coo_tensor(torch.zeros(x_i),
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torch.arange(x_v_numel).resize_(x_v).to(torch.float),
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torch.Size(x_size))
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x_dense = x.to_dense()
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y = torch.sparse_coo_tensor(torch.zeros(y_i),
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torch.ones(y_v).to(torch.float),
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torch.Size(y_size))
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y_dense = y.to_dense()
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x.resize_as_(y)
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x_dense.resize_as_(y_dense)
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self.assertEqual(x.shape, y.shape)
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self.assertEqual(x.sparse_dim(), y.sparse_dim())
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self.assertEqual(x.dense_dim(), y.dense_dim())
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self.assertEqual(x.shape, x_dense.shape)
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self.assertEqual(y.shape, y_dense.shape)
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# Here we make sure that the original data are preserved after resizing
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self.assertEqual(x.to_dense().view(-1)[0:x_v_numel].view(x_v),
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|
x_dense.view(-1)[0:x_v_numel].view(x_v))
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|
|
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def test_resize(self):
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# 1. Expand the size of some dense dimensions [Supported]
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self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
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[1, 1], [1, 2, 4], [2, 2, 4])
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|
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self._test_resize_shape([1, 1], [1, 2, 0], [2, 2, 0],
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[1, 1], [1, 2, 4], [2, 2, 4])
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|
|
|
# 2. Expand the size of some sparse dimensions [Supported]
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self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
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|
[1, 1], [1, 2, 3], [4, 2, 3])
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|
|
|
# 3. Change the shapes of both sparse and dense dimensions when nnz is zero [Supported]
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self._test_resize_shape([1, 0], [0, 2, 3], [2, 2, 3],
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|
[2, 0], [0, 2, 4, 5], [1, 1, 2, 4, 5])
|
|
|
|
self._test_resize_shape([1, 0], [0, 2, 3], [2, 2, 3],
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|
[2, 0], [0, 2, 4, 0], [1, 1, 2, 4, 0])
|
|
|
|
# 4. Add dims to dense dimensions [Not Supported]
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|
with self.assertRaisesRegex(RuntimeError, "changing the number of dense dimensions"):
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|
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, "bool value of Tensor with no values is ambiguous"):
|
|
torch.sparse_coo_tensor(([0, 1],), self.ValueTensor(2, 0), (4, 0)).is_nonzero()
|
|
|
|
|
|
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.IndexTensor = torch.cuda.LongTensor
|
|
self.ValueTensor = torch.cuda.DoubleTensor
|
|
self.SparseTensor = 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 'other' to be a CPU tensor\\, but got a CUDA 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 'other' to be a CPU tensor\\, but got a CUDA 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 'other' to be a CPU tensor\\, but got a CUDA tensor"):
|
|
x + sparse_y
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|