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42fefd4403
Add support for sparse fake tensors. - The testing strategy is to run a fake tensor cross ref test on `test_sparse.py`. This is necessary because OpInfo sparse coverage is completely nonexistent. We could have tried to turn on cross ref testing globally for all files, but that would be very time consuming and the tests I'm interested in are mostly in this file. There are some exclusions in testing for things that don't work. - I make fake tensor converter raise a UnsupportedFakeTensorException if the meta converter fails to do a conversion (which can happen in a relatively large number of situations). - I relax fake tensor invariants so that you can make a fake tensor from a meta tensor. This is useful because in the cross ref test sometimes we operate on meta tensors. - Fake tensor wrapping is improved to handle the case when a function doesn't return any tensors - Meta converter is taught how to convert sparse tensors to meta There's still a little more cleanup that needs to be done, but this is good for review. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/82172 Approved by: https://github.com/eellison
4013 lines
175 KiB
Python
4013 lines
175 KiB
Python
# Owner(s): ["module: sparse"]
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import torch
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import itertools
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import functools
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import operator
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import random
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import unittest
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from torch.testing import make_tensor
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from torch.testing._internal.common_utils import TestCase, run_tests, skipIfRocm, do_test_dtypes, \
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do_test_empty_full, load_tests, TEST_NUMPY, TEST_SCIPY, IS_WINDOWS, gradcheck, coalescedonoff, \
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DeterministicGuard, first_sample, TEST_WITH_CROSSREF
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from torch.testing._internal.common_cuda import TEST_CUDA, _get_torch_cuda_version
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from numbers import Number
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from typing import Dict, Any
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from distutils.version import LooseVersion
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from torch.testing._internal.common_cuda import \
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(SM53OrLater, SM80OrLater, CUDA11OrLater)
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, ops, dtypes, dtypesIfCUDA, onlyCPU, onlyCUDA, precisionOverride,
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deviceCountAtLeast, OpDTypes)
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from torch.testing._internal.common_methods_invocations import \
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(sparse_unary_ufuncs, sparse_masked_reduction_ops)
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from torch.testing._internal.common_dtype import (
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all_types, all_types_and_complex, all_types_and_complex_and, floating_and_complex_types,
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floating_and_complex_types_and, integral_types, floating_types_and,
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)
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from torch.utils._python_dispatch import TorchDispatchMode
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if TEST_SCIPY:
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import scipy.sparse
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# load_tests from torch.testing._internal.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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# batched grad doesn't support sparse
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gradcheck = functools.partial(gradcheck, check_batched_grad=False)
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CUSPARSE_SPMM_COMPLEX128_SUPPORTED = (
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IS_WINDOWS and torch.version.cuda and LooseVersion(torch.version.cuda) > "11.2"
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) or (not IS_WINDOWS and CUDA11OrLater)
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class CrossRefSparseFakeMode(TorchDispatchMode):
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def __torch_dispatch__(self, func, types, args=(), kwargs=None):
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kwargs = kwargs or {}
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def on_tensor(f):
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def go(t):
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if isinstance(t, torch.Tensor):
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return f(t)
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else:
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return t
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return go
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# empty_like excluded for now due to sparse complex
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# aten._to_dense.default this one is getting called with csc
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if (
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func not in [
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torch.ops.aten.lift_fresh.default,
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torch.ops.aten.empty_like.default,
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torch.ops.aten.set_.source_Storage_storage_offset,
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torch.ops.aten.sspaddmm.out,
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torch.ops.aten._spdiags.default,
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torch.ops.aten._to_dense.default
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]
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and torch.Tag.dynamic_output_shape not in func.tags
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and torch.Tag.inplace_view not in func.tags
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):
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from torch._subclasses.fake_tensor import FakeTensorMode, UnsupportedFakeTensorException
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from torch.utils._pytree import tree_map
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try:
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with FakeTensorMode(allow_meta=True) as fake_mode:
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fake_args, fake_kwargs = tree_map(on_tensor(fake_mode.from_tensor), (args, kwargs))
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fake_r = func(*fake_args, **fake_kwargs)
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except UnsupportedFakeTensorException:
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pass
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r = func(*args, **kwargs)
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return r
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class TestSparseBase(TestCase):
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def run(self, result=None):
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if TEST_WITH_CROSSREF:
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with CrossRefSparseFakeMode():
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return super().run(result)
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else:
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return super().run(result)
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class TestSparse(TestSparseBase):
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def setUp(self):
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TestCase.setUp(self)
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self.index_tensor = lambda *args, **kwargs: torch.tensor(*args, **kwargs, dtype=torch.int64)
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def sparse_empty_factory(*args, **kwargs):
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kwargs['layout'] = kwargs.get('layout', torch.sparse_coo)
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return torch.empty(*args, **kwargs)
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self.sparse_empty = sparse_empty_factory
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def sparse_tensor_factory(*args, **kwargs):
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return torch.sparse_coo_tensor(*args, **kwargs)
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self.sparse_tensor = sparse_tensor_factory
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self.legacy_sparse_tensor = torch.sparse.DoubleTensor
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def _gen_sparse(self, sparse_dim, nnz, with_size, dtype, device, coalesced):
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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, not coalesced, dtype=dtype, device=device)
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if not coalesced:
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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 torch.empty(*args, **kwargs).normal_()
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@dtypes(torch.double)
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def test_print_coalesced(self, device, dtype):
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self._test_print(device, dtype, True)
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@dtypes(torch.double)
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def test_print_uncoalesced(self, device, dtype):
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self._test_print(device, dtype, False)
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def _test_print(self, device, dtype, coalesced):
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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.index_tensor(0).dtype,
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device=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 not coalesced 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=dtype,
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device=device).view(values_shape).div_(values_numel / 2.)
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sp_tensor = self.sparse_tensor(indices, values, shape, dtype=dtype, device=device)
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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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@coalescedonoff
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@dtypes(torch.double, torch.cdouble)
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def test_basic(self, device, dtype, coalesced):
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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, dtype, device, coalesced)
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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(x.coalesce()._nnz(), nnz if x.is_coalesced() else nnz // 2)
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self.assertEqual(list(x.size()), with_size)
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# Test .indices() and .values()
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if not coalesced:
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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.index_tensor([[9, 0, 0, 0, 8, 1, 1, 1, 2, 7, 2, 2, 3, 4, 6, 9]], device=device)
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v = torch.tensor([[idx**2, idx] for idx in range(i.size(1))], dtype=dtype, device=device)
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x = self.sparse_tensor(i, v, torch.Size([10, 2]), dtype=dtype, device=device)
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self.assertEqual(x.coalesce()._nnz(), 9)
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# Make sure we can access empty indices / values
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x = self.legacy_sparse_tensor()
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self.assertEqual(x._indices().numel(), 0)
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self.assertEqual(x._values().numel(), 0)
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@coalescedonoff
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@dtypes(torch.double, torch.cdouble, torch.bfloat16)
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@precisionOverride({torch.bfloat16: 1e-2})
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def test_coalesce(self, device, dtype, coalesced):
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def _test_coalesce(t):
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tc = t.coalesce()
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self.assertEqual(tc.to_dense(), t.to_dense())
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self.assertTrue(tc.is_coalesced())
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# Our code below doesn't work when nnz is 0, because
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# then it's a 0D tensor, not a 2D tensor.
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if t._nnz() == 0:
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self.assertEqual(t._indices(), tc._indices())
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self.assertEqual(t._values(), tc._values())
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return tc
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value_map: Dict[Any, Any] = {}
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for idx, val in zip(t._indices().t(), t._values()):
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idx_tup = tuple(idx.tolist())
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if idx_tup in value_map:
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value_map[idx_tup] += val
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else:
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value_map[idx_tup] = val.clone() if isinstance(val, torch.Tensor) else val
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new_indices = sorted(list(value_map.keys()))
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_new_values = [value_map[idx] for idx in new_indices]
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if t._values().ndimension() < 2:
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new_values = t._values().new(_new_values)
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else:
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new_values = torch.stack(_new_values)
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new_indices = t._indices().new(new_indices).t()
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tg = t.new(new_indices, new_values, t.size())
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self.assertEqual(tc._indices(), tg._indices())
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self.assertEqual(tc._values(), tg._values())
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if t.is_coalesced():
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self.assertEqual(tc._indices(), t._indices())
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self.assertEqual(tc._values(), t._values())
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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, dtype, device, coalesced)
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_test_coalesce(t) # this tests correctness
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@dtypes(torch.double)
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def test_coalesce_reference_cycle(self, device, dtype):
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# Test coalesce doesn't create autograd graph cycles (gh-52253)
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# Sanity check that the helper class works as expected
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t = torch.rand(2)
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t_ref = torch._C._WeakTensorRef(t)
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self.assertFalse(t_ref.expired())
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del t
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self.assertTrue(t_ref.expired())
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def test_sparse_sum():
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i = torch.tensor([[0], [4]], dtype=torch.long, device=device)
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v = torch.tensor([[[-0.4567, -1.8797, 0.0380, 1.4316]]],
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dtype=dtype, device=device)
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S = torch.sparse_coo_tensor(i, v)
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S = S.coalesce()
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S.requires_grad_(True)
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S2 = S.coalesce()
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self.assertTrue(S2.is_coalesced())
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return torch._C._WeakTensorRef(S2)
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ref = test_sparse_sum()
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self.assertTrue(ref.expired())
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@dtypes(torch.double)
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def test_ctor_large_sizes(self, device, dtype):
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# Test that integer overflow is detected when computing numel
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# of a sparse tensor with large dimensions (gh-57416). Notice
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# that numel is computed internally when constructing a
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# tensor, hence the overflow may appear during the tensor
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# construction step.
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N = 100000
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indices = torch.tensor([[N, N - 1]] * 4, dtype=torch.int64, device=device)
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values = torch.tensor([1, 2], dtype=dtype, device=device)
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self.assertRaises(RuntimeError,
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lambda: torch.sparse_coo_tensor(
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indices, values, (N + 1,) * 4, device=device))
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@dtypes(torch.double, torch.cdouble)
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def test_ctor_size_checks(self, device, dtype):
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indices = self.index_tensor([
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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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], device=device)
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values = torch.tensor([2, 1, 3, 4], dtype=dtype, device=device)
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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.sparse_tensor(indices, values, torch.Size([2, 1, 1])))
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# values inconsistent with size
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values = torch.tensor([
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[2, 1, 2, 1],
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[1, 0, 5, 2],
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], dtype=dtype, device=device)
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self.assertRaises(
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RuntimeError,
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lambda: self.sparse_tensor(indices, values, torch.Size([2, 4, 2, 1])))
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@dtypes(*floating_and_complex_types_and(torch.float16, torch.bfloat16))
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def test_to_dense(self, device, dtype):
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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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dense_x = x.to_dense()
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safe_dense_x = self.safeToDense(x)
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dense_x = dense_x.to(res.dtype)
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safe_dense_x = safe_dense_x.to(res.dtype)
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self.assertEqual(res, dense_x)
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self.assertEqual(res, safe_dense_x)
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# Only run autograd test for float64
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if x.dtype != torch.float64:
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return
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def fn(x):
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return x.to_dense()
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x.requires_grad_(True)
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gradcheck(fn, (x,), check_sparse_nnz=True)
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for value_type in [torch.double, torch.cdouble]:
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i = self.index_tensor([
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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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], device=device)
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# we don't have to_dense for half types on CPU because it is implemented
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# with a slower add_ operation
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v = torch.tensor([2, 1, 3, 4], dtype=dtype, device=device)
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x = self.sparse_tensor(i, v, torch.Size([3, 4, 5]), dtype=value_type, device=device)
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res = torch.tensor([
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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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], dtype=dtype, device=device)
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test_tensor(x, res)
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test_tensor(res, res)
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i = self.index_tensor([
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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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], device=device)
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v = torch.empty(4, 0, dtype=dtype, device=device)
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x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 0]), dtype=value_type, device=device)
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res = torch.empty((3, 4, 5, 0), dtype=dtype, device=device)
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test_tensor(x, res)
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@coalescedonoff
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@dtypes(torch.float16, torch.bfloat16, torch.float64, torch.int, torch.cfloat, torch.cdouble)
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def test_to_sparse(self, device, dtype, coalesced):
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shape = [5, 2, 10, 4]
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max_nnz = 1
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for value_type in [torch.double, torch.cdouble]:
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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, dtype=value_type, device=device,
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coalesced=coalesced)
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expected = expected.to(dtype)
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d = expected.to_dense()
|
|
result = d.to_sparse(dim)
|
|
self.assertEqual(d, result.to_dense())
|
|
self.assertEqual(expected.size(), result.size())
|
|
self.assertEqual(dim, result.sparse_dim())
|
|
|
|
sp, _, _ = self._gen_sparse(2, 10, [3, 3, 3], dtype=value_type, device=device, coalesced=coalesced)
|
|
self.assertRaises(RuntimeError, lambda: sp.to_sparse())
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_sparse_bool(self, device, dtype):
|
|
a = torch.tensor([True, False], dtype=dtype, device=device).to(torch.bool)
|
|
b = a.to_sparse().to_dense()
|
|
self.assertEqual(a, b)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_scalar(self, device, dtype):
|
|
# tensor with value
|
|
a = self.sparse_tensor(self.index_tensor([], device=device).unsqueeze(1), 12.3, [], dtype=dtype, device=device)
|
|
self.assertEqual(1, a._values().numel())
|
|
self.assertEqual(a, a.clone())
|
|
a_coalesced = a.coalesce()
|
|
self.assertTrue(a_coalesced.is_coalesced())
|
|
self.assertEqual(torch.tensor(12.3, dtype=dtype, device=device), a.to_dense())
|
|
self.assertEqual(a, a.to_dense().to_sparse())
|
|
|
|
# tensor with multiple values
|
|
a = self.sparse_tensor(self.index_tensor([], device=device).unsqueeze(1).expand(0, 2),
|
|
[12.3, 12.3], [], dtype=dtype, device=device)
|
|
self.assertEqual(2, a._values().numel())
|
|
self.assertEqual(a, a.clone())
|
|
a_coalesced = a.coalesce()
|
|
self.assertTrue(a_coalesced.is_coalesced())
|
|
self.assertEqual(torch.tensor(12.3 * 2, dtype=dtype, device=device), a.to_dense())
|
|
self.assertEqual(a.coalesce(), a.coalesce().to_dense().to_sparse())
|
|
|
|
# tensor without value
|
|
a = self.sparse_empty((), dtype=dtype, device=device)
|
|
self.assertEqual(0, a._values().numel())
|
|
self.assertEqual(a, a.clone())
|
|
a_coalesced = a.coalesce()
|
|
self.assertTrue(a_coalesced.is_coalesced())
|
|
self.assertEqual(torch.tensor(0, dtype=dtype, device=device), a.to_dense())
|
|
self.assertEqual(a, a.to_dense().to_sparse())
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_shared(self, device, dtype):
|
|
i = self.index_tensor([[2]], device=device)
|
|
v = torch.tensor([5], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3]))
|
|
v[0] = 6
|
|
self.assertEqual(torch.tensor([0, 0, 6], dtype=dtype, device=device), self.safeToDense(x))
|
|
i[0][0] = 0
|
|
self.assertEqual(torch.tensor([6, 0, 0], dtype=dtype, device=device), self.safeToDense(x))
|
|
|
|
i = self.index_tensor([[2]], device=device)
|
|
v = torch.empty((1, 0), dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 0]))
|
|
i[0][0] = 0
|
|
self.assertEqual(torch.empty((3, 0), dtype=dtype, device=device), self.safeToDense(x))
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_to_dense_hybrid(self, device, dtype):
|
|
def test_tensor(x, res):
|
|
x.to_dense() # Tests double to_dense for memory corruption
|
|
x.to_dense()
|
|
x.to_dense()
|
|
self.assertEqual(res, x.to_dense())
|
|
self.assertEqual(res, self.safeToDense(x))
|
|
|
|
def fn(x):
|
|
return x.to_dense()
|
|
x.requires_grad_(True)
|
|
gradcheck(fn, (x,), check_sparse_nnz=True)
|
|
|
|
i = self.index_tensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
], device=device)
|
|
v = torch.tensor([[2, 3], [1, 2], [3, 4], [4, 5]], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 2]))
|
|
res = torch.tensor([
|
|
[[2, 3],
|
|
[0, 0],
|
|
[0, 0],
|
|
[0, 0]],
|
|
[[1, 2],
|
|
[0, 0],
|
|
[0, 0],
|
|
[0, 0]],
|
|
[[3, 4],
|
|
[0, 0],
|
|
[0, 0],
|
|
[4, 5]],
|
|
], dtype=dtype, device=device)
|
|
test_tensor(x, res)
|
|
|
|
i = self.index_tensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
], device=device)
|
|
v = torch.empty((4, 2, 0), dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 2, 0]))
|
|
res = torch.empty((3, 4, 2, 0), dtype=dtype, device=device)
|
|
test_tensor(x, res)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_contig(self, device, dtype):
|
|
def test_tensor(x, exp_i, exp_v):
|
|
x = x.coalesce()
|
|
self.assertEqual(exp_i, x._indices())
|
|
self.assertEqual(exp_v, x._values())
|
|
|
|
i = self.index_tensor([
|
|
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
|
|
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
|
|
], device=device)
|
|
v = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([100, 100]))
|
|
exp_i = self.index_tensor([
|
|
[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
|
|
[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
|
|
], device=device)
|
|
exp_v = torch.tensor([2, 1, 6, 4, 10, 3, 5, 9, 8, 7], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.index_tensor([
|
|
[2, 0, 2, 1],
|
|
[0, 0, 3, 0],
|
|
[1, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.tensor([3, 2, 4, 1], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5]))
|
|
exp_i = self.index_tensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
[0, 0, 1, 4],
|
|
], device=device)
|
|
exp_v = torch.tensor([2, 1, 3, 4], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.index_tensor([
|
|
[2, 0, 2, 1],
|
|
[0, 0, 3, 0],
|
|
[1, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.empty([4, 0], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 0]))
|
|
exp_i = self.index_tensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
[0, 0, 1, 4],
|
|
], device=device)
|
|
exp_v = torch.empty([4, 0], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
# Duplicate indices
|
|
i = self.index_tensor([
|
|
[0, 0, 2, 0],
|
|
[0, 0, 3, 0],
|
|
[0, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.tensor([3, 2, 4, 1], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5]))
|
|
exp_i = self.index_tensor([
|
|
[0, 2],
|
|
[0, 3],
|
|
[0, 4],
|
|
], device=device)
|
|
exp_v = torch.tensor([6, 4], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.index_tensor([
|
|
[0, 0, 2, 0],
|
|
[0, 0, 3, 0],
|
|
[0, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.empty([4, 0], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 0]))
|
|
exp_i = self.index_tensor([
|
|
[0, 2],
|
|
[0, 3],
|
|
[0, 4],
|
|
], device=device)
|
|
exp_v = torch.empty([2, 0], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_contig_hybrid(self, device, dtype):
|
|
def test_tensor(x, exp_i, exp_v):
|
|
x = x.coalesce()
|
|
self.assertEqual(exp_i, x._indices())
|
|
self.assertEqual(exp_v, x._values())
|
|
|
|
i = self.index_tensor([
|
|
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
|
|
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
|
|
], device=device)
|
|
v = torch.tensor([
|
|
[1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
|
|
[6, 7], [7, 8], [8, 9], [9, 10], [10, 11],
|
|
], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([100, 100, 2]))
|
|
exp_i = self.index_tensor([
|
|
[0, 1, 6, 14, 27, 35, 39, 40, 66, 71],
|
|
[31, 92, 65, 50, 34, 62, 22, 56, 74, 89],
|
|
], device=device)
|
|
exp_v = torch.tensor([
|
|
[2, 3], [1, 2], [6, 7], [4, 5], [10, 11],
|
|
[3, 4], [5, 6], [9, 10], [8, 9], [7, 8],
|
|
], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.index_tensor([
|
|
[2, 0, 2, 1],
|
|
[0, 0, 3, 0],
|
|
[1, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.tensor([[3, 3, 3], [2, 2, 2], [4, 4, 4], [1, 1, 1]], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3]))
|
|
exp_i = self.index_tensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
[0, 0, 1, 4],
|
|
], device=device)
|
|
exp_v = torch.tensor([[2, 2, 2], [1, 1, 1], [3, 3, 3], [4, 4, 4]], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.index_tensor([
|
|
[2, 0, 2, 1],
|
|
[0, 0, 3, 0],
|
|
[1, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.empty([4, 3, 0], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3, 0]))
|
|
exp_i = self.index_tensor([
|
|
[0, 1, 2, 2],
|
|
[0, 0, 0, 3],
|
|
[0, 0, 1, 4],
|
|
], device=device)
|
|
exp_v = torch.empty([4, 3, 0], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
# Duplicate indices
|
|
i = self.index_tensor([
|
|
[0, 0, 2, 0],
|
|
[0, 0, 3, 0],
|
|
[0, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.tensor([[3, 2, 3], [2, 1, 1], [4, 3, 4], [1, 1, 1]], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3]))
|
|
exp_i = self.index_tensor([
|
|
[0, 2],
|
|
[0, 3],
|
|
[0, 4],
|
|
], device=device)
|
|
exp_v = torch.tensor([[6, 4, 5], [4, 3, 4]], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
i = self.index_tensor([
|
|
[0, 0, 2, 0],
|
|
[0, 0, 3, 0],
|
|
[0, 0, 4, 0],
|
|
], device=device)
|
|
v = torch.empty([4, 3, 0], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 4, 5, 3, 0]))
|
|
exp_i = self.index_tensor([
|
|
[0, 2],
|
|
[0, 3],
|
|
[0, 4],
|
|
], device=device)
|
|
exp_v = torch.empty([2, 3, 0], dtype=dtype, device=device)
|
|
test_tensor(x, exp_i, exp_v)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_clone(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, with_size):
|
|
x = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[0]
|
|
if not coalesced:
|
|
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])
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble, torch.bfloat16)
|
|
@precisionOverride({torch.bfloat16: 2e-2})
|
|
def test_Sparse_to_Sparse_copy_(self, device, dtype, coalesced):
|
|
# 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, dtype, device, coalesced)
|
|
x2, _, _ = self._gen_sparse(sparse_dims, nnz + 10, sizes, dtype, device, coalesced)
|
|
|
|
# test copy
|
|
x2_dense = x2.to_dense()
|
|
x1.copy_(x2)
|
|
self.assertEqual(x2_dense, x1.to_dense())
|
|
|
|
# test type conversion (when x1.copy_(x2), x1.dtype should stay the same)
|
|
x1 = x1.to(torch.float32)
|
|
|
|
x2 = x2.to(torch.float16)
|
|
x1_dtype = x1.dtype
|
|
x1.copy_(x2)
|
|
self.assertEqual(x1_dtype, x1.dtype)
|
|
|
|
x2 = x2.to(torch.float64)
|
|
x1_dtype = x1.dtype
|
|
x1.copy_(x2)
|
|
self.assertEqual(x1_dtype, x1.dtype)
|
|
|
|
# test no broadcast
|
|
self.assertRaises(RuntimeError, lambda: x1.copy_(x2.narrow_copy(0, 0, 1)))
|
|
|
|
# test raise error on copy_() between dense and sparse Tensors
|
|
self.assertRaises(RuntimeError, lambda: x1.copy_(torch.randn(5, 5)))
|
|
|
|
# test autograd
|
|
x1, _, _ = self._gen_sparse(sparse_dims, nnz, sizes, dtype, device, coalesced)
|
|
x2, _, _ = self._gen_sparse(sparse_dims, nnz + 10, sizes, dtype, device, coalesced)
|
|
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)
|
|
|
|
@coalescedonoff
|
|
@unittest.skipIf(torch.cuda.device_count() < 2, "no multi-GPU")
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_Sparse_to_Sparse_copy_multi_gpu(self, device, dtype, coalesced):
|
|
# 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, dtype, device, coalesced)
|
|
x2, _, _ = self._gen_sparse(sparse_dims, nnz + 10, sizes, dtype, device, coalesced)
|
|
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)
|
|
|
|
@onlyCUDA
|
|
def test_cuda_empty(self, device):
|
|
def test_tensor(x):
|
|
y = x.to(device)
|
|
self.assertEqual(x.sparse_dim(), y.sparse_dim())
|
|
self.assertEqual(x.dense_dim(), y.dense_dim())
|
|
x = y.cpu()
|
|
self.assertEqual(y.sparse_dim(), x.sparse_dim())
|
|
self.assertEqual(y.dense_dim(), x.dense_dim())
|
|
|
|
x = torch.sparse.FloatTensor(2, 3, 4)
|
|
test_tensor(x)
|
|
|
|
x = torch.sparse.HalfTensor(2, 3, 4)
|
|
test_tensor(x)
|
|
|
|
x = torch.cuda.sparse.HalfTensor(2, 3, 4)
|
|
test_tensor(x)
|
|
|
|
x = torch.sparse.FloatTensor(2, 3, 4, 0)
|
|
test_tensor(x)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_transpose(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, with_size):
|
|
x = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[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])
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_permute(self, device, dtype, coalesced):
|
|
# trivial checks
|
|
s = torch.rand(3, 3, 3, device=device, dtype=dtype).to_sparse()
|
|
with self.assertRaisesRegex(RuntimeError, "does not match the length"):
|
|
s.permute(dims=(1, 0))
|
|
with self.assertRaisesRegex(RuntimeError, "duplicate dims"):
|
|
s.permute(dims=(1, 1, 1))
|
|
|
|
def test_shape(sparse_dims, nnz, with_size):
|
|
ndim = len(with_size)
|
|
valid_sparse_dims = torch.arange(-ndim, -ndim + sparse_dims)
|
|
valid_dense_dims = torch.arange(-ndim + sparse_dims, 0)
|
|
|
|
for dims in itertools.permutations(range(-ndim, 0)):
|
|
s = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[0]
|
|
d = self.safeToDense(s)
|
|
|
|
dims_sparse, _ = torch.tensor(dims[:sparse_dims]).sort()
|
|
dims_dense, _ = torch.tensor(dims[sparse_dims:]).sort()
|
|
|
|
if (valid_sparse_dims == dims_sparse).all() and (valid_dense_dims == dims_dense).all():
|
|
# if valid permutation, test for correctness
|
|
s_permuted = s.permute(dims)
|
|
self.assertEqual(s_permuted, d.permute(dims))
|
|
|
|
# if s is coalesced, and perm does not touch 0-dim,
|
|
# the result has to be coalesced as well
|
|
if dims[0] == 0:
|
|
self.assertEqual(s_permuted.is_coalesced(), s.is_coalesced())
|
|
else:
|
|
self.assertFalse(s_permuted.is_coalesced())
|
|
|
|
gradcheck(lambda t: t.permute(dims).to_dense(), s.requires_grad_(True), check_sparse_nnz=True)
|
|
else:
|
|
# otherwise check if exception is thrown
|
|
fail_message = "transpositions between sparse and dense dimensions are not allowed"
|
|
with self.assertRaisesRegex(RuntimeError, fail_message):
|
|
s.permute(dims)
|
|
|
|
test_shape(2, 3, [2, 3, 4, 5])
|
|
test_shape(2, 3, [2, 2, 0])
|
|
# if nnz=0, it is not true that t == t.to_dense().to_sparse()
|
|
# unless t.sparse_dim == t.dim (i.e. t is not hybrid)
|
|
test_shape(3, 0, [0, 0, 2])
|
|
|
|
@coalescedonoff
|
|
@onlyCPU
|
|
@dtypes(torch.double)
|
|
def test_coalesce_transpose_mm(self, device, dtype, coalesced):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x, _, _ = self._gen_sparse(2, nnz, [dj, di], dtype, device, coalesced)
|
|
y = torch.randn(dj, dk, dtype=dtype, device=device)
|
|
|
|
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)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_t_empty(self, device, dtype):
|
|
def test_in_place(x):
|
|
shape_original = x.shape
|
|
x.t_()
|
|
self.assertEqual(torch.Size([shape_original[1], shape_original[0]]), x.size())
|
|
self.assertEqual(0, x._indices().numel())
|
|
self.assertEqual(0, x._values().numel())
|
|
self.assertEqual(x.sparse_dim(), 2)
|
|
self.assertEqual(x.dense_dim(), 0)
|
|
|
|
def test_not_in_place(x):
|
|
shape_original = x.shape
|
|
y = x.t()
|
|
self.assertEqual(torch.Size([shape_original[1], shape_original[0]]), y.size())
|
|
self.assertEqual(0, y._indices().numel())
|
|
self.assertEqual(0, y._values().numel())
|
|
self.assertEqual(x.sparse_dim(), 2)
|
|
self.assertEqual(x.dense_dim(), 0)
|
|
|
|
x = self.sparse_empty(2, 3, dtype=dtype, device=device)
|
|
test_in_place(x)
|
|
test_not_in_place(x)
|
|
|
|
x = self.sparse_empty(2, 0, dtype=dtype, device=device)
|
|
test_in_place(x)
|
|
test_not_in_place(x)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_add_zeros(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, sizes):
|
|
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes, dtype, device, coalesced)
|
|
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])
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_add_sub_nnz(self, device, dtype):
|
|
# nnz should not grow unbounded (gh-34964)
|
|
x = torch.randn(10, dtype=dtype, device=device).to_sparse()
|
|
x.add_(x)
|
|
x.add_(x)
|
|
self.assertLessEqual(x._nnz(), 10)
|
|
|
|
x.sub_(2 * x)
|
|
x.sub_(2 * x)
|
|
self.assertLessEqual(x._nnz(), 10)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_cat(self, device, dtype, coalesced):
|
|
# 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], dtype, device, coalesced)[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], dtype, device, coalesced)[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))
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_unsqueeze(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, sizes, unsqueeze_dim, fail_message=None):
|
|
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes, dtype, device, coalesced)
|
|
if fail_message:
|
|
with self.assertRaisesRegex(IndexError, fail_message):
|
|
torch.unsqueeze(x, unsqueeze_dim)
|
|
else:
|
|
result = torch.unsqueeze(x, unsqueeze_dim)
|
|
dense_result = torch.unsqueeze(x.to_dense(), unsqueeze_dim)
|
|
self.assertEqual(dense_result, result.to_dense())
|
|
|
|
# basic case
|
|
test_shape(3, 10, [5, 7, 11], 0)
|
|
|
|
# hybrid sparse/dense, unsqueeze along sparse dim
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], 0)
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], 3)
|
|
|
|
# unsqueeze along dense dimensions
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], 4)
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], 5)
|
|
|
|
# wrapped dimensions
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], -1)
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], -6)
|
|
|
|
# bounds
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], -7, "Dimension out of range")
|
|
test_shape(3, 10, [5, 7, 11, 13, 17], 6, "Dimension out of range")
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_select(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, sizes, select_dim, select_index, fail_message=None):
|
|
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes, dtype, device, coalesced)
|
|
if fail_message:
|
|
with self.assertRaisesRegex(IndexError, fail_message):
|
|
torch.select(x, select_dim, select_index)
|
|
else:
|
|
result = torch.select(x, select_dim, select_index)
|
|
if result.is_sparse:
|
|
result = result.to_dense()
|
|
dense_result = torch.select(x.to_dense(), select_dim, select_index)
|
|
self.assertEqual(dense_result, result)
|
|
|
|
|
|
sizes = [5, 7, 11, 13, 17]
|
|
# hybrid sparse/dense, select sparse dim, result is dense
|
|
for i in range(sizes[0]):
|
|
test_shape(1, 10, sizes, 0, i)
|
|
test_shape(1, 10, sizes, 0, sizes[0] + 1, r'select[(][)][:] index \d out of range.*')
|
|
|
|
# hybrid sparse/dense, select sparse dim, result is sparse
|
|
for d in range(3):
|
|
for i in range(sizes[d]):
|
|
test_shape(3, 10, sizes, d, i)
|
|
|
|
# hybrid sparse/dense, select dense dim, result is sparse
|
|
for d in range(1, 3):
|
|
for i in range(sizes[d]):
|
|
test_shape(1, 10, sizes, d, i)
|
|
|
|
@dtypes(*integral_types())
|
|
def test_select_no_type_promotion(self, device, dtype):
|
|
# see https://github.com/pytorch/pytorch/issues/82150
|
|
idx = torch.tensor([[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]])
|
|
val = torch.ones(6, dtype=dtype)
|
|
s = torch.sparse_coo_tensor(idx, val, size=(3, 3))
|
|
|
|
for t in (s, s * torch.tensor(0, dtype=dtype)):
|
|
# empty checks
|
|
self.assertEqual(t.dtype, t[2].dtype)
|
|
self.assertEqual(t.dtype, t[0, 1].dtype)
|
|
# sum should not promote
|
|
self.assertEqual(t.dtype, t[0, 0].dtype)
|
|
self.assertEqual(t.dtype, t[1, 1].dtype)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_index_select(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, sizes, select_dim, select_index, fail_message=None):
|
|
if isinstance(select_index, int):
|
|
select_index = [select_index]
|
|
if isinstance(select_index, list):
|
|
select_index = torch.tensor(select_index, device=device, dtype=torch.long)
|
|
x, _, _ = self._gen_sparse(sparse_dims, nnz, sizes, dtype, device, coalesced)
|
|
if fail_message:
|
|
with self.assertRaisesRegex(IndexError, fail_message):
|
|
torch.index_select(x, select_dim, select_index)
|
|
else:
|
|
result = torch.index_select(x, select_dim, select_index)
|
|
if result.is_sparse:
|
|
result = result.to_dense()
|
|
dense_result = torch.index_select(x.to_dense(), select_dim, select_index)
|
|
self.assertEqual(dense_result, result)
|
|
|
|
sizes = [5, 7, 11, 13, 17]
|
|
for d in range(len(sizes)):
|
|
for index in [0, sizes[d] - 1, [0, sizes[d] // 2, sizes[d] - 1]]:
|
|
test_shape(1, 10, sizes, d, index)
|
|
test_shape(len(sizes) // 2, 10, sizes, d, index)
|
|
test_shape(len(sizes), 10, sizes, d, index)
|
|
|
|
def _test_index_select_exhaustive_index(self, sizes, dims, device, dtype, coalesced):
|
|
t = make_tensor(sizes, dtype=dtype, device=device)
|
|
t_sparse = t.to_sparse().coalesce() if coalesced else t.to_sparse()
|
|
t_small_sparse, _, _ = self._gen_sparse(len(sizes), 2, sizes, dtype, device, coalesced)
|
|
t_small = t_small_sparse.to_dense()
|
|
for d in dims:
|
|
# NOTE: indices are negative
|
|
idx_dim_d_range = list(range(-sizes[d], 0))
|
|
for idx_len in range(sizes[d], sizes[d] + 1):
|
|
# creates all possible valid indices into dim d of lenght idx_len
|
|
for idx in itertools.product(*itertools.repeat(idx_dim_d_range, idx_len)):
|
|
t_idx = torch.tensor(idx, dtype=torch.long, device=device)
|
|
|
|
# NOTE: index_select for dense does not support negative indices,
|
|
# hence + sizes[d]. See https://github.com/pytorch/pytorch/issues/76347
|
|
|
|
# tests the nnz > sizes[d] branch
|
|
dense_result = t.index_select(d, t_idx + sizes[d])
|
|
sparse_result = t_sparse.index_select(d, t_idx)
|
|
self.assertEqual(dense_result, sparse_result)
|
|
|
|
# tests the nnz <= sizes[d] branch
|
|
small_dense_result = t_small.index_select(d, t_idx + sizes[d])
|
|
small_sparse_result = t_small_sparse.index_select(d, t_idx)
|
|
self.assertEqual(small_dense_result, small_sparse_result)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_index_select_exhaustive_index_small(self, device, dtype, coalesced):
|
|
# will trigger brute-force algo
|
|
self._test_index_select_exhaustive_index((3, 3, 4), range(3), device, dtype, coalesced)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_index_select_exhaustive_index_large(self, device, dtype, coalesced):
|
|
# will trigger more sophisticated algos
|
|
self._test_index_select_exhaustive_index((100, 50, 3, 3), (2, 3), device, dtype, coalesced)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_index_select_empty_and_non_contiguous_index(self, device, dtype, coalesced):
|
|
# empty index
|
|
idx_empty = torch.tensor([], dtype=torch.long, device=device)
|
|
t = make_tensor((5, 5), dtype=dtype, device=device)
|
|
res_dense = t.index_select(0, idx_empty)
|
|
res_sparse = t.to_sparse().index_select(0, idx_empty)
|
|
self.assertEqual(res_dense, res_sparse)
|
|
|
|
# non-contigous index
|
|
idx = torch.randint(low=0, high=5, size=(10, 2), device=device)[:, 0]
|
|
|
|
def run_test(sizes):
|
|
# case nnz > size[d]
|
|
t = make_tensor(sizes, dtype=dtype, device=device)
|
|
res_dense = t.index_select(0, idx)
|
|
res_sparse = t.to_sparse().index_select(0, idx)
|
|
self.assertEqual(res_dense, res_sparse)
|
|
|
|
# case nnz <= size[d]
|
|
t_small_sparse, _, _ = self._gen_sparse(len(sizes), 2, sizes, dtype, device, coalesced)
|
|
res_sparse = t_small_sparse.index_select(0, idx)
|
|
res_dense = t_small_sparse.to_dense().index_select(0, idx)
|
|
self.assertEqual(res_dense, res_sparse)
|
|
|
|
# brute-force
|
|
run_test((10, 10))
|
|
# more sophisticated algos
|
|
run_test((10, 100, 100))
|
|
|
|
@onlyCPU
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_index_select_parallelization(self, device, dtype, coalesced):
|
|
"""
|
|
Test with sizes that will trigger parallelization (i.e. with sizes
|
|
that are >= at::internal::GRAIN_SIZE)
|
|
"""
|
|
def run_test(nnz, size):
|
|
t_sparse, _, _ = self._gen_sparse(1, nnz, (size,), dtype, device, coalesced)
|
|
t_dense = t_sparse.to_dense()
|
|
|
|
# idx_small to (sort) and (binary) search into t_sparse
|
|
idx_small = torch.randint(size, (nnz // 2,), device=device)
|
|
# idx_large to (sort) and (binary) search into idx_large
|
|
# NOTE: when coalesced=True, the (binary) search will be
|
|
# done over t_sparse anyway, as it is already sorted.
|
|
idx_large = torch.randint(size, (nnz * 2,), device=device)
|
|
for idx in (idx_small, idx_large):
|
|
res_dense = t_dense.index_select(0, idx)
|
|
res_sparse = t_sparse.index_select(0, idx)
|
|
self.assertEqual(res_dense, res_sparse)
|
|
|
|
# NOTE: GRAIN_SIZE = 32768
|
|
# case nnz <= size[d]
|
|
tlen = 70000 # > 2 * GRAIN_SIZE
|
|
run_test(tlen, tlen)
|
|
|
|
# case nnz > size[d]
|
|
run_test(tlen, tlen // 2)
|
|
|
|
@onlyCPU
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_mm(self, device, dtype, coalesced):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x, _, _ = self._gen_sparse(2, nnz, [di, dj], dtype, device, coalesced)
|
|
t = torch.randn(di, dk, dtype=dtype, device=device)
|
|
y = torch.randn(dj, dk, dtype=dtype, device=device)
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
|
|
res = torch.addmm(t, x, y, beta=beta, alpha=alpha)
|
|
expected = torch.addmm(t, self.safeToDense(x), y, beta=beta, alpha=alpha)
|
|
self.assertEqual(res, expected)
|
|
|
|
res = torch.addmm(t, x, y)
|
|
expected = torch.addmm(t, self.safeToDense(x), y)
|
|
self.assertEqual(res, expected)
|
|
|
|
res = torch.mm(x, y)
|
|
expected = torch.mm(self.safeToDense(x), y)
|
|
self.assertEqual(res, expected)
|
|
|
|
test_shape(10, 100, 100, 20)
|
|
test_shape(100, 1000, 200, 20)
|
|
test_shape(64, 10000, 300, 20)
|
|
test_shape(0, 100, 100, 0)
|
|
test_shape(10, 0, 100, 0)
|
|
test_shape(10, 100, 0, 0)
|
|
test_shape(10, 100, 0, 20)
|
|
|
|
@unittest.skipIf(
|
|
IS_WINDOWS and TEST_CUDA,
|
|
"bmm sparse-dense CUDA is not yet supported in Windows, at least up to CUDA 10.1"
|
|
)
|
|
@unittest.skipIf(
|
|
TEST_CUDA and _get_torch_cuda_version() < (10, 1),
|
|
"bmm sparse-dense requires CUDA 10.1 or greater"
|
|
)
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_bmm(self, device, dtype, coalesced):
|
|
def test_shape(num_mats, dim_i, dim_j, dim_k, nnz):
|
|
a_list = []
|
|
b_list = []
|
|
for mat_idx in range(num_mats):
|
|
a_mat = self._gen_sparse(2, nnz, [dim_i, dim_j], dtype, device, coalesced)[0]
|
|
b_mat = torch.randn([dim_j, dim_k], dtype=dtype, device=device)
|
|
a_list.append(a_mat)
|
|
b_list.append(b_mat)
|
|
|
|
a = torch.stack(a_list)
|
|
b = torch.stack(b_list)
|
|
ab = a.bmm(b)
|
|
|
|
# Compare each matrix against result from mm()
|
|
for mat_idx in range(num_mats):
|
|
a_mat = a_list[mat_idx]
|
|
b_mat = b_list[mat_idx]
|
|
ab_mat_bmm = ab[mat_idx]
|
|
ab_mat_mm = a_mat.mm(b_mat)
|
|
self.assertEqual(ab_mat_bmm, ab_mat_mm)
|
|
|
|
test_shape(10, 10, 100, 99, 20)
|
|
test_shape(10, 100, 1000, 200, 20)
|
|
test_shape(10, 64, 10000, 300, 20)
|
|
test_shape(10, 0, 100, 99, 0)
|
|
test_shape(10, 10, 0, 100, 0)
|
|
test_shape(10, 10, 100, 0, 0)
|
|
test_shape(10, 10, 100, 0, 20)
|
|
test_shape(10, 10, 100, 0, 20)
|
|
|
|
a = torch.rand([10, 23, 32], dtype=dtype, device=device)
|
|
a[3] = torch.zeros(23, 32, dtype=dtype, device=device)
|
|
a[6] = torch.zeros(23, 32, dtype=dtype, device=device)
|
|
a = a.to_sparse()
|
|
b = torch.rand([10, 32, 10], dtype=dtype, device=device)
|
|
b[4] = torch.zeros(32, 10, dtype=dtype, device=device)
|
|
b[6] = torch.zeros(32, 10, dtype=dtype, device=device)
|
|
ab = a.bmm(b)
|
|
for mat_idx in range(ab.size(0)):
|
|
ab_mat = ab[mat_idx]
|
|
ab_mat_check = a[mat_idx].mm(b[mat_idx])
|
|
self.assertEqual(ab_mat, ab_mat_check)
|
|
|
|
ab_traspose_check = b.transpose(1, 2).to_sparse().bmm(
|
|
a.transpose(1, 2).to_dense()
|
|
).transpose(1, 2)
|
|
self.assertEqual(ab, ab_traspose_check)
|
|
|
|
@onlyCUDA
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
@unittest.skipIf(
|
|
IS_WINDOWS,
|
|
"bmm sparse-dense CUDA is not yet supported in Windows, at least up to CUDA 10.1"
|
|
)
|
|
@unittest.skipIf(
|
|
_get_torch_cuda_version() < (10, 1),
|
|
"bmm sparse-dense requires CUDA 10.1 or greater"
|
|
)
|
|
def test_bmm_deterministic(self, device, dtype, coalesced):
|
|
def test_shape(num_mats, dim_i, dim_j, dim_k, nnz):
|
|
a_list = []
|
|
b_list = []
|
|
for mat_idx in range(num_mats):
|
|
a_list.append(self._gen_sparse(2, nnz, [dim_i, dim_j], dtype, device, coalesced)[0])
|
|
b_list.append(torch.randn([dim_j, dim_k], dtype=dtype, device=device))
|
|
|
|
a = torch.stack(a_list).cuda()
|
|
b = torch.stack(b_list).cuda()
|
|
with DeterministicGuard(torch.are_deterministic_algorithms_enabled()):
|
|
torch.use_deterministic_algorithms(False)
|
|
ab_nondeterministic = torch.bmm(a, b)
|
|
torch.use_deterministic_algorithms(True)
|
|
ab_deterministic = torch.bmm(a, b)
|
|
diff_abs = (ab_deterministic - ab_nondeterministic).abs()
|
|
diff_rel = diff_abs / ab_deterministic.abs()
|
|
diff_rel[torch.isnan(diff_rel)] = 0
|
|
|
|
# deterministic and non-deterministic results should either be
|
|
# equal or within a small relative difference
|
|
equal_abs_or_rel = diff_abs.eq(0).logical_or(diff_rel.lt(0.001))
|
|
self.assertTrue(equal_abs_or_rel.all())
|
|
|
|
test_shape(10, 10, 100, 99, 20)
|
|
test_shape(10, 100, 1000, 200, 20)
|
|
test_shape(10, 64, 10000, 300, 20)
|
|
test_shape(10, 0, 100, 99, 0)
|
|
test_shape(10, 10, 0, 100, 0)
|
|
test_shape(10, 10, 100, 0, 0)
|
|
test_shape(10, 10, 100, 0, 20)
|
|
test_shape(10, 10, 100, 0, 20)
|
|
|
|
@onlyCUDA
|
|
@unittest.skipIf(
|
|
not IS_WINDOWS or _get_torch_cuda_version() >= (11, 0),
|
|
"this test ensures bmm sparse-dense CUDA gives an error when run on Windows with CUDA < 11.0"
|
|
)
|
|
@dtypes(torch.double)
|
|
def test_bmm_windows_error(self, device, dtype):
|
|
a = torch.rand(2, 2, 2, dtype=dtype).to_sparse().cuda()
|
|
b = torch.rand(2, 2, 2, dtype=dtype).cuda()
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"bmm sparse-dense CUDA is not supported on Windows with cuda before 11.0"):
|
|
ab = a.bmm(b)
|
|
|
|
@onlyCUDA
|
|
@skipIfRocm
|
|
@unittest.skipIf(
|
|
_get_torch_cuda_version() >= (10, 1),
|
|
"this test ensures bmm gives error if CUDA version is less than 10.1"
|
|
)
|
|
@dtypes(torch.double)
|
|
def test_bmm_cuda_version_error(self, device, dtype):
|
|
a = torch.rand(2, 2, 2, dtype=dtype).to_sparse().cuda()
|
|
b = torch.rand(2, 2, 2, dtype=dtype).cuda()
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"bmm sparse-dense requires CUDA 10.1 or greater"):
|
|
ab = a.bmm(b)
|
|
|
|
@onlyCPU
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_saddmm(self, device, dtype, coalesced):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x = self._gen_sparse(2, nnz, [di, dj], dtype, device, coalesced)[0]
|
|
t = self._gen_sparse(2, nnz, [di, dk], dtype, device, coalesced)[0]
|
|
y = torch.randn(dj, dk, dtype=dtype, device=device)
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
|
|
res = torch.saddmm(t, x, y, beta=beta, alpha=alpha)
|
|
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y, beta=beta, alpha=alpha)
|
|
self.assertEqual(self.safeToDense(res), expected)
|
|
|
|
res = torch.saddmm(t, x, y)
|
|
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y)
|
|
self.assertEqual(self.safeToDense(res), expected)
|
|
|
|
res = torch.smm(x, y)
|
|
expected = torch.mm(self.safeToDense(x), y)
|
|
self.assertEqual(self.safeToDense(res), expected)
|
|
|
|
test_shape(7, 5, 3, 20)
|
|
test_shape(1000, 100, 100, 20)
|
|
test_shape(3000, 64, 300, 20)
|
|
test_shape(0, 100, 100, 0)
|
|
test_shape(1000, 0, 100, 0)
|
|
test_shape(1000, 100, 0, 0)
|
|
|
|
@onlyCPU
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_sspaddmm(self, device, dtype, coalesced):
|
|
|
|
def test_shape(di, dj, dk, nnz):
|
|
x = self._gen_sparse(2, nnz, [di, dj], dtype, device, coalesced)[0]
|
|
t = self._gen_sparse(2, nnz, [di, dk], dtype, device, coalesced)[0]
|
|
y = torch.randn(dj, dk, dtype=dtype, device=device)
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
|
|
res = t.sspaddmm(x, y, beta=beta, alpha=alpha)
|
|
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y, beta=beta, alpha=alpha)
|
|
self.assertEqual(self.safeToDense(res), expected)
|
|
|
|
res = t.sspaddmm(x, y)
|
|
expected = torch.addmm(self.safeToDense(t), self.safeToDense(x), y)
|
|
self.assertEqual(self.safeToDense(res), expected)
|
|
|
|
test_shape(7, 5, 3, 20)
|
|
test_shape(1000, 100, 100, 20)
|
|
test_shape(3000, 64, 300, 20)
|
|
test_shape(0, 100, 100, 0)
|
|
test_shape(1000, 0, 100, 0)
|
|
test_shape(1000, 100, 0, 0)
|
|
|
|
# Test code from issue https://github.com/pytorch/pytorch/issues/45113
|
|
batch_size, input_size, hidden_size = 5, 3, 7
|
|
|
|
# Create coalesced sparse tensor with non-contiguous indices
|
|
weight = torch.randn(hidden_size, input_size, dtype=dtype, device=device).to_sparse()
|
|
self.assertTrue(weight.is_coalesced())
|
|
non_contig_indices = weight.indices().mT.contiguous().mT
|
|
weight = torch.sparse_coo_tensor(
|
|
indices=non_contig_indices, values=weight.values(), size=weight.shape)
|
|
weight._coalesced_(True)
|
|
self.assertFalse(weight._indices().is_contiguous())
|
|
# Create un/coalesced sparse tensor
|
|
bias = torch.randn((hidden_size, 1), dtype=dtype, device=device).to_sparse()
|
|
bias = torch.cat([bias] * batch_size, dim=1)
|
|
|
|
if coalesced:
|
|
bias = bias.coalesce()
|
|
|
|
x = torch.randn(input_size, batch_size, dtype=dtype, device=device)
|
|
res = bias.sspaddmm(weight, x)
|
|
|
|
true_result = (bias.to_dense() + torch.matmul(weight.to_dense(), x)).to_sparse()
|
|
self.assertEqual(self.safeToDense(res), self.safeToDense(true_result))
|
|
|
|
@coalescedonoff
|
|
@unittest.skip("See https://github.com/pytorch/pytorch/issues/73145")
|
|
@dtypes(torch.double, torch.cdouble, torch.bfloat16)
|
|
def test_sparse_addmm(self, device, dtype, coalesced):
|
|
def test_shape(m, n, p, nnz, broadcast, alpha_beta=None):
|
|
if alpha_beta is None:
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
else:
|
|
alpha, beta = alpha_beta
|
|
if broadcast:
|
|
D1 = make_tensor((), dtype=dtype, device=device, requires_grad=True)
|
|
else:
|
|
D1 = make_tensor([n, p], dtype=dtype, device=device, requires_grad=True)
|
|
D2 = make_tensor([m, p], dtype=dtype, device=device, requires_grad=True)
|
|
S = self._gen_sparse(2, nnz, [n, m], dtype, device, coalesced)[0]
|
|
S_dense = S.to_dense().requires_grad_(True)
|
|
S.requires_grad_(True)
|
|
Y = torch.sparse.addmm(D1, S, D2, beta=beta, alpha=alpha)
|
|
Y_dense = torch.addmm(D1, S_dense, D2, beta=beta, alpha=alpha)
|
|
self.assertEqual(Y, Y_dense)
|
|
|
|
def fn(S, D1, D2, beta=beta, alpha=alpha):
|
|
return torch.sparse.addmm(D1, S, D2, beta=beta, alpha=alpha)
|
|
gradcheck(fn, (S, D1, D2), check_sparse_nnz=True)
|
|
|
|
test_shape(7, 8, 9, 20, False, None)
|
|
test_shape(7, 8, 9, 20, True, None)
|
|
test_shape(7, 8, 9, 20, False, (1, 0))
|
|
test_shape(7, 8, 9, 20, True, (1, 0))
|
|
test_shape(7, 8, 9, 20, False, (1, 1))
|
|
test_shape(7, 8, 9, 20, True, (1, 1))
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_sparse_mm(self, device, dtype, coalesced):
|
|
def test_shape(d1, d2, d3, nnz, transposed):
|
|
if transposed:
|
|
D = torch.randn(d3, d2, dtype=dtype,
|
|
device=device).t_().requires_grad_(True)
|
|
else:
|
|
D = torch.randn(d2, d3, dtype=dtype, device=device).requires_grad_(True)
|
|
S = self._gen_sparse(2, nnz, [d1, d2], dtype, device, coalesced)[0]
|
|
S_dense = S.to_dense().requires_grad_(True)
|
|
S.requires_grad_(True)
|
|
self.assertEqual(torch.sparse.mm(S, D), torch.mm(S_dense, D))
|
|
|
|
def fn(S, D):
|
|
return torch.sparse.mm(S, D)
|
|
gradcheck(fn, (S, D), check_sparse_nnz=True)
|
|
|
|
test_shape(7, 8, 9, 20, False)
|
|
test_shape(7, 8, 9, 20, True)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_sparse_mul(self, device, dtype, coalesced):
|
|
# https://github.com/pytorch/pytorch/issues/79914
|
|
a = torch.tensor([[0., 1]], dtype=dtype, device=device).to_sparse().requires_grad_(True)
|
|
b = torch.tensor([[0., 1]], dtype=dtype, device=device).to_sparse().requires_grad_(True)
|
|
gradcheck(lambda x, y: torch.sparse.sum(x * y).to_dense(), [a, b], check_sparse_nnz=True)
|
|
|
|
def test_shape(sparse_dims, nnz, with_shape):
|
|
a = self._gen_sparse(sparse_dims, nnz, with_shape, dtype, device, coalesced)[0].requires_grad_(True)
|
|
b = self._gen_sparse(sparse_dims, nnz, with_shape, dtype, device, coalesced)[0].requires_grad_(True)
|
|
|
|
self.assertEqual((a * b).to_dense(), a.to_dense() * b.to_dense())
|
|
gradcheck(lambda x, y: (x * y).to_dense(), [a, b], check_sparse_nnz=True)
|
|
# Issues with 0-dim indices/values
|
|
gradcheck(lambda x, y: torch.sparse.sum(x * y).to_dense(), [a, b], check_sparse_nnz=True)
|
|
|
|
# TODO: Re-enable these
|
|
# test_shape(2, 3, [2, 3, 4, 5])
|
|
# test_shape(2, 3, [2, 2, 0])
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_dsmm(self, device, dtype, coalesced):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x = self._gen_sparse(2, nnz, [di, dj], dtype, device, coalesced)[0]
|
|
y = self.randn(dj, dk, dtype=dtype, device=device)
|
|
|
|
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)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_hsmm(self, device, dtype, coalesced):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x = self._gen_sparse(2, nnz, [di, dj], dtype, device, coalesced)[0]
|
|
y = self.randn(dj, dk, dtype=dtype, device=device)
|
|
|
|
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)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_spadd(self, device, dtype, coalesced):
|
|
|
|
def _test_spadd_shape(nnz, shape_i, shape_v=None):
|
|
shape = shape_i + (shape_v or [])
|
|
x, _, _ = self._gen_sparse(len(shape_i), nnz, shape, dtype, device, coalesced)
|
|
y = self.randn(*shape, dtype=dtype, device=device)
|
|
r = random.random()
|
|
|
|
res = torch.add(y, x, alpha=r)
|
|
expected = y + r * self.safeToDense(x)
|
|
|
|
self.assertEqual(res, expected)
|
|
|
|
# Non contiguous dense tensor
|
|
s = list(shape)
|
|
s[0] = shape[-1]
|
|
s[-1] = shape[0]
|
|
y = self.randn(*s, dtype=dtype, device=device)
|
|
y.transpose_(0, len(s) - 1)
|
|
r = random.random()
|
|
|
|
res = torch.add(y, x, alpha=r)
|
|
expected = y + r * self.safeToDense(x)
|
|
|
|
self.assertEqual(res, expected)
|
|
|
|
x, i, v = self._gen_sparse(len(shape_i), nnz, shape, dtype, device, coalesced)
|
|
nnz = i.size(1)
|
|
|
|
# Non contiguous sparse indices tensor
|
|
x_ = self.sparse_tensor(i[:, ::2], v[:(nnz + 1) // 2], x.shape, dtype=dtype, device=device)
|
|
res = torch.add(y, x_, alpha=r)
|
|
expected = y + r * self.safeToDense(x_)
|
|
self.assertEqual(res, expected)
|
|
|
|
# Non contiguous sparse values tensor
|
|
|
|
x_ = self.sparse_tensor(i[:, :(nnz + 1) // 2], v[::2], x.shape, dtype=dtype, device=device)
|
|
res = torch.add(y, x_, alpha=r)
|
|
expected = y + r * self.safeToDense(x_)
|
|
self.assertEqual(res, expected)
|
|
|
|
# Non contiguous sparse indices and values tensors
|
|
x_ = self.sparse_tensor(i[:, 1::2], v[1::2], x.shape, dtype=dtype, device=device)
|
|
res = torch.add(y, x_, alpha=r)
|
|
expected = y + r * self.safeToDense(x_)
|
|
self.assertEqual(res, expected)
|
|
|
|
def _test_spadd():
|
|
_test_spadd_shape(10, [5, 6])
|
|
_test_spadd_shape(10, [10, 10, 10])
|
|
_test_spadd_shape(10, [50, 30, 20])
|
|
_test_spadd_shape(10, [5, 5, 5, 5, 5, 5])
|
|
_test_spadd_shape(0, [0, 30, 20])
|
|
_test_spadd_shape(0, [50, 0, 20])
|
|
_test_spadd_shape(0, [50, 30, 0])
|
|
|
|
def _test_spadd_hybrid():
|
|
_test_spadd_shape(10, [5, 6], [2, 3])
|
|
_test_spadd_shape(10, [10, 10, 10], [3])
|
|
_test_spadd_shape(10, [50, 30, 20], [2])
|
|
_test_spadd_shape(10, [5, 5, 5, 5, 5, 5], [2])
|
|
_test_spadd_shape(0, [0, 30, 20], [2, 0])
|
|
_test_spadd_shape(0, [50, 0, 20], [2, 0])
|
|
_test_spadd_shape(0, [50, 30, 0], [2, 0])
|
|
_test_spadd_shape(10, [50, 30, 20], [2, 0])
|
|
|
|
_test_spadd()
|
|
_test_spadd_hybrid()
|
|
|
|
@onlyCUDA
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_sparse_add_out_bfloat16(self, device, dtype, coalesced):
|
|
# fp32
|
|
x, _, _ = self._gen_sparse(3, 5, 10, dtype, device, coalesced)
|
|
y, _, _ = self._gen_sparse(3, 5, 10, dtype, device, coalesced)
|
|
x = x.float().cuda()
|
|
y = y.float().cuda()
|
|
res_fp32 = torch.add(x, y)
|
|
|
|
# bfloat16
|
|
x = x.bfloat16()
|
|
y = y.bfloat16()
|
|
res_bf16 = torch.add(x, y)
|
|
res_bf16 = res_bf16.float() # to compare with reference
|
|
self.assertEqual(res_fp32, res_bf16, atol=1e-2, rtol=0)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_norm(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, with_size):
|
|
x, _, _ = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)
|
|
y = x.coalesce()
|
|
self.assertEqual(x.norm(), y._values().norm())
|
|
|
|
test_shape(3, 10, 100)
|
|
test_shape(4, 10, [100, 100, 100, 5, 5, 5, 0])
|
|
test_shape(4, 0, [0, 0, 100, 5, 5, 5, 0])
|
|
|
|
# Unsupported arguments should error
|
|
kwarg_error_pairs = [
|
|
({'keepdim': True},
|
|
RuntimeError, r'norm_sparse currently does not support keepdim=True'),
|
|
({'dim': 0},
|
|
RuntimeError, r'norm_sparse currently only supports full reductions'),
|
|
({'dtype': torch.double, 'p': 'fro'},
|
|
ValueError, r'dtype argument is not supported in frobenius norm'),
|
|
({'dtype': torch.double, 'p': 0},
|
|
RuntimeError, r"norm_sparse currently does not support 'dtype' argument")
|
|
]
|
|
x = self._gen_sparse(3, 10, 100, dtype, device, coalesced)[0]
|
|
for kwargs, err, msg in kwarg_error_pairs:
|
|
with self.assertRaisesRegex(err, msg):
|
|
x.norm(**kwargs)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
@unittest.skipIf(TEST_WITH_CROSSREF, "fallback triggers cuda device error")
|
|
def test_sparse_sum(self, device, dtype, coalesced):
|
|
|
|
def run_tests(S, td=None):
|
|
D = S.coalesce().to_dense().detach().requires_grad_(True)
|
|
if td is None:
|
|
S_sum = torch.sparse.sum(S)
|
|
D_sum = D.sum()
|
|
self.assertEqual(S_sum.item(), D_sum.item())
|
|
|
|
def fn(S):
|
|
res = torch.sparse.sum(S)
|
|
if res.is_sparse:
|
|
res = res.to_dense()
|
|
return res
|
|
gradcheck(fn, (S,), check_sparse_nnz=True)
|
|
else:
|
|
S_sum = torch.sparse.sum(S, td)
|
|
D_sum = D.sum(td)
|
|
self.assertEqual(S_sum.to_dense() if S_sum.is_sparse else S_sum, D_sum)
|
|
|
|
def fn(S):
|
|
res = torch.sparse.sum(S, td)
|
|
if res.is_sparse:
|
|
res = res.to_dense()
|
|
return res
|
|
gradcheck(fn, (S,), check_sparse_nnz=True)
|
|
|
|
nnz = 10
|
|
sparse_dims = 2
|
|
with_size = [5, 5, 1, 4] # use a dense dim = 1 to test for squeeze
|
|
test_dims = []
|
|
for i in range(1, 5):
|
|
test_dims += itertools.combinations(range(len(with_size)), i)
|
|
|
|
# https://github.com/pytorch/pytorch/issues/16501
|
|
x = torch.tensor([[1., 0., 0., 1.],
|
|
[0., 1., 0., 0.],
|
|
[0., 1., 1., 0.],
|
|
[0., 1., 0., 2.]], dtype=dtype, device=device).to_sparse()
|
|
self.assertEqual(torch.sparse.sum(x, dim=0), torch.sparse.sum(x, dim=-2))
|
|
self.assertEqual(torch.sum(x.to_dense(), dim=0), torch.sparse.sum(x, dim=0).to_dense())
|
|
|
|
# not support SparseTensor.sum()
|
|
S = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[0]
|
|
self.assertRaises(RuntimeError, lambda: S.sum())
|
|
|
|
# dim out of range
|
|
self.assertRaises(IndexError, lambda: torch.sparse.sum(S, 5))
|
|
|
|
# dim 0 appears multiple times in the list of dims
|
|
self.assertRaises(RuntimeError, lambda: torch.sparse.sum(S, [0, 0]))
|
|
|
|
# sum an empty tensor
|
|
empty_S = torch.sparse_coo_tensor(size=with_size, dtype=dtype, device=device)
|
|
self.assertEqual(torch.sparse.sum(empty_S, [0]).to_dense(), torch.sum(empty_S.to_dense(), [0]))
|
|
self.assertEqual(torch.sparse.sum(empty_S), torch.tensor(0, dtype=dtype, device=device))
|
|
empty_S.requires_grad_(True)
|
|
empty_S_sum = torch.sparse.sum(empty_S)
|
|
empty_S_sum.backward()
|
|
self.assertEqual(empty_S.grad.to_dense(), empty_S.clone().detach().to_dense())
|
|
|
|
# test values().sum()
|
|
S = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[0]
|
|
run_tests(S.requires_grad_(True))
|
|
|
|
for test_dim in test_dims:
|
|
S = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[0]
|
|
run_tests(S.requires_grad_(True), test_dim)
|
|
|
|
def _test_basic_ops_shape(self, nnz_x1, nnz_x2, shape_i, shape_v, dtype, device, coalesced):
|
|
shape = shape_i + (shape_v)
|
|
x1, _, _ = self._gen_sparse(len(shape_i), nnz_x1, shape, dtype, device, coalesced)
|
|
x2, _, _ = self._gen_sparse(len(shape_i), nnz_x2, shape, dtype, device, coalesced)
|
|
|
|
y1 = x1 + x2
|
|
y2 = x1.clone()
|
|
y2.add_(x2)
|
|
expected = self.safeToDense(x1) + self.safeToDense(x2)
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
y1 = x1 - x2
|
|
y2 = x1.clone()
|
|
y2.sub_(x2)
|
|
expected = self.safeToDense(x1) - self.safeToDense(x2)
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
y1 = x1 * x2
|
|
y2 = x1.clone()
|
|
y2.mul_(x2)
|
|
expected = self.safeToDense(x1) * self.safeToDense(x2)
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
y1 = x1 * 37.5
|
|
y2 = x1.clone()
|
|
y2.mul_(37.5)
|
|
expected = self.safeToDense(x1) * 37.5
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
y1 = x1 / 37.5
|
|
y2 = x1.clone()
|
|
y2.div_(37.5)
|
|
expected = self.safeToDense(x1) / 37.5
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
y1 = x1 // 37.5
|
|
y2 = x1.clone()
|
|
y2.floor_divide_(37.5)
|
|
expected = self.safeToDense(x1) // 37.5
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
# TODO: add back inplace support
|
|
y1 = x1 ** 2
|
|
y2 = x1.clone()
|
|
y2 = y2.pow(2)
|
|
expected = self.safeToDense(x1) ** 2
|
|
self.assertEqual(self.safeToDense(y1), expected)
|
|
self.assertEqual(self.safeToDense(y2), expected)
|
|
|
|
y = x1.clone()
|
|
y.zero_()
|
|
expected = torch.zeros(x1.size(), dtype=dtype, device=device)
|
|
self.assertEqual(self.safeToDense(y), expected)
|
|
|
|
self.assertEqual(x1.is_coalesced(), coalesced)
|
|
y = x1.coalesce()
|
|
z = x1.coalesce()
|
|
self.assertEqual(x1.is_coalesced(), coalesced)
|
|
self.assertTrue(y.is_coalesced())
|
|
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())
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_basic_ops(self, device, dtype, coalesced):
|
|
|
|
def _test_basic_ops():
|
|
self._test_basic_ops_shape(9, 12, [5, 6], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [50, 30, 20], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [5, 5, 5, 5, 5, 5], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 12, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 0, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 0, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 0, [10, 10, 0], [], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 0, [], [], dtype, device, coalesced)
|
|
|
|
def _test_basic_ops_hybrid():
|
|
self._test_basic_ops_shape(9, 12, [5, 6], [2, 3], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [10, 10, 10], [3], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [50, 30, 20], [2], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [5, 5, 5, 5, 5, 5], [2], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 12, [10, 10, 10], [2], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 0, [10, 10, 10], [2], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 0, [10, 10, 10], [2], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 12, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 12, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(9, 0, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 0, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_basic_ops_shape(0, 0, [10, 10, 0], [2, 0], dtype, device, coalesced)
|
|
|
|
_test_basic_ops()
|
|
_test_basic_ops_hybrid()
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_add_dense_sparse_mismatch(self, device, dtype):
|
|
def test_shape(dense_size, sparse_dims_shape, dense_dims_shape, sparse_size):
|
|
x = torch.zeros(dense_size, dtype=dtype, device=device)
|
|
sparse_y = self.sparse_tensor(torch.zeros(sparse_dims_shape, dtype=torch.int64, device=device),
|
|
torch.randn(dense_dims_shape, dtype=dtype, device=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])
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_add_noncontiguous(self, device, dtype):
|
|
indices = self.index_tensor([[1, 2], [0, 2]], device=device)
|
|
values = torch.tensor([1.], dtype=dtype, device=device).expand(2, 3, 4, 5)
|
|
x = self.sparse_tensor(indices, values, dtype=dtype, device=device)
|
|
assert not x._values().is_contiguous()
|
|
y = x + x
|
|
expected = self.safeToDense(x) + self.safeToDense(x)
|
|
self.assertEqual(self.safeToDense(y), expected)
|
|
|
|
def _test_sparse_mask_shape(self, nnz_x1, nnz_x2, shape_i, shape_v, dtype, device, coalesced):
|
|
shape = shape_i + (shape_v or [])
|
|
x1, _, _ = self._gen_sparse(len(shape_i), nnz_x1, shape, dtype, device, coalesced)
|
|
x2, _, _ = self._gen_sparse(len(shape_i), nnz_x2, shape, dtype, device, coalesced)
|
|
|
|
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)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_sparse_mask(self, device, dtype, coalesced):
|
|
def _test_sparse_mask_fixed():
|
|
i = self.index_tensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
], device=device)
|
|
v = torch.tensor([1, 2, 3, 4], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([5, 4]), dtype=dtype, device=device).coalesce()
|
|
dense = torch.tensor([
|
|
[1, 2, 3, 4],
|
|
[5, 6, 7, 8],
|
|
[9, 10, 11, 12],
|
|
[13, 14, 15, 16],
|
|
[17, 18, 19, 20],
|
|
], dtype=dtype, device=device)
|
|
exp_v = torch.tensor([7, 14, 3, 20], dtype=dtype, device=device)
|
|
res = dense.sparse_mask(x)
|
|
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4]), dtype=dtype, device=device)
|
|
self.assertEqual(res.coalesce(), expected.coalesce())
|
|
|
|
i = self.index_tensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
], device=device)
|
|
v = torch.empty([4, 0], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([5, 4, 0])).coalesce()
|
|
dense = torch.empty([5, 4, 0], dtype=dtype, device=device)
|
|
exp_v = torch.empty([4, 0], dtype=dtype, device=device)
|
|
res = dense.sparse_mask(x)
|
|
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4, 0]), dtype=dtype, device=device)
|
|
self.assertEqual(res.coalesce(), expected.coalesce())
|
|
|
|
_test_sparse_mask_fixed()
|
|
|
|
self._test_sparse_mask_shape(9, 12, [5, 6], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [50, 30, 20], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [5, 5, 5, 5, 5, 5], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 12, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 0, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 0, [10, 10, 10], [], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 0, [10, 10, 0], [], dtype, device, coalesced)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_sparse_mask_hybrid(self, device, dtype, coalesced):
|
|
def _test_sparse_mask_hybrid_fixed():
|
|
i = self.index_tensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
])
|
|
v = torch.tensor([[1, 2], [2, 3], [3, 4], [4, 5]])
|
|
# TODO: This is also testing that, if coalesce is a no-op,
|
|
# the indices don't get permuted. I don't know if we actually
|
|
# want to give this invariant.
|
|
x = self.sparse_tensor(i, v, torch.Size([5, 4, 2])).coalesce()
|
|
dense = torch.tensor([
|
|
[[1, 3], [2, 2], [3, 3], [4, 2]],
|
|
[[5, 7], [6, 7], [7, 9], [8, 9]],
|
|
[[9, 2], [10, 4], [11, 1], [12, 3]],
|
|
[[13, 5], [14, 1], [15, 1], [16, 6]],
|
|
[[17, 7], [18, 2], [19, 7], [20, 1]],
|
|
])
|
|
res = dense.sparse_mask(x)
|
|
exp_v = torch.tensor([[7, 9], [14, 1], [3, 3], [20, 1]])
|
|
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4, 2]))
|
|
self.assertEqual(res.coalesce(), expected.coalesce())
|
|
|
|
i = self.index_tensor([
|
|
[1, 3, 0, 4],
|
|
[2, 1, 2, 3],
|
|
])
|
|
v = torch.empty(4, 2, 0)
|
|
x = self.sparse_tensor(i, v, torch.Size([5, 4, 2, 0])).coalesce()
|
|
dense = torch.empty(5, 4, 2, 0)
|
|
res = dense.sparse_mask(x)
|
|
exp_v = torch.empty(4, 2, 0)
|
|
expected = self.sparse_tensor(i, exp_v, torch.Size([5, 4, 2, 0]))
|
|
self.assertEqual(res.coalesce(), expected.coalesce())
|
|
|
|
_test_sparse_mask_hybrid_fixed()
|
|
|
|
self._test_sparse_mask_shape(9, 12, [5, 6], [2, 3], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [10, 10, 10], [3], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [50, 30, 20], [2], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [5, 5, 5, 5, 5, 5], [2], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 12, [10, 10, 10], [2], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 0, [10, 10, 10], [2], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 0, [10, 10, 10], [2], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 12, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 12, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(9, 0, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 0, [10, 10, 10], [2, 0], dtype, device, coalesced)
|
|
self._test_sparse_mask_shape(0, 0, [10, 10, 0], [2, 0], dtype, device, coalesced)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_zeros(self, device, dtype, coalesced):
|
|
def _test_zeros(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, dtype, device, coalesced)
|
|
torch.zeros(*shape, out=out, dtype=dtype, device=device)
|
|
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_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):
|
|
_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])
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_zeros_like(self, device, dtype, coalesced):
|
|
def _test_zeros_like(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, dtype, device, coalesced)
|
|
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_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):
|
|
_test_zeros_like(nnzs, i_shapes[:i_dim], v_shapes[:v_dim])
|
|
test_shape([2, 3, 4], [3, 4, 5, 6], [9, 12])
|
|
test_shape([0, 3, 4], [3, 4, 5, 6], [0])
|
|
test_shape([2, 3, 4], [0, 4, 5, 6], [9, 12])
|
|
test_shape([2, 3, 4], [3, 4, 5, 6], [9, 12])
|
|
test_shape([0, 3, 4], [3, 4, 5, 6], [0])
|
|
test_shape([2, 3, 4], [0, 4, 5, 6], [9, 12])
|
|
|
|
sparse_tensor, _, _ = self._gen_sparse(len([2, 3]), 9, [2, 3] + [5, 6], dtype, device, coalesced)
|
|
data = (sparse_tensor, sparse_tensor, sparse_tensor, sparse_tensor.unsqueeze(0))
|
|
mem_formats = [torch.channels_last, torch.contiguous_format, torch.preserve_format, torch.channels_last_3d]
|
|
for x, mem_format in zip(data, mem_formats):
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "memory format option is only supported by strided tensors"):
|
|
result = torch.zeros_like(x, memory_format=mem_format)
|
|
|
|
result = torch.zeros_like(x, layout=torch.strided, memory_format=mem_format)
|
|
self.assertTrue(result.layout == torch.strided)
|
|
|
|
dense_tensor = sparse_tensor.to_dense()
|
|
result = torch.zeros_like(dense_tensor, layout=torch.sparse_coo)
|
|
self.assertEqual(dense_tensor.shape, result.shape)
|
|
self.assertEqual(result.layout, torch.sparse_coo)
|
|
|
|
sparse_zeros = torch.zeros(dense_tensor.shape, layout=torch.sparse_coo)
|
|
self.assertEqual(result._indices().shape, sparse_zeros._indices().shape)
|
|
self.assertEqual(result._values().shape, sparse_zeros._values().shape)
|
|
|
|
def _assert_sparse_invars(self, t):
|
|
# SparseTensor has the following invariants:
|
|
# - sparse_dim + dense_dim = len(SparseTensor.shape)
|
|
# - SparseTensor._indices().shape = (sparse_dim, nnz)
|
|
# - SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])
|
|
self.assertEqual(t.sparse_dim() + t.dense_dim(), len(t.shape))
|
|
self.assertEqual(tuple(t._indices().shape), (t.sparse_dim(), t._nnz()))
|
|
self.assertEqual(tuple(t._values().shape), (t._nnz(), ) + t.shape[t.sparse_dim():])
|
|
|
|
def _test_empty_like(self, sparse_tensor, dtype, device, coalesced):
|
|
|
|
result = torch.empty_like(sparse_tensor)
|
|
self.assertTrue(result.is_sparse)
|
|
self._assert_sparse_invars(result)
|
|
self.assertEqual(result.shape, sparse_tensor.shape)
|
|
self.assertEqual(result.dtype, sparse_tensor.dtype)
|
|
self.assertEqual(result.device, sparse_tensor.device)
|
|
self.assertEqual(result.sparse_dim(), sparse_tensor.sparse_dim())
|
|
self.assertEqual(result.dense_dim(), sparse_tensor.dense_dim())
|
|
|
|
sparse_tensor, _, _ = self._gen_sparse(len([2, 3]), 9, [2, 3] + [5, 6], dtype, device, coalesced)
|
|
data = (sparse_tensor, sparse_tensor, sparse_tensor, sparse_tensor.unsqueeze(0))
|
|
mem_formats = [torch.channels_last, torch.contiguous_format, torch.preserve_format, torch.channels_last_3d]
|
|
for x, mem_format in zip(data, mem_formats):
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "memory format option is only supported by strided tensors"):
|
|
result = torch.empty_like(x, memory_format=mem_format)
|
|
|
|
result = torch.empty_like(x, layout=torch.strided, memory_format=mem_format)
|
|
self.assertTrue(result.layout == torch.strided)
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, r"Could not run 'aten::empty_strided' with arguments from the 'Sparse(CPU|CUDA)' backend"
|
|
):
|
|
dense_tensor = sparse_tensor.to_dense()
|
|
result = torch.empty_like(dense_tensor, layout=torch.sparse_coo)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_empty_like(self, device, dtype, coalesced):
|
|
# tests https://github.com/pytorch/pytorch/issues/43699
|
|
|
|
if coalesced:
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0, 1, 2]]),
|
|
values=torch.tensor([3.0, -4.0, 5.0]),
|
|
size=[3, ],
|
|
dtype=dtype,
|
|
device=device
|
|
).coalesce()
|
|
self._test_empty_like(input_coalesced, dtype, device, coalesced)
|
|
|
|
# hybrid sparse input
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[1, 3], [2, 4]]),
|
|
values=torch.tensor([[-1.0, 3.0], [-5.0, 7.0]]),
|
|
size=[4, 5, 2],
|
|
dtype=dtype,
|
|
device=device
|
|
).coalesce()
|
|
self._test_empty_like(input_coalesced, dtype, device, coalesced)
|
|
|
|
if not coalesced:
|
|
# test uncoalesced input
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
|
|
values=torch.tensor([2.0, -3.0, -4.0, 1.0, -1.0, 1.5]),
|
|
size=[3, ],
|
|
dtype=dtype,
|
|
device=device
|
|
)
|
|
self._test_empty_like(input_uncoalesced, dtype, device, coalesced)
|
|
|
|
# test on empty sparse tensor
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.zeros([2, 0]),
|
|
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
|
|
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
|
|
dtype=dtype,
|
|
device=device
|
|
)
|
|
self._test_empty_like(input_uncoalesced, dtype, device, coalesced)
|
|
|
|
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]
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_narrow(self, device, dtype, coalesced):
|
|
shape = [3, 3, 4, 2]
|
|
input, _, _ = self._gen_sparse(4, 19, shape, dtype, device, coalesced)
|
|
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, dtype, device, coalesced)
|
|
for narrow_args in self._all_narrow_combs(shape):
|
|
self._test_narrow(with_dense, narrow_args)
|
|
|
|
self.assertRaises(RuntimeError, lambda: with_dense.narrow_copy(10, 0, 3)) # dim > sparseDim + denseDim
|
|
|
|
def _test_log1p_tensor(self, sparse_tensor, coalesced):
|
|
def is_integral(dtype):
|
|
return dtype in integral_types()
|
|
|
|
dense_tensor = sparse_tensor.to_dense()
|
|
expected_output = dense_tensor.log1p()
|
|
is_integral_dtype = is_integral(sparse_tensor.dtype)
|
|
self.assertEqual(expected_output, sparse_tensor.log1p().to_dense())
|
|
if is_integral_dtype:
|
|
with self.assertRaisesRegex(RuntimeError, "result type .* can't be cast to"):
|
|
sparse_tensor.coalesce().log1p_()
|
|
else:
|
|
self.assertEqual(expected_output, sparse_tensor.coalesce().log1p_().to_dense())
|
|
|
|
if not coalesced:
|
|
# test in-place op on uncoalesced input
|
|
with self.assertRaisesRegex(RuntimeError, "log1p_ requires coalesced input"):
|
|
sparse_tensor.log1p_()
|
|
|
|
if not is_integral_dtype:
|
|
sparse_tensor.requires_grad_()
|
|
self.assertTrue(sparse_tensor.requires_grad)
|
|
|
|
# test autograd
|
|
x = sparse_tensor.clone()
|
|
y = sparse_tensor.log1p()
|
|
with self.assertRaisesRegex(RuntimeError, "log1p of a sparse tensor is made to be non-differentiable"):
|
|
y.backward(x)
|
|
else:
|
|
with self.assertRaisesRegex(RuntimeError, "only Tensors of floating point dtype can require gradients"):
|
|
sparse_tensor.requires_grad_()
|
|
|
|
@coalescedonoff
|
|
@dtypes(*all_types())
|
|
def test_log1p(self, device, dtype, coalesced):
|
|
if coalesced:
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0], [1], [2]]).transpose(1, 0),
|
|
values=torch.tensor([3.0, 4.0, 5.0]),
|
|
size=[3, ],
|
|
device=device,
|
|
dtype=dtype
|
|
).coalesce()
|
|
self._test_log1p_tensor(input_coalesced, coalesced)
|
|
|
|
# hybrid sparse input
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[1, 3], [2, 4]]),
|
|
values=torch.tensor([[1.0, 3.0], [5.0, 7.0]]),
|
|
size=[4, 5, 2],
|
|
device=device,
|
|
dtype=dtype
|
|
).coalesce()
|
|
self._test_log1p_tensor(input_coalesced, coalesced)
|
|
|
|
if not coalesced:
|
|
# test uncoalesced input
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
|
|
values=torch.tensor([2.0, 3.0, 4.0, 1.0, 1.0, 1.0]),
|
|
size=[3, ],
|
|
device=device,
|
|
dtype=dtype
|
|
)
|
|
self._test_log1p_tensor(input_uncoalesced, coalesced)
|
|
|
|
# test on empty sparse tensor
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.zeros([2, 0]),
|
|
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
|
|
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
|
|
device=device,
|
|
dtype=dtype
|
|
)
|
|
self._test_log1p_tensor(input_uncoalesced, coalesced)
|
|
|
|
def _test_neg_negative(self, sparse_tensor):
|
|
dense_tensor = sparse_tensor.to_dense()
|
|
expected_output = dense_tensor.neg()
|
|
|
|
ops = (
|
|
torch.neg, torch.Tensor.neg, torch.Tensor.neg_,
|
|
torch.negative, torch.Tensor.negative, torch.Tensor.negative_,
|
|
operator.neg
|
|
)
|
|
for op in ops:
|
|
sparse_tensor_copy = sparse_tensor.clone()
|
|
self.assertEqual(expected_output, op(sparse_tensor_copy).to_dense())
|
|
|
|
if op in (torch.neg, torch.negative):
|
|
sparse_tensor_out = torch.zeros_like(sparse_tensor)
|
|
op(sparse_tensor, out=sparse_tensor_out)
|
|
self.assertEqual(expected_output, sparse_tensor_out.to_dense())
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_neg_negative(self, device, dtype, coalesced):
|
|
|
|
if coalesced:
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0, 1, 2]]),
|
|
values=torch.tensor([3.0, -4.0, 5.0]),
|
|
size=[3, ],
|
|
dtype=dtype,
|
|
device=device
|
|
).coalesce()
|
|
self._test_neg_negative(input_coalesced)
|
|
|
|
# hybrid sparse input
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[1, 3], [2, 4]]),
|
|
values=torch.tensor([[-1.0, 3.0], [-5.0, 7.0]]),
|
|
size=[4, 5, 2],
|
|
dtype=dtype,
|
|
device=device
|
|
).coalesce()
|
|
self._test_neg_negative(input_coalesced)
|
|
|
|
if not coalesced:
|
|
# test uncoalesced input
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
|
|
values=torch.tensor([2.0, -3.0, -4.0, 1.0, -1.0, 1.5]),
|
|
size=[3, ],
|
|
dtype=dtype,
|
|
device=device
|
|
)
|
|
self._test_neg_negative(input_uncoalesced)
|
|
|
|
# test on empty sparse tensor
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.zeros([2, 0]),
|
|
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
|
|
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
|
|
dtype=dtype,
|
|
device=device
|
|
)
|
|
self._test_neg_negative(input_uncoalesced)
|
|
|
|
def _test_asin_arcsin(self, sparse_tensor, coalesced):
|
|
def is_integral(dtype):
|
|
return dtype in integral_types()
|
|
is_integral_dtype = is_integral(sparse_tensor.dtype)
|
|
|
|
dense_tensor = sparse_tensor.to_dense()
|
|
expected_output = dense_tensor.asin()
|
|
|
|
ops = (
|
|
torch.asin, torch.Tensor.asin,
|
|
torch.arcsin, torch.Tensor.arcsin,
|
|
)
|
|
for op in ops:
|
|
self.assertEqual(expected_output, op(sparse_tensor).to_dense())
|
|
if op in (torch.asin, torch.arcsin):
|
|
sparse_tensor_out = torch.zeros_like(sparse_tensor)
|
|
if not is_integral_dtype:
|
|
op(sparse_tensor, out=sparse_tensor_out)
|
|
self.assertEqual(expected_output, sparse_tensor_out.to_dense())
|
|
else:
|
|
with self.assertRaisesRegex(RuntimeError, "result type .* can't be cast to"):
|
|
op(sparse_tensor, out=sparse_tensor_out)
|
|
|
|
for op in (torch.Tensor.asin_, torch.Tensor.arcsin_):
|
|
if is_integral_dtype:
|
|
# test coalesce on integral dtype tensor
|
|
with self.assertRaisesRegex(RuntimeError, "result type .* can't be cast to"):
|
|
op(sparse_tensor.clone().coalesce()).to_dense()
|
|
else:
|
|
self.assertEqual(expected_output, op(sparse_tensor.clone().coalesce()).to_dense())
|
|
|
|
if not coalesced:
|
|
# test in-place op on uncoalesced input
|
|
with self.assertRaisesRegex(RuntimeError, "asin_ requires coalesced input"):
|
|
op(sparse_tensor)
|
|
|
|
@coalescedonoff
|
|
@dtypes(*all_types())
|
|
def test_asin_arcsin(self, device, dtype, coalesced):
|
|
if coalesced:
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0, 1, 2, 3]]),
|
|
values=torch.tensor([0.5, -0.5, 0.7, -0.7]),
|
|
size=[4, ],
|
|
dtype=dtype,
|
|
device=device
|
|
).coalesce()
|
|
self._test_asin_arcsin(input_coalesced, coalesced)
|
|
|
|
# hybrid sparse input
|
|
input_coalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[1, 3], [2, 4]]),
|
|
values=torch.tensor([[-0.1, 0.24], [-0.44, 0.1]]),
|
|
size=[4, 5, 2],
|
|
dtype=dtype,
|
|
device=device
|
|
).coalesce()
|
|
self._test_asin_arcsin(input_coalesced, coalesced)
|
|
|
|
if not coalesced:
|
|
# test uncoalesced input
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.tensor([[0], [1], [2], [0], [1], [2]]).transpose(1, 0),
|
|
values=torch.tensor([0.3, -0.3, -0.4, 0.3, -0.5, 0.15]),
|
|
size=[3, ],
|
|
dtype=dtype,
|
|
device=device
|
|
)
|
|
self._test_asin_arcsin(input_uncoalesced, coalesced)
|
|
|
|
# test on empty sparse tensor
|
|
input_uncoalesced = torch.sparse_coo_tensor(
|
|
indices=torch.zeros([2, 0]),
|
|
values=torch.zeros([0, 5, 5, 5, 5, 5, 5, 0]),
|
|
size=[0, 0, 5, 5, 5, 5, 5, 5, 0],
|
|
dtype=dtype,
|
|
device=device
|
|
)
|
|
self._test_asin_arcsin(input_uncoalesced, coalesced)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_mv(self, device, dtype, coalesced):
|
|
def test_shape(di, dj, dk, nnz):
|
|
x, _, _ = self._gen_sparse(2, nnz, [di, dj], dtype, device, coalesced)
|
|
t = torch.randn(dk, dtype=dtype, device=device)
|
|
|
|
res = x.matmul(t)
|
|
expected = self.safeToDense(x).matmul(t)
|
|
self.assertEqual(res, expected)
|
|
|
|
test_shape(10, 100, 100, 20)
|
|
test_shape(100, 1000, 1000, 20)
|
|
test_shape(64, 10000, 10000, 20)
|
|
test_shape(0, 100, 100, 0)
|
|
test_shape(10, 0, 0, 0)
|
|
test_shape(10, 100, 100, 0)
|
|
test_shape(10, 100, 100, 20)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, r"mv: expected self\.size\(-1\) == vec\.size\(-1\)"):
|
|
test_shape(10, 100, 10, 20)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "mv: two tensor dim should be 2 and 1"):
|
|
x, _, _ = self._gen_sparse(2, 20, [10, 100], dtype, device, coalesced)
|
|
y, _, _ = self._gen_sparse(2, 20, [10, 100], dtype, device, coalesced)
|
|
res = x.mv(y)
|
|
|
|
@dtypes(*floating_and_complex_types())
|
|
def test_sparse_add_coalesce(self, device, dtype):
|
|
i = self.index_tensor([[1, 2, 1]], device=device)
|
|
v = torch.tensor([3, 4, 5], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3]))
|
|
y = self.sparse_tensor(i, v, torch.Size([3]))
|
|
z = x + y
|
|
|
|
self.assertFalse(z._indices().numel() != 2 and z.is_coalesced())
|
|
|
|
i = self.index_tensor([[1, 2, 1]], device=device)
|
|
v = torch.empty([3, 0], dtype=dtype, device=device)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 0]))
|
|
y = self.sparse_tensor(i, v, torch.Size([3, 0]))
|
|
z = x + y
|
|
|
|
self.assertFalse(z._indices().numel() != 2 and z.is_coalesced())
|
|
|
|
@onlyCUDA
|
|
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)
|
|
|
|
@onlyCUDA
|
|
@deviceCountAtLeast(2)
|
|
def test_same_gpu(self, devices):
|
|
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)
|
|
|
|
dev1, dev2 = devices[0], devices[1]
|
|
|
|
i = self.index_tensor([[2]], device=dev2)
|
|
v = torch.tensor([5], device=dev2)
|
|
x = self.sparse_tensor(i, v, torch.Size([3]), device=1)
|
|
check_device(x, 1)
|
|
|
|
i = self.index_tensor([[2]], device=dev2)
|
|
v = torch.empty(1, 0, device=dev2)
|
|
x = self.sparse_tensor(i, v, torch.Size([3, 0]), device=1)
|
|
check_device(x, 1)
|
|
|
|
x = self.sparse_empty(3, device=1)
|
|
check_device(x, 1)
|
|
|
|
x = self.sparse_empty(3, 0, device=1)
|
|
check_device(x, 1)
|
|
|
|
i = self.index_tensor([[2]], device=dev2)
|
|
v = torch.tensor([5], device=dev1)
|
|
# NB: non-legacy constructor allows this and moves indices
|
|
self.assertRaises(RuntimeError, lambda: self.legacy_sparse_tensor(i, v, torch.Size([3])))
|
|
|
|
i = self.index_tensor([[2]], device=dev2)
|
|
v = torch.empty(1, 0, device=dev1)
|
|
# NB: non-legacy constructor allows this and moves indices
|
|
self.assertRaises(RuntimeError, lambda: self.legacy_sparse_tensor(i, v, torch.Size([3, 0])))
|
|
|
|
def _test_new_device(self, size, device=torch.cuda):
|
|
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)
|
|
|
|
@onlyCUDA
|
|
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)
|
|
|
|
@onlyCUDA
|
|
@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)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_new(self, device, dtype, coalesced):
|
|
def test_shape(sparse_dims, nnz, with_size):
|
|
x, indices, values = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)
|
|
if not x.is_cuda:
|
|
# CUDA sparse tensors currently requires the size to be
|
|
# specified if nDimV > 0
|
|
out = x.new(indices, values).coalesce()
|
|
x_c = x.coalesce()
|
|
self.assertEqual((out.indices(), out.values()), (x_c.indices(), x_c.values()))
|
|
self.assertEqual(x.new(indices, values, x.size()), x)
|
|
|
|
test_shape(3, 10, 100)
|
|
test_shape(3, 0, [100, 100, 0])
|
|
|
|
@onlyCPU # not really, but we only really want to run this once
|
|
@dtypes(torch.float64, torch.float32, torch.float16, torch.cfloat, torch.cdouble)
|
|
def test_factory(self, device, dtype):
|
|
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
|
|
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 = torch.empty(1, 0).to(dtype)
|
|
else:
|
|
if use_tensor_val:
|
|
values = torch.tensor([1.], dtype=dtype)
|
|
else:
|
|
values = 1.
|
|
if include_size:
|
|
sparse_tensor = torch.sparse_coo_tensor(indices, values, size, dtype=dtype,
|
|
device=device, requires_grad=True)
|
|
else:
|
|
sparse_tensor = torch.sparse_coo_tensor(indices, values, dtype=dtype,
|
|
device=device, requires_grad=True)
|
|
self.assertEqual(indices, sparse_tensor._indices())
|
|
self.assertEqual(values, sparse_tensor._values())
|
|
self.assertEqual(size if include_size else default_size, sparse_tensor.size())
|
|
self.assertEqual(dtype, sparse_tensor.dtype)
|
|
if use_cuda:
|
|
self.assertEqual(device, sparse_tensor._values().device)
|
|
self.assertEqual(True, sparse_tensor.requires_grad)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_factory_size_check(self, device, dtype):
|
|
indices = self.index_tensor([[1, 2],
|
|
[0, 2]], device=device)
|
|
values = torch.tensor([.5, .5], dtype=dtype, device=device)
|
|
sizes = torch.Size([2, 3])
|
|
with self.assertRaisesRegex(RuntimeError, "size is inconsistent with indices"):
|
|
torch.sparse_coo_tensor(indices, values, sizes, dtype=dtype, device=device)
|
|
|
|
indices.fill_(-1)
|
|
with self.assertRaisesRegex(RuntimeError, "found negative index"):
|
|
torch.sparse_coo_tensor(indices, values, sizes, dtype=dtype, device=device)
|
|
|
|
indices = self.index_tensor([[1, 2],
|
|
[0, 2]], device=device)
|
|
values = torch.empty([2, 1, 0], dtype=dtype, device=device)
|
|
sizes = torch.Size([2, 3, 1, 0])
|
|
with self.assertRaisesRegex(RuntimeError, "size is inconsistent with indices"):
|
|
torch.sparse_coo_tensor(indices, values, sizes, dtype=dtype, device=device)
|
|
|
|
indices = self.index_tensor([[1, 2],
|
|
[0, 2]], device=device)
|
|
values = torch.empty([2, 2, 2], dtype=dtype, device=device)
|
|
sizes = torch.Size([0, 0, 2, 2])
|
|
with self.assertRaisesRegex(RuntimeError, "size is inconsistent with indices"):
|
|
torch.sparse_coo_tensor(indices, values, sizes, dtype=dtype, device=device)
|
|
|
|
indices = self.index_tensor([[1, 2],
|
|
[0, 2]], device=device)
|
|
values = torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=dtype, device=device)
|
|
sizes = torch.Size([3, 3, 2])
|
|
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
|
|
torch.sparse_coo_tensor(indices, values, sizes, dtype=dtype, device=device)
|
|
|
|
indices = self.index_tensor([[1, 2],
|
|
[0, 2]], device=device)
|
|
values = torch.empty([2, 1, 0], dtype=dtype, device=device)
|
|
sizes = torch.Size([3, 3, 2, 0])
|
|
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
|
|
torch.sparse_coo_tensor(indices, values, sizes, dtype=dtype, device=device)
|
|
|
|
def test_factory_default(self, device):
|
|
tensor = self.legacy_sparse_tensor()
|
|
expected_indices = self.index_tensor([[]], device=device)
|
|
expected_size = torch.Size([0])
|
|
self.assertEqual(tensor._indices(), expected_indices)
|
|
self.assertEqual(tensor.shape, expected_size)
|
|
|
|
def test_factory_empty_indices(self, device):
|
|
tensor = self.legacy_sparse_tensor()
|
|
expected_indices = torch.empty((1, 0), dtype=torch.long, device=device)
|
|
self.assertEqual(tensor._indices(), expected_indices)
|
|
|
|
tensor = torch.sparse_coo_tensor(torch.Size([2, 0]), device=device)
|
|
expected_indices = torch.empty((2, 0), dtype=torch.long, device=device)
|
|
self.assertEqual(tensor._indices(), expected_indices)
|
|
|
|
tensor = torch.sparse_coo_tensor(torch.Size([2, 2, 0]), device=device)
|
|
expected_indices = torch.empty((3, 0), dtype=torch.long, device=device)
|
|
self.assertEqual(tensor._indices(), expected_indices)
|
|
|
|
tensor = torch.sparse_coo_tensor(torch.Size([2, 2, 0, 0]), device=device)
|
|
expected_indices = torch.empty((4, 0), dtype=torch.long, device=device)
|
|
self.assertEqual(tensor._indices(), expected_indices)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_factory_nnz(self, device, dtype):
|
|
indices = self.index_tensor([[0]], device=device) # (sparse_dim, nnz): (1, 1)
|
|
values = torch.tensor([[1, 1], [1, 1]], dtype=dtype, device=device) # (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, dtype=dtype, device=device)
|
|
|
|
indices = self.index_tensor([[0]], device=device) # (sparse_dim, nnz): (1, 1)
|
|
values = torch.empty([2, 0], dtype=dtype, device=device) # (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, dtype=dtype, device=device)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_factory_nnz_zero(self, device, dtype):
|
|
def test_shape(i_shape, v_shape, size, expected_size):
|
|
if size:
|
|
t = torch.sparse_coo_tensor(torch.empty(i_shape), torch.empty(v_shape), torch.Size(size),
|
|
dtype=dtype, device=device)
|
|
else:
|
|
t = torch.sparse_coo_tensor(torch.empty(i_shape), torch.empty(v_shape), dtype=dtype, device=device)
|
|
expected_indices = torch.empty(i_shape, device=device, dtype=torch.int64)
|
|
expected_values = torch.empty(v_shape, device=device, dtype=dtype)
|
|
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])
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_factory_dense_dim(self, device, dtype):
|
|
indices = self.index_tensor([[0]], device=device)
|
|
values = torch.tensor([[[1, 1, 1], [1, 1, 1]]], dtype=dtype, device=device)
|
|
sizes = torch.Size([1, 3, 4])
|
|
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
|
|
torch.sparse_coo_tensor(indices, values, sizes)
|
|
|
|
indices = self.index_tensor([[0]], device=device)
|
|
values = torch.empty([1, 2, 3, 0], dtype=dtype, device=device)
|
|
sizes = torch.Size([1, 3, 4, 0])
|
|
with self.assertRaisesRegex(RuntimeError, "values has incorrect size"):
|
|
torch.sparse_coo_tensor(indices, values, sizes)
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.float16, torch.float32, torch.float64, torch.cfloat, torch.cdouble, torch.int64)
|
|
def test_factory_type_inference(self, device, dtype):
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.tensor([1.], dtype=dtype))
|
|
self.assertEqual(dtype, t.dtype)
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.tensor([1]))
|
|
self.assertEqual(torch.int64, t.dtype)
|
|
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.HalfTensor(1, 0))
|
|
self.assertEqual(torch.float16, t.dtype)
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.FloatTensor(1, 0))
|
|
self.assertEqual(torch.float32, t.dtype)
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.DoubleTensor(1, 0))
|
|
self.assertEqual(torch.float64, t.dtype)
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0], [2])), torch.LongTensor(1, 0))
|
|
self.assertEqual(torch.int64, t.dtype)
|
|
|
|
|
|
@onlyCUDA
|
|
def test_factory_device_type_inference(self, device):
|
|
# both indices/values are CUDA
|
|
|
|
cpu_cuda = ('cpu', 'cuda')
|
|
cpu_cuda_none = cpu_cuda + (None,)
|
|
for indices_device, values_device, device in itertools.product(cpu_cuda,
|
|
cpu_cuda,
|
|
cpu_cuda_none):
|
|
indices = torch.tensor(([0], [2]), device=indices_device)
|
|
values = torch.tensor([1.], device=values_device)
|
|
empty_values = torch.empty(1, 0).to(values_device)
|
|
shape = (1, 3)
|
|
empty_shape = (1, 3, 0)
|
|
if device is None and indices_device != values_device:
|
|
with self.assertRaises(RuntimeError):
|
|
torch.sparse_coo_tensor(indices, values, shape, device=device)
|
|
with self.assertRaises(RuntimeError):
|
|
torch.sparse_coo_tensor(indices, empty_values, empty_shape, device=device)
|
|
else:
|
|
t = torch.sparse_coo_tensor(indices, values, shape, device=device)
|
|
t_empty = torch.sparse_coo_tensor(indices, empty_values, empty_shape, device=device)
|
|
should_be_cuda = (device == 'cuda' or (device is None and values_device == 'cuda'))
|
|
self.assertEqual(should_be_cuda, t.is_cuda)
|
|
self.assertEqual(t.is_cuda, t_empty.is_cuda)
|
|
|
|
@onlyCPU
|
|
def test_factory_copy(self, device):
|
|
def test_tensor(indices, values, indices_equal, values_equal):
|
|
sparse_tensor = torch.sparse_coo_tensor(indices, values, dtype=torch.float64, device=device)
|
|
if indices_equal:
|
|
self.assertEqual(indices.data_ptr(), sparse_tensor._indices().data_ptr())
|
|
else:
|
|
self.assertNotEqual(indices.data_ptr(), sparse_tensor._indices().data_ptr())
|
|
if values_equal:
|
|
self.assertEqual(values.data_ptr(), sparse_tensor._values().data_ptr())
|
|
else:
|
|
self.assertNotEqual(values.data_ptr(), sparse_tensor._values().data_ptr())
|
|
|
|
# both correct
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int64)
|
|
values = torch.tensor([1.], dtype=torch.float64)
|
|
test_tensor(indices, values, True, True)
|
|
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int64)
|
|
values = torch.DoubleTensor(1, 0)
|
|
test_tensor(indices, values, True, True)
|
|
|
|
# only indices correct
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int64)
|
|
values = torch.tensor([1.], dtype=torch.float32)
|
|
test_tensor(indices, values, True, False)
|
|
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int64)
|
|
values = torch.tensor([1.], dtype=torch.float16)
|
|
test_tensor(indices, values, True, False)
|
|
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int64)
|
|
values = torch.FloatTensor(1, 0)
|
|
test_tensor(indices, values, True, True) # An empty tensor's data_ptr is always equal to 0
|
|
|
|
# only values correct
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int32)
|
|
values = torch.tensor([1.], dtype=torch.float64)
|
|
test_tensor(indices, values, False, True)
|
|
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int32)
|
|
values = torch.DoubleTensor(1, 0)
|
|
test_tensor(indices, values, False, True)
|
|
|
|
# neither correct
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int32)
|
|
values = torch.tensor([1.], dtype=torch.float32)
|
|
test_tensor(indices, values, False, False)
|
|
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int32)
|
|
values = torch.FloatTensor(1, 0)
|
|
test_tensor(indices, values, False, True) # An empty tensor's data_ptr is always equal to 0
|
|
|
|
# complex support
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int64)
|
|
values = make_tensor([1, ], dtype=torch.cdouble, device=device)
|
|
test_tensor(indices, values, True, False)
|
|
|
|
indices = torch.tensor(([0], [2]), dtype=torch.int32)
|
|
values = make_tensor([1, 1], dtype=torch.cdouble, device=device)
|
|
test_tensor(indices, values, False, False)
|
|
|
|
|
|
@onlyCPU # just run once, we test both cpu and cuda
|
|
def test_constructor_device_legacy(self, device):
|
|
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
|
|
v = torch.tensor([3., 4., 5.])
|
|
size = torch.Size([2, 3])
|
|
|
|
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(device='cuda'))
|
|
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(i, v, device='cuda'))
|
|
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(i, v, size, device='cuda'))
|
|
self.assertRaises(RuntimeError, lambda: torch.sparse.FloatTensor(torch.Size([2, 3, 4]), device='cuda'))
|
|
|
|
x = torch.sparse_coo_tensor(i, v, size, device='cpu')
|
|
self.assertRaises(RuntimeError, lambda: x.new(device='cuda'))
|
|
self.assertRaises(RuntimeError, lambda: x.new(i, v, device='cuda'))
|
|
self.assertRaises(RuntimeError, lambda: x.new(i, v, size, device='cuda'))
|
|
self.assertRaises(RuntimeError, lambda: x.new(torch.Size([2, 3, 4]), device='cuda'))
|
|
|
|
if torch.cuda.is_available():
|
|
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(i, v, device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(i, v, size, device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: torch.cuda.sparse.FloatTensor(torch.Size([2, 3, 4]), device='cpu'))
|
|
|
|
x = torch.sparse_coo_tensor(i, v, size, device='cuda')
|
|
self.assertRaises(RuntimeError, lambda: x.new(device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: x.new(i, v, device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: x.new(i, v, size, device='cpu'))
|
|
self.assertRaises(RuntimeError, lambda: x.new(torch.Size([2, 3, 4]), device='cpu'))
|
|
|
|
def test_legacy_constructor(self, device):
|
|
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
|
|
v = torch.tensor([3., 4., 5.])
|
|
size = torch.Size([2, 3])
|
|
|
|
self.assertRaises(TypeError, lambda: torch.sparse.FloatTensor(v.storage()))
|
|
self.assertRaises(TypeError, lambda: torch.sparse.FloatTensor(v))
|
|
self.assertEqual(torch.sparse_coo, torch.sparse.FloatTensor(torch.Size([2, 3])).layout)
|
|
self.assertRaises(TypeError, lambda: torch.sparse.FloatTensor([6]))
|
|
|
|
def test_legacy_new(self, device):
|
|
i = torch.tensor([[0, 1, 1], [2, 0, 2]])
|
|
v = torch.tensor([3., 4., 5.])
|
|
size = torch.Size([2, 3])
|
|
s = torch.sparse_coo_tensor(i, v, size)
|
|
|
|
self.assertEqual(torch.sparse_coo, s.new(device='cpu').layout)
|
|
self.assertRaises(TypeError, lambda: s.new(v.storage()))
|
|
self.assertRaises(TypeError, lambda: s.new(v))
|
|
self.assertEqual(torch.sparse_coo, s.new(torch.Size([2, 3])).layout)
|
|
self.assertRaises(TypeError, lambda: s.new([6]))
|
|
|
|
@onlyCPU # not really, but we only really want to run this once
|
|
def test_dtypes(self, device):
|
|
all_sparse_dtypes = all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16)
|
|
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'))
|
|
|
|
@onlyCPU # not really, but we only really want to run this once
|
|
def test_empty_full(self, device):
|
|
all_sparse_dtypes = all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16)
|
|
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cpu'))
|
|
if torch.cuda.device_count() > 0:
|
|
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, None)
|
|
do_test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cuda:0'))
|
|
|
|
def test_is_sparse(self, device):
|
|
x = torch.randn(3, 3)
|
|
self.assertFalse(x.is_sparse)
|
|
|
|
x = torch.randn(3, 3, 0)
|
|
self.assertFalse(x.is_sparse)
|
|
|
|
x = self.legacy_sparse_tensor()
|
|
self.assertTrue(x.is_sparse)
|
|
|
|
x = self.sparse_empty(1, 0, device=device)
|
|
self.assertTrue(x.is_sparse)
|
|
|
|
def test_resize_as(self, device):
|
|
def do_test(t):
|
|
y = t.new().resize_as_(t).zero_()
|
|
self.assertEqual(y.shape, t.shape)
|
|
# Check that y can be added to t. Currently, this requires that
|
|
# sparse_dim and dense_dim match.
|
|
self.assertEqual(t, t + y)
|
|
|
|
do_test(self.legacy_sparse_tensor())
|
|
do_test(self.sparse_empty([3, 0], device=device))
|
|
do_test(self.sparse_empty([3, 3], device=device))
|
|
|
|
def _test_resize_shape(self, x_i, x_v, x_size, y_i, y_v, y_size, dtype, device):
|
|
x_v_numel = torch.zeros(x_v).numel()
|
|
y_v_numel = torch.zeros(y_v).numel()
|
|
x = torch.sparse_coo_tensor(torch.zeros(x_i),
|
|
torch.arange(x_v_numel).resize_(x_v).to(torch.float),
|
|
torch.Size(x_size), dtype=dtype, device=device)
|
|
x_dense = x.to_dense()
|
|
y = torch.sparse_coo_tensor(torch.zeros(y_i),
|
|
torch.ones(y_v).to(torch.float),
|
|
torch.Size(y_size), dtype=dtype, device=device)
|
|
y_dense = y.to_dense()
|
|
x.resize_as_(y)
|
|
x_dense.resize_as_(y_dense)
|
|
self.assertEqual(x.shape, y.shape)
|
|
self.assertEqual(x.sparse_dim(), y.sparse_dim())
|
|
self.assertEqual(x.dense_dim(), y.dense_dim())
|
|
self.assertEqual(x.shape, x_dense.shape)
|
|
self.assertEqual(y.shape, y_dense.shape)
|
|
# Here we make sure that the original data are preserved after resizing
|
|
self.assertEqual(x.to_dense().view(-1)[0:x_v_numel].view(x_v),
|
|
x_dense.view(-1)[0:x_v_numel].view(x_v))
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_resize(self, device, dtype):
|
|
# 1. Expand the size of some dense dimensions [Supported]
|
|
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
|
|
[1, 1], [1, 2, 4], [2, 2, 4],
|
|
dtype=dtype, device=device)
|
|
|
|
self._test_resize_shape([1, 1], [1, 2, 0], [2, 2, 0],
|
|
[1, 1], [1, 2, 4], [2, 2, 4],
|
|
dtype=dtype, device=device)
|
|
|
|
# 2. Expand the size of some sparse dimensions [Supported]
|
|
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
|
|
[1, 1], [1, 2, 3], [4, 2, 3],
|
|
dtype=dtype, device=device)
|
|
|
|
# 3. Change the shapes of both sparse and dense dimensions when nnz is zero [Supported]
|
|
self._test_resize_shape([1, 0], [0, 2, 3], [2, 2, 3],
|
|
[2, 0], [0, 2, 4, 5], [1, 1, 2, 4, 5],
|
|
dtype=dtype, device=device)
|
|
|
|
self._test_resize_shape([1, 0], [0, 2, 3], [2, 2, 3],
|
|
[2, 0], [0, 2, 4, 0], [1, 1, 2, 4, 0],
|
|
dtype=dtype, device=device)
|
|
|
|
# 4. Add dims to dense dimensions [Not Supported]
|
|
with self.assertRaisesRegex(RuntimeError, "changing the number of dense dimensions"):
|
|
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
|
|
[1, 1], [1, 2, 3, 4], [2, 2, 3, 4],
|
|
dtype=dtype, device=device)
|
|
|
|
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],
|
|
dtype=dtype, device=device)
|
|
|
|
# 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],
|
|
dtype=dtype, device=device)
|
|
|
|
# 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],
|
|
dtype=dtype, device=device)
|
|
|
|
# 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],
|
|
dtype=dtype, device=device)
|
|
|
|
# 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],
|
|
dtype=dtype, device=device)
|
|
|
|
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],
|
|
dtype=dtype, device=device)
|
|
|
|
def test_is_nonzero(self, device):
|
|
self.assertTrue(torch.sparse_coo_tensor(([0],), 1., (1,), device=device).is_nonzero())
|
|
self.assertFalse(torch.sparse_coo_tensor(([0],), 0., (1,), device=device).is_nonzero())
|
|
self.assertFalse(torch.sparse_coo_tensor(([0], [0]), 0., (1, 1), device=device).is_nonzero())
|
|
self.assertFalse(torch.sparse_coo_tensor(([0, 0],), (0., 0.), (1,), device=device).is_nonzero())
|
|
self.assertFalse(torch.sparse_coo_tensor(([0, 0],), (-1., 1.), (1,), device=device).is_nonzero())
|
|
|
|
# scalar sparse tensor
|
|
self.assertTrue(torch.sparse_coo_tensor(torch.zeros(0, 1), 12.3, [], device=device).is_nonzero())
|
|
with self.assertRaisesRegex(RuntimeError, "Boolean value of Tensor with no values is ambiguous"):
|
|
torch.sparse_coo_tensor(([0, 1],), torch.empty(2, 0), (4, 0), device=device).is_nonzero()
|
|
self.assertTrue(torch.sparse_coo_tensor(([0],), 2.3 - 4.5j, (1,), dtype=torch.cfloat, device=device)
|
|
.is_nonzero())
|
|
self.assertTrue(torch.sparse_coo_tensor(([0],), 2.3 - 4.5j, (1,), dtype=torch.cdouble, device=device)
|
|
.is_nonzero())
|
|
self.assertFalse(torch.sparse_coo_tensor(([0],), 0. + 0j, (1,), dtype=torch.cfloat, device=device)
|
|
.is_nonzero())
|
|
self.assertFalse(torch.sparse_coo_tensor(([0],), 0. + 0j, (1,), dtype=torch.cdouble, device=device)
|
|
.is_nonzero())
|
|
|
|
def test_allow_tensor_metadata_change(self, device):
|
|
def do_test(t):
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"raw_resize_ is not allowed on a Tensor created from .data or .detach()"):
|
|
t.transpose_(0, 1)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"resize_ is not allowed on a Tensor created from .data or .detach()"):
|
|
t.resize_as_(self.sparse_empty(3, 3))
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"resize_and_clear_ is not allowed on a Tensor created from .data or .detach()"):
|
|
t.mul_(t)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"set_coalesced is not allowed on a Tensor created from .data or .detach()"):
|
|
t._coalesced_(True)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"set_indices_and_values_unsafe is not allowed on a Tensor created from .data or .detach()"):
|
|
a = self.sparse_tensor(torch.tensor([[0, 1, 1], [2, 0, 2]]), torch.tensor([3., 4., 5.])).data
|
|
a.add_(a)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"resize_and_clear_ is not allowed on a Tensor created from .data or .detach()"):
|
|
a.zero_()
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"resize_ is not allowed on a Tensor created from .data or .detach()"):
|
|
a.copy_(self.sparse_empty(3, 3))
|
|
|
|
do_test(self.sparse_empty([3, 0], device=device).data)
|
|
do_test(self.sparse_empty([3, 0], device=device).detach())
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_change_tensor_metadata(self, device, dtype):
|
|
i = self.index_tensor([[0], [1]], device=device)
|
|
v = torch.tensor([[3, 4, 5]], dtype=dtype, device=device)
|
|
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]), dtype=dtype, device=device)
|
|
i.resize_(2, 3)
|
|
v.resize_(4, 5)
|
|
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
|
|
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
|
|
|
|
i = self.index_tensor([[0], [1]], device=device)
|
|
v = torch.tensor([[3, 4, 5]], dtype=dtype, device=device)
|
|
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
|
|
i.resize_as_(self.index_tensor([0, 1], device=device))
|
|
v.resize_as_(torch.tensor([3, 4, 5], dtype=dtype, device=device))
|
|
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
|
|
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
|
|
|
|
i = self.index_tensor([[0], [1]], device=device)
|
|
v = torch.tensor([[3, 4, 5]], dtype=dtype, device=device)
|
|
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
|
|
i.as_strided_((2, 1), (1, 1))
|
|
v.as_strided_((1, 3), (1, 1))
|
|
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
|
|
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
|
|
|
|
i = self.index_tensor([[0], [1]], device=device)
|
|
v = torch.tensor([[3, 4, 5]], dtype=dtype, device=device)
|
|
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
|
|
i.set_(self.index_tensor([0, 1], device=device))
|
|
v.set_(torch.tensor([3, 4, 5], dtype=dtype, device=device))
|
|
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
|
|
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
|
|
|
|
i = self.index_tensor([[0], [1]], device=device)
|
|
v = torch.tensor([[3, 4, 5]], dtype=dtype, device=device)
|
|
t = torch.sparse_coo_tensor(i, v, torch.Size([1, 2, 3]))
|
|
i.transpose_(0, 1)
|
|
v.transpose_(0, 1)
|
|
self.assertEqual(list(t.coalesce().indices().size()), [2, 1])
|
|
self.assertEqual(list(t.coalesce().values().size()), [1, 3])
|
|
|
|
@skipIfRocm
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_pickle(self, device, dtype, coalesced):
|
|
import pickle
|
|
|
|
shape_sparse_dim_nnz = [
|
|
((), 0, 2),
|
|
((0,), 0, 10),
|
|
((2,), 0, 3),
|
|
((100, 3), 1, 3),
|
|
((100, 20, 3), 2, 0),
|
|
((10, 0, 3), 0, 3),
|
|
((10, 0, 3), 0, 0),
|
|
]
|
|
|
|
for shape, sparse_dim, nnz in shape_sparse_dim_nnz:
|
|
indices_shape = torch.Size((sparse_dim, nnz))
|
|
values_shape = torch.Size((nnz,) + shape[sparse_dim:])
|
|
indices = torch.arange(indices_shape.numel(), dtype=self.index_tensor(0).dtype,
|
|
device=device).view(indices_shape)
|
|
for d in range(sparse_dim):
|
|
indices[d].clamp_(max=(shape[d] - 1)) # make it valid index
|
|
if not coalesced and indices.numel() > 0:
|
|
indices[:, -1] = indices[:, 0] # make it uncoalesced
|
|
values_numel = values_shape.numel()
|
|
values = torch.arange(values_numel, dtype=dtype,
|
|
device=device).view(values_shape).div_(values_numel / 2.)
|
|
sp_tensor = self.sparse_tensor(indices, values, shape)
|
|
serialized = pickle.dumps(sp_tensor)
|
|
sp_tensor_loaded = pickle.loads(serialized)
|
|
self.assertEqual(sp_tensor, sp_tensor_loaded)
|
|
|
|
def test_any(self, device):
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([False, False]), device=device)
|
|
t_any = torch.tensor(False)
|
|
self.assertEqual(torch.any(t), t_any)
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([True, False]), device=device)
|
|
t_any = torch.tensor(True)
|
|
self.assertEqual(torch.any(t), t_any)
|
|
|
|
def test_isnan(self, device):
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [0, 2])), torch.tensor([1, 4]), device=device)
|
|
t_nan = torch.sparse_coo_tensor(torch.tensor(([0, 0], [0, 2])), torch.tensor([False, False]), device=device)
|
|
self.assertEqual(torch.isnan(t).int(), t_nan.int())
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [0, 2])), torch.tensor([1, float("nan")]), device=device)
|
|
t_nan = torch.sparse_coo_tensor(torch.tensor(([0, 0], [0, 2])), torch.tensor([False, True]), device=device)
|
|
self.assertEqual(torch.isnan(t).int(), t_nan.int())
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.float32, torch.float64)
|
|
def test_div_rounding_mode(self, device, dtype, coalesced):
|
|
sparse, _, _ = self._gen_sparse(2, 10, (10, 10), dtype,
|
|
device, coalesced)
|
|
dense = self.safeToDense(sparse)
|
|
|
|
for mode in (None, 'floor', 'trunc'):
|
|
actual = sparse.div(-2, rounding_mode=mode)
|
|
expect = dense.div(-2, rounding_mode=mode)
|
|
self.assertEqual(self.safeToDense(actual), expect)
|
|
|
|
# Test inplace
|
|
actual = sparse.clone().div_(-2, rounding_mode=mode)
|
|
self.assertEqual(self.safeToDense(actual), expect)
|
|
|
|
# Test out argument
|
|
actual.zero_()
|
|
torch.div(sparse, -2, rounding_mode=mode, out=actual)
|
|
self.assertEqual(self.safeToDense(actual), expect)
|
|
|
|
def test_div_by_sparse_error(self, device):
|
|
self.assertRaisesRegex(RuntimeError, 'Sparse division requires',
|
|
lambda: torch.tensor(1., device=device).to_sparse()
|
|
/ torch.tensor(1., device=device).to_sparse())
|
|
|
|
def test_floor_divide_by_sparse_error(self, device):
|
|
self.assertRaisesRegex(RuntimeError, 'Sparse floor division requires',
|
|
lambda: torch.tensor(1., device=device).to_sparse()
|
|
// torch.tensor(1., device=device).to_sparse())
|
|
|
|
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
|
|
@onlyCPU
|
|
def test_sparse_to_numpy(self, device):
|
|
t = torch.sparse_coo_tensor(torch.tensor(([0, 0], [2, 0])), torch.tensor([1, 4]))
|
|
self.assertRaises(TypeError, lambda: t.numpy())
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_softmax(self, device, dtype, coalesced):
|
|
import torch.nn.functional as F
|
|
|
|
def to_dense(sparse, fill_value=None):
|
|
"""
|
|
Return dense tensor from a sparse tensor using given fill value.
|
|
"""
|
|
if fill_value is None or fill_value == 0:
|
|
return sparse.to_dense()
|
|
sparse = sparse.coalesce()
|
|
dense = torch.full(sparse.shape, fill_value, dtype=sparse.dtype, device=sparse.device)
|
|
for idx, value in zip(sparse._indices().t(), sparse._values()):
|
|
dense[tuple(idx)] = value
|
|
return dense
|
|
|
|
def softmax_to_dense(sparse, dim):
|
|
"""Dense softmax of a sparse tensor. Useful only for testing softmax
|
|
correctness.
|
|
|
|
When computing softmax of a sparse tensor, the value of
|
|
unspecified items is negative infinity rather than zero so
|
|
that
|
|
|
|
softmax(sparse.to_dense(fill_value=-inf), dim) == softmax(sparse, dim).to_dense()
|
|
|
|
holds for non-empty lines. One empty lines, the softmax
|
|
values are defined as 0 in order to preserve the sparsity
|
|
of result.
|
|
|
|
Note that in PyTorch, ``to_dense`` method does not
|
|
implement the ``fill_value`` keyword argument.
|
|
"""
|
|
dtype = sparse.dtype
|
|
device = sparse.device
|
|
dense = to_dense(sparse, fill_value=-float('inf'))
|
|
r = F.softmax(dense, dim)
|
|
# softmax on empty lines results nan, replace with zeros to match the definition
|
|
r[r != r] = 0
|
|
return r
|
|
|
|
def sparse_softmax(sparse, dim):
|
|
"""Pure Python softmax of a sparse tensor. Assuming -inf for
|
|
unspecified sparse tensor data. This is a prototype of
|
|
sparse softmax algorithm in Python.
|
|
"""
|
|
dtype = sparse.dtype
|
|
device = sparse.device
|
|
|
|
# softmax is non-linear operation, so sparse tensors must
|
|
# be coalesced.
|
|
sparse = sparse.coalesce()
|
|
inf = float('inf')
|
|
indices = sparse._indices()
|
|
values = sparse._values()
|
|
|
|
if dim < sparse.sparse_dim():
|
|
nnz = sparse._nnz()
|
|
|
|
# compute pool indices
|
|
size = sparse.size()
|
|
strides = torch.ones((sparse.sparse_dim(), 1), dtype=indices.dtype, device=indices.device)
|
|
for i in reversed(range(sparse.sparse_dim() - 1)):
|
|
strides[i, 0] = strides[i + 1, 0] * size[i + 1]
|
|
strides[dim, 0] = 0
|
|
|
|
pool = (indices * strides).sum(dim=0)
|
|
i2p = {}
|
|
for i in range(nnz):
|
|
c = int(pool[i])
|
|
if c not in i2p:
|
|
i2p[c] = len(i2p)
|
|
pool[i] = i2p[c]
|
|
|
|
# compute max
|
|
dense_size = tuple(size[sparse.sparse_dim():])
|
|
mx = torch.empty((pool.max() + 1,) + dense_size, dtype=dtype, device=device)
|
|
mx[:] = -inf
|
|
for n in range(nnz):
|
|
p = pool[n]
|
|
mx[p] = torch.max(mx[p], values[n])
|
|
|
|
# apply exp to (v - mx) and sum the results
|
|
exp_values = torch.empty_like(values)
|
|
exp_sums = torch.zeros_like(mx)
|
|
for n in range(nnz):
|
|
p = pool[n]
|
|
v = exp_values[n] = (values[n] - mx[p]).exp()
|
|
exp_sums[p] = exp_sums[p] + v
|
|
|
|
# normalize with the sum of exponents
|
|
for n in range(nnz):
|
|
p = pool[n]
|
|
exp_values[n] = exp_values[n] / exp_sums[p]
|
|
|
|
return torch.sparse_coo_tensor(indices,
|
|
exp_values,
|
|
sparse.size(),
|
|
dtype=dtype, device=device)
|
|
|
|
elif dim < sparse.sparse_dim() + sparse.dense_dim():
|
|
return torch.sparse_coo_tensor(indices,
|
|
F.softmax(values, dim - sparse.sparse_dim() + 1),
|
|
sparse.size(),
|
|
dtype=dtype, device=device)
|
|
else:
|
|
raise ValueError(
|
|
'`dim(=%s)` must be smaller than `sparse_dim(=%s) + dense_dim(=%s)`'
|
|
% (dim, sparse.sparse_dim(), sparse.dense_dim()))
|
|
|
|
def softmax_jacobian_analytic(x, dim):
|
|
"""Return Jacobian of softmax using analytic formula
|
|
|
|
D_jS_i = S_i * (1[i==j] - S_j).
|
|
|
|
where S = softmax(x, dim), x is dense tensor, i,j in
|
|
range(x.shape[dim]).
|
|
"""
|
|
y = F.softmax(x, dim)
|
|
y[y != y] = 0 # replace nan-s with zeros
|
|
J = torch.zeros((x.shape[dim],) + tuple(x.shape), dtype=x.dtype, device=x.device)
|
|
si = [slice(None)] * len(y.shape)
|
|
sj = [slice(None)] * len(y.shape)
|
|
s = [slice(None)] * len(J.shape)
|
|
for i in range(y.shape[dim]):
|
|
si[dim] = i
|
|
s[dim + 1] = i
|
|
yi = y[tuple(si)]
|
|
for j in range(y.shape[dim]):
|
|
sj[dim] = j
|
|
s[0] = j
|
|
if i == j:
|
|
J[tuple(s)] = yi * (1 - yi)
|
|
else:
|
|
yj = y[tuple(sj)]
|
|
J[tuple(s)] = - yi * yj
|
|
sj[dim] = slice(None)
|
|
si[dim] = slice(None)
|
|
s[dim + 1] = slice(None)
|
|
return J
|
|
|
|
def softmax_jacobian_autograd(x, dim, log=False):
|
|
"""Return Jacobian of softmax using PyTorch autograd feature.
|
|
|
|
x can be dense or sparse tensor.
|
|
"""
|
|
import itertools
|
|
|
|
if x.is_sparse:
|
|
x = x.coalesce()
|
|
|
|
dtype = x.dtype
|
|
device = x.device
|
|
shape = tuple(x.shape)
|
|
J = torch.zeros((shape[dim],) + shape, dtype=dtype, device=device)
|
|
for i in range(shape[dim]):
|
|
if x.is_sparse:
|
|
sparse_dim = x.sparse_dim()
|
|
dense_dim = x.dense_dim()
|
|
if dim < sparse_dim:
|
|
ranges = []
|
|
for j, sz in enumerate(shape[:sparse_dim]):
|
|
if dim == j:
|
|
ranges.append([i])
|
|
else:
|
|
ranges.append(list(range(sz)))
|
|
indices = torch.tensor(list(itertools.product(*ranges)), dtype=torch.long, device=device).t()
|
|
values = torch.ones((indices.shape[1],) + shape[sparse_dim:], dtype=dtype, device=device)
|
|
else:
|
|
ranges = []
|
|
for j, sz in enumerate(shape[:sparse_dim]):
|
|
ranges.append(list(range(sz)))
|
|
indices = torch.tensor(list(itertools.product(*ranges)), dtype=torch.long, device=device).t()
|
|
values = torch.zeros((indices.shape[1],) + shape[sparse_dim:], dtype=dtype, device=device)
|
|
sv = [slice(None)] * (dense_dim + 1)
|
|
sv[dim - sparse_dim + 1] = i
|
|
values[tuple(sv)] = 1
|
|
v = torch.sparse_coo_tensor(indices, values, shape, dtype=dtype, device=device)
|
|
else:
|
|
v = torch.zeros_like(x)
|
|
sv = [slice(None)] * len(v.shape)
|
|
sv[dim] = i
|
|
v[tuple(sv)] = 1
|
|
x_ = x.clone()
|
|
x_.requires_grad_(True)
|
|
|
|
if log:
|
|
if x_.is_sparse:
|
|
y = torch.sparse.log_softmax(x_, dim)
|
|
else:
|
|
y = F.log_softmax(x_, dim)
|
|
else:
|
|
if x_.is_sparse:
|
|
y = torch.sparse.softmax(x_, dim)
|
|
else:
|
|
y = F.softmax(x_, dim)
|
|
# replace nan-s with zeros
|
|
y.data[y != y] = 0
|
|
y.backward(v)
|
|
g = x_.grad
|
|
if not g.is_sparse:
|
|
# replace nan-s with zeros
|
|
g.data[g != g] = 0
|
|
J[i] = g.to_dense() if g.is_sparse else g
|
|
return J
|
|
|
|
def test_op(sparse_dims, nnz, with_size, coalesced):
|
|
if isinstance(with_size, Number):
|
|
with_size = [with_size] * sparse_dims
|
|
|
|
x, i, v = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)
|
|
|
|
def sparse_log(x):
|
|
return torch.sparse_coo_tensor(x._indices(), x._values().log(),
|
|
x.size(), dtype=x.dtype, device=x.device)
|
|
|
|
for dim in range(x.sparse_dim() + x.dense_dim()):
|
|
# Check sparse softmax definition
|
|
|
|
# check Python sparse softmax
|
|
y = sparse_softmax(x, dim)
|
|
r1 = softmax_to_dense(x, dim)
|
|
r2 = y.to_dense()
|
|
self.assertEqual(r1, r2)
|
|
|
|
# check C++ sparse softmax
|
|
y1 = torch.sparse.softmax(x, dim)
|
|
self.assertEqual(y, y1)
|
|
|
|
# check C++ sparse log_softmax
|
|
ly1 = torch.sparse.log_softmax(x, dim)
|
|
self.assertEqual(ly1, sparse_log(y1))
|
|
|
|
# Check autograd support on sparse softmax
|
|
|
|
# check softmax Jacobian definition for dense input
|
|
x1 = to_dense(x, fill_value=float('-inf'))
|
|
J = softmax_jacobian_analytic(x1, dim)
|
|
assert J.shape[0] == x.shape[dim]
|
|
assert J.shape[dim + 1] == x.shape[dim]
|
|
|
|
# check softmax Jacobian from autograd, dense input
|
|
J2 = softmax_jacobian_autograd(x1, dim)
|
|
self.assertEqual(J, J2)
|
|
|
|
# check softmax Jacobian from autograd, sparse input
|
|
J3 = softmax_jacobian_autograd(x, dim)
|
|
self.assertEqual(J, J3)
|
|
|
|
'''
|
|
y = softmax(x, dim)
|
|
z = log(y) = log_softmax(x, dim)
|
|
Dy/Dx = J
|
|
Dz/Dx = Dz/Dy Dy/Dx = 1/y * J
|
|
=> J = J_log * y
|
|
'''
|
|
# log_softmax Jacobian from autograd, dense input
|
|
J2_log = softmax_jacobian_autograd(x1, dim, log=True)
|
|
|
|
# log_softmax Jacobian from autograd, sparse input
|
|
J3_log = softmax_jacobian_autograd(x, dim, log=True)
|
|
|
|
J = J.transpose(0, dim + 1)
|
|
J2_log = J2_log.transpose(0, dim + 1)
|
|
J3_log = J3_log.transpose(0, dim + 1)
|
|
self.assertEqual(J, J2_log * r1)
|
|
self.assertEqual(J, J3_log * r1)
|
|
|
|
if dim == 0:
|
|
# check dtype argument
|
|
other_dtype = torch.float32
|
|
y2 = torch.sparse.softmax(x, dim, dtype=other_dtype)
|
|
self.assertEqual(y2.dtype, other_dtype)
|
|
self.assertEqual(y2, y1.type(other_dtype))
|
|
|
|
ly2 = torch.sparse.log_softmax(x, dim, dtype=other_dtype)
|
|
self.assertEqual(ly2.dtype, other_dtype)
|
|
self.assertEqual(ly2, ly1.type(other_dtype))
|
|
|
|
test_op(1, 10, [3], coalesced)
|
|
test_op(1, 10, [2, 3], coalesced)
|
|
test_op(1, 10, [3, 2], coalesced)
|
|
test_op(2, 10, [2, 3, 4], coalesced)
|
|
test_op(2, 10, [3, 4], coalesced)
|
|
test_op(2, 5, [5, 4], coalesced)
|
|
test_op(2, 10, [3, 4, 2], coalesced)
|
|
test_op(3, 10, [3, 4, 2], coalesced)
|
|
test_op(3, 100, [3, 4, 2], coalesced)
|
|
test_op(3, 100, [3, 4, 2, 3], coalesced)
|
|
test_op(3, 100, [3, 4, 2, 3, 5, 2], coalesced)
|
|
test_op(4, 100, [3, 4, 2, 3, 5, 2], coalesced)
|
|
|
|
|
|
@dtypes(torch.double)
|
|
def test_softmax_zero_nnz(self, device, dtype):
|
|
t = torch.sparse_coo_tensor([[]], [], (3,), device=device, dtype=dtype)
|
|
out = torch.sparse.softmax(t, 0)
|
|
self.assertEqual(out.to_dense(), torch.zeros_like(t))
|
|
|
|
# TODO: Check after why ROCm's cusparseXcsrgemm2Nnz function doesn't return the same nnz value as CUDA
|
|
@skipIfRocm
|
|
@coalescedonoff
|
|
@dtypes(*floating_and_complex_types())
|
|
@dtypesIfCUDA(*floating_types_and(*[torch.half] if CUDA11OrLater and SM53OrLater else [],
|
|
*[torch.bfloat16] if CUDA11OrLater and SM80OrLater else [],
|
|
*[torch.complex64] if CUDA11OrLater else [],
|
|
*[torch.complex128] if CUSPARSE_SPMM_COMPLEX128_SUPPORTED else []))
|
|
@unittest.skipIf(TEST_WITH_CROSSREF, "not working with fake tensor")
|
|
@precisionOverride({torch.bfloat16: 1e-2, torch.float16: 1e-2, torch.complex64: 1e-2, torch.float32: 1e-2})
|
|
def test_sparse_matmul(self, device, dtype, coalesced):
|
|
"""
|
|
This function test `torch.sparse.mm` when both the mat1 and mat2 are sparse tensors.
|
|
"""
|
|
|
|
def ref_sparse_mm(a, b):
|
|
return a.to_dense() @ b.to_dense()
|
|
|
|
def grad_with_custom_sparsity_pattern_test_helper(sparse_dims, nnz, shape_a, shape_b):
|
|
def test_grad_dense(a_s, b_s, g_s):
|
|
a = a_s.to_dense().detach()
|
|
b = b_s.to_dense().detach()
|
|
g = g_s.to_dense().detach()
|
|
|
|
a.requires_grad_(True)
|
|
b.requires_grad_(True)
|
|
c = a @ b
|
|
c.backward(g)
|
|
return a.grad.sparse_mask(a_s.coalesce()), b.grad.sparse_mask(b_s.coalesce())
|
|
|
|
a, _, _ = self._gen_sparse(sparse_dims, nnz, shape_a, dtype, device, coalesced)
|
|
b, _, _ = self._gen_sparse(sparse_dims, nnz, shape_b, dtype, device, coalesced)
|
|
a.requires_grad_(True)
|
|
b.requires_grad_(True)
|
|
|
|
c = torch.sparse.mm(a, b)
|
|
c2 = c.to_dense().detach()
|
|
c2 = torch.rand_like(c2)
|
|
g = c2.sparse_mask(c.coalesce())
|
|
|
|
c.backward(g)
|
|
|
|
a_grad, b_grad = test_grad_dense(a, b, g)
|
|
self.assertEqual(a.grad, a_grad)
|
|
self.assertEqual(b.grad, b_grad)
|
|
|
|
def test_sparse_matmul(sparse_dims, nnz, shape_a, shape_b):
|
|
a, i_a, v_a = self._gen_sparse(sparse_dims, nnz, shape_a, dtype, device, coalesced)
|
|
b, i_b, v_b = self._gen_sparse(sparse_dims, nnz, shape_b, dtype, device, coalesced)
|
|
|
|
# dense implementation
|
|
r1 = ref_sparse_mm(a, b)
|
|
|
|
# cpp implementation
|
|
r2 = torch.sparse.mm(a, b)
|
|
self.assertEqual(r1, r2.to_dense())
|
|
|
|
if dtype in [torch.double, torch.cdouble]:
|
|
a.requires_grad_(True)
|
|
b.requires_grad_(True)
|
|
|
|
# check autograd support on sparse matmul
|
|
def fn(D1, D2):
|
|
return torch.sparse.mm(D1, D2).to_dense()
|
|
|
|
if a.is_cuda:
|
|
# For cuda, `nondet_tol` is set with `1e-5`
|
|
# This is because cuSparse sometimes returns approximate zero values like `~e-323`
|
|
# TODO: Check this cuSparse issue.
|
|
# This happens when you do chain multiplication `torch.sparse.mm` operations
|
|
gradcheck(fn, (a, b), check_sparse_nnz=True, nondet_tol=1e-5)
|
|
else:
|
|
gradcheck(fn, (a, b), check_sparse_nnz=True)
|
|
grad_with_custom_sparsity_pattern_test_helper(sparse_dims, nnz, shape_a, shape_b)
|
|
|
|
def test_error_cases():
|
|
def fn(sparse_dims, nnz, shape_a, shape_b):
|
|
a, i_a, v_a = self._gen_sparse(sparse_dims, nnz, shape_a, dtype, device, coalesced)
|
|
b, i_b, v_b = self._gen_sparse(sparse_dims, nnz, shape_b, dtype, device, coalesced)
|
|
r2 = torch.sparse.mm(a, b)
|
|
|
|
# This is not a matrix
|
|
self.assertRaises(RuntimeError, lambda: fn(3, 4, [2, 2, 2], [2, 2, 2]))
|
|
|
|
# Shapes does not
|
|
self.assertRaisesRegex(RuntimeError,
|
|
r"mat1 and mat2 shapes cannot be multiplied \(2x3 and 4x2\)",
|
|
lambda: fn(2, 10, [2, 3], [4, 2]))
|
|
|
|
def different_dtypes():
|
|
a, i_a, v_a = self._gen_sparse(2, 10, [2, 2], dtype, device, coalesced)
|
|
b, i_b, v_b = self._gen_sparse(2, 10, [2, 2], dtype, device, coalesced)
|
|
r2 = torch.sparse.mm(a.to(torch.float64), a.to(torch.float32))
|
|
|
|
self.assertRaisesRegex(RuntimeError, 'mat1 dtype Double does not match mat2 dtype Float', different_dtypes)
|
|
|
|
for n in range(2, 5):
|
|
for m in range(2, 8):
|
|
for p in range(2, 8):
|
|
test_sparse_matmul(2, 10, [n, m], [m, p])
|
|
|
|
test_sparse_matmul(2, 0, [0, 0], [0, 0])
|
|
test_sparse_matmul(2, 0, [0, 10], [10, 0])
|
|
test_error_cases()
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_assign(self, device, dtype, coalesced):
|
|
def assign_to():
|
|
a, i_a, v_a = self._gen_sparse(2, 5, [2, 3], dtype, device, coalesced)
|
|
a[0] = 100
|
|
|
|
self.assertRaises(TypeError, assign_to)
|
|
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_full_broadcast_to(self, device, dtype):
|
|
def can_broadcast(s0, s1):
|
|
s0 = tuple(reversed(s0))
|
|
s1 = tuple(reversed(s1))
|
|
for i in range(len(s0)):
|
|
if s0[i] != 1 and s0[i] != s1[i]:
|
|
return False
|
|
return True
|
|
sizes = (
|
|
(), (1,), (2,), (1, 1), (3, 1), (3, 2), (4, 1, 1), (4, 3, 2)
|
|
)
|
|
for s0, s1 in itertools.combinations(sizes, r=2):
|
|
t = make_tensor(s0, dtype=dtype, device=device, low=-9, high=9)
|
|
for sparse_dims in range(1, len(s0) + 1):
|
|
s = t.to_sparse(sparse_dims)
|
|
if can_broadcast(s0, s1):
|
|
t_res = torch.broadcast_to(t, s1)
|
|
s_res = torch._sparse_broadcast_to(s, s1)
|
|
torch._validate_sparse_coo_tensor_args(s_res._indices(), s_res._values(), s_res.shape)
|
|
if s_res.is_coalesced():
|
|
# ensure that is_coalesced is estimated correctly
|
|
self.assertEqual(s_res, torch.sparse_coo_tensor(s_res._indices(), s_res._values(), s_res.shape).coalesce())
|
|
self.assertEqual(s_res.to_dense(), t_res)
|
|
else:
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
r"The expanded size of the tensor \(\d\) "
|
|
r"must match the existing size \(\d\)"):
|
|
torch._sparse_broadcast_to(s, s1)
|
|
|
|
@coalescedonoff
|
|
@dtypes(torch.double, torch.cdouble)
|
|
def test_sparse_broadcast_to(self, device, dtype, coalesced):
|
|
def test(sparse_dims, nnz, with_size, new_size):
|
|
x = self._gen_sparse(sparse_dims, nnz, with_size, dtype, device, coalesced)[0]
|
|
y = self.safeToDense(x)
|
|
x1 = torch._sparse_broadcast_to(x, new_size)
|
|
y1 = y.broadcast_to(new_size)
|
|
self.assertEqual(self.safeToDense(x1), y1)
|
|
|
|
test(4, 6, [7, 3, 1, 3, 0], [7, 3, 4, 3, 0])
|
|
test(4, 6, [7, 3, 1, 3, 0], [2, 7, 3, 1, 3, 0])
|
|
test(4, 6, [7, 3, 1, 3, 1, 3], [7, 3, 1, 3, 2, 3])
|
|
test(4, 6, [7, 3, 1, 3, 2, 1], [7, 3, 1, 3, 2, 3])
|
|
|
|
@coalescedonoff
|
|
@dtypes(*all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16))
|
|
@precisionOverride({torch.bfloat16: 1e-2, torch.float16: 1e-2})
|
|
def test_sparse_dense_mul(self, device, dtype, coalesced):
|
|
skipTestIfUncoalesced = False
|
|
# This case always coalesce inputs and that could lead to loss of precision,
|
|
# hence it is inhibited for float16/bfloat16 by providing already coalesced tensors.
|
|
if not coalesced and dtype in {torch.float16, torch.bfloat16}:
|
|
skipTestIfUncoalesced = True
|
|
# to_dense is problematic for boolean non-coalesced CUDA tensors
|
|
# see https://github.com/pytorch/pytorch/issues/81648
|
|
if not coalesced and dtype == torch.bool and torch.device(device).type == "cuda":
|
|
skipTestIfUncoalesced = True
|
|
|
|
if skipTestIfUncoalesced:
|
|
self.skipTest(f"Test with dtype={dtype}, device={device} runs only with coalesced inputs")
|
|
|
|
shape = (2, 3, 4, 10)
|
|
nnz = 10
|
|
|
|
def check(self, s, d):
|
|
res = d * s
|
|
|
|
# check commutativity
|
|
self.assertEqual(res, s * d)
|
|
|
|
# check correctness
|
|
self.assertEqual(res.to_dense(), s.to_dense() * d)
|
|
|
|
# check in-placeness for dense
|
|
if d.dim() >= s.dim():
|
|
dc = d.clone()
|
|
self.assertEqual(d.mul_(s), dc.mul_(s.to_dense()))
|
|
|
|
# check in-placeness for sparse
|
|
if s.dim() >= d.dim():
|
|
# for sparse
|
|
sc = s.clone()
|
|
self.assertEqual(s.mul_(d).to_dense(), sc.to_dense().mul_(d))
|
|
|
|
for dim in range(len(shape) + 1):
|
|
sub_shape = shape[dim:]
|
|
sparse_dim = len(sub_shape) // 2
|
|
|
|
def check_empty(sparse_shape, nnz, dense_shape, coalesce):
|
|
from itertools import product
|
|
for nnz_val, shape_suffix in product((nnz, 0), ((), (0,))):
|
|
empty_sparse_shape = sparse_shape + shape_suffix
|
|
empty_dense_shape = dense_shape + shape_suffix
|
|
s = self._gen_sparse(sparse_dim, nnz_val, empty_sparse_shape, dtype, device, coalesce)[0]
|
|
d = make_tensor(empty_dense_shape, dtype=dtype, device=device)
|
|
check(self, s, d)
|
|
|
|
# check scalar multiplication
|
|
s = self._gen_sparse(sparse_dim, nnz, sub_shape, dtype, device, coalesced)[0]
|
|
for scalar in (True, 1, 1.0):
|
|
res_sparse = s * scalar
|
|
res_dense = s.to_dense() * scalar
|
|
# check correctness and dtype
|
|
self.assertEqual(s.to(res_sparse.dtype), res_sparse)
|
|
self.assertEqual(res_sparse.dtype, res_dense.dtype)
|
|
|
|
# Case 1: sparse broadcasts over dense
|
|
s = self._gen_sparse(sparse_dim, nnz, sub_shape, dtype, device, coalesced)[0]
|
|
d = make_tensor(shape, dtype=dtype, device=device)
|
|
check(self, s, d)
|
|
check_empty(sub_shape, nnz, shape, coalesced)
|
|
|
|
# Case 2: dense broadcasts over sparse
|
|
s = self._gen_sparse(3, nnz, shape, dtype, device, coalesced)[0]
|
|
d = make_tensor(sub_shape, dtype=dtype, device=device)
|
|
check(self, s, d)
|
|
check_empty(shape, nnz, sub_shape, coalesced)
|
|
|
|
@unittest.skipIf(not TEST_NUMPY, "NumPy is not availible")
|
|
@onlyCPU
|
|
@dtypes(*all_types_and_complex_and(torch.bool))
|
|
def test_sparse_spdiags(self, device, dtype):
|
|
|
|
make_diags = functools.partial(make_tensor, dtype=dtype, device=device)
|
|
make_offsets = functools.partial(torch.tensor, dtype=torch.long, device=device)
|
|
|
|
if TEST_SCIPY:
|
|
def reference(diags, offsets, shape):
|
|
return scipy.sparse.spdiags(diags, offsets, *shape).toarray()
|
|
|
|
else:
|
|
def reference(diags, offsets, shape):
|
|
result = torch.zeros(shape, dtype=dtype, device=device)
|
|
for i, off in enumerate(offsets):
|
|
res_view = result.diagonal(off)
|
|
data = diags[i]
|
|
if off > 0:
|
|
data = data[off:]
|
|
|
|
m = min(res_view.shape[0], data.shape[0])
|
|
res_view[:m] = data[:m]
|
|
return result
|
|
|
|
def check_valid(diags, offsets, shape, layout=None):
|
|
ref_out = reference(diags, offsets, shape)
|
|
out = torch.sparse.spdiags(diags, offsets, shape, layout=layout)
|
|
if layout is None:
|
|
ex_layout = torch.sparse_coo
|
|
else:
|
|
ex_layout = layout
|
|
out_dense = out.to_dense()
|
|
self.assertTrue(out.layout == ex_layout, f"Output layout {out.layout} expected {ex_layout}")
|
|
self.assertEqual(out_dense, ref_out, f"Result:\n{out_dense} does not match reference:\n{ref_out}")
|
|
|
|
def check_invalid(args, error):
|
|
with self.assertRaisesRegex(RuntimeError, error):
|
|
torch.sparse.spdiags(*args)
|
|
|
|
def valid_cases():
|
|
# some normal cases
|
|
yield (make_diags((1, 5)), make_offsets([0]), (5, 5))
|
|
yield (make_diags((3, 3)), make_offsets([-1, 0, 1]), (4, 4))
|
|
# noncontigous diags
|
|
yield (make_diags((5, 4), noncontiguous=True), make_offsets([-1, 1, 0, 2, -2]), (5, 5))
|
|
# noncontigous offsets
|
|
yield (make_diags((3, 4)), make_offsets([1, -1, 0, -2, 2])[::2], (5, 5))
|
|
# noncontigous diags + offsets
|
|
yield (make_diags((3, 4), noncontiguous=True), make_offsets([1, -1, 0, -2, 2])[::2], (5, 5))
|
|
# correct dimensionality, 2d, 2d , and shapes match, but the number of diagonals is zero
|
|
yield (make_diags((0, 3)), make_offsets([]), (3, 3))
|
|
# forward rotation of upper diagonals
|
|
yield (make_diags((3, 8)), make_offsets([1, 2, 3]), (4, 4))
|
|
# rotation exausts input space to read from
|
|
yield (make_diags((2, 3)), make_offsets([2, 1]), (3, 3))
|
|
# Simple cases repeated with special output format
|
|
yield (make_diags((1, 5)), make_offsets([0]), (5, 5), torch.sparse_csc)
|
|
yield (make_diags((3, 3)), make_offsets([-1, 0, 1]), (4, 4), torch.sparse_csr)
|
|
# vector diags
|
|
yield (make_diags((3, )), make_offsets([1]), (4, 4))
|
|
# Scalar offset
|
|
yield (make_diags((1, 3)), make_offsets(2), (4, 4))
|
|
# offsets out of range
|
|
yield (make_diags((1, 3)), make_offsets([3]), (3, 3))
|
|
yield (make_diags((1, 3)), make_offsets([-3]), (3, 3))
|
|
|
|
for case in valid_cases():
|
|
check_valid(*case)
|
|
|
|
def invalid_cases():
|
|
yield (make_diags((1, 3)), make_offsets([0]), (3, 2, 3)), "Output shape must be 2d"
|
|
yield (make_diags((2, 3)), make_offsets([[1, 2], [0, 3]]), (3, 3)), "Offsets must be scalar or vector"
|
|
yield (make_diags((3, 2, 3)), make_offsets([0, 1, 2]), (4, 4)), "Diagonals must be vector or matrix"
|
|
yield (make_diags((3, 3)), make_offsets([-1, 0]), (3, 3)),\
|
|
r"Number of diagonals \(\d\) does not match the number of offsets \(\d\)"
|
|
yield (make_diags((5,)), make_offsets([0, 1, 2, 3, 4]), (3, 3)),\
|
|
r"Number of diagonals \(\d\) does not match the number of offsets \(\d\)"
|
|
yield (make_diags((2, 2)), make_offsets([-1, 0]), (2, 3), torch.strided),\
|
|
r"Only output layouts \(\w+, \w+, \w+\) are supported, got \w+"
|
|
yield (make_diags((2, 5)), make_offsets([0, 0]), (5, 5)), "Offset tensor contains duplicate values"
|
|
yield (make_diags((1, 5)), make_offsets([0]).to(torch.int32), (5, 5)), r"Offset Tensor must have dtype Long but got \w+"
|
|
|
|
|
|
for case, error_regex in invalid_cases():
|
|
check_invalid(case, error_regex)
|
|
|
|
|
|
|
|
class TestSparseOneOff(TestCase):
|
|
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
|
|
def test_cuda_from_cpu(self):
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!"):
|
|
torch.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
|
|
torch.randn(4, 4, 4),
|
|
[3, 4, 4])
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!"):
|
|
torch.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
|
|
torch.randn(4, 4, 4, 0),
|
|
[3, 4, 4, 0])
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!"):
|
|
torch.sparse.FloatTensor(torch.LongTensor(1, 0).cuda(),
|
|
torch.randn(0, 4, 4, 0),
|
|
[0, 4, 4, 0])
|
|
|
|
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
|
|
def test_cuda_sparse_cpu_dense_add(self):
|
|
x = torch.zeros(3, 4, 4)
|
|
sparse_y = torch.cuda.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
|
|
torch.randn(4, 4, 4).cuda(),
|
|
[3, 4, 4])
|
|
with self.assertRaisesRegex(RuntimeError, "add: expected 'self' to be a CUDA tensor, but got a CPU tensor"):
|
|
x + sparse_y
|
|
|
|
x = torch.zeros(3, 4, 4, 0)
|
|
sparse_y = torch.cuda.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
|
|
torch.randn(4, 4, 4, 0).cuda(),
|
|
[3, 4, 4, 0])
|
|
with self.assertRaisesRegex(RuntimeError, "add: expected 'self' to be a CUDA tensor, but got a CPU tensor"):
|
|
x + sparse_y
|
|
|
|
x = torch.zeros(0, 4, 4, 0)
|
|
sparse_y = torch.cuda.sparse.FloatTensor(torch.LongTensor(1, 0).cuda(),
|
|
torch.randn(0, 4, 4, 0).cuda(),
|
|
[0, 4, 4, 0])
|
|
with self.assertRaisesRegex(RuntimeError, "add: expected 'self' to be a CUDA tensor, but got a CPU tensor"):
|
|
x + sparse_y
|
|
|
|
|
|
def _sparse_to_dense(tensor):
|
|
if tensor.dtype != torch.bool:
|
|
return tensor.to_dense()
|
|
|
|
# to_dense uses coalesce which isn't implemented for bool
|
|
return tensor.to(torch.int8).to_dense().to(torch.bool)
|
|
|
|
|
|
_sparse_unary_ops = ops(sparse_unary_ufuncs, dtypes=OpDTypes.supported,
|
|
allowed_dtypes=all_types_and_complex())
|
|
class TestSparseUnaryUfuncs(TestCase):
|
|
exact_dtype = True
|
|
|
|
|
|
@_sparse_unary_ops
|
|
def test_sparse_consistency(self, device, dtype, op):
|
|
sample = first_sample(self, op.sample_inputs(device, dtype))
|
|
assert isinstance(sample.input, torch.Tensor)
|
|
|
|
expected = op(sample.input, *sample.args, **sample.kwargs)
|
|
assert torch.is_tensor(expected)
|
|
output = op(sample.input.to_sparse(), *sample.args, **sample.kwargs)
|
|
assert torch.is_tensor(output)
|
|
self.assertEqual(_sparse_to_dense(output), expected)
|
|
|
|
@_sparse_unary_ops
|
|
def test_out(self, device, dtype, op):
|
|
if not op.supports_out:
|
|
self.skipTest("Skipped! Out not supported")
|
|
|
|
sample = first_sample(self, op.sample_inputs(device, dtype))
|
|
sample.input = sample.input.to_sparse()
|
|
expect = op(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
out = torch.zeros(sample.input.shape, device=device,
|
|
dtype=expect.dtype, layout=torch.sparse_coo)
|
|
op(sample.input, *sample.args, **sample.kwargs, out=out)
|
|
self.assertEqual(out, expect)
|
|
|
|
@_sparse_unary_ops
|
|
def test_inplace(self, device, dtype, op):
|
|
if op.inplace_variant is None:
|
|
self.skipTest("Skipped! Out not supported")
|
|
|
|
sample = first_sample(self, op.sample_inputs(device, dtype))
|
|
sample.input = sample.input.to_sparse().coalesce()
|
|
expect = op(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
if not torch.can_cast(expect.dtype, dtype):
|
|
with self.assertRaisesRegex(RuntimeError, "result type .* can't be cast to"):
|
|
op.inplace_variant(sample.input, *sample.args, **sample.kwargs)
|
|
return
|
|
|
|
actual = op.inplace_variant(sample.input, *sample.args, **sample.kwargs)
|
|
self.assertIs(actual, sample.input)
|
|
self.assertEqual(actual, expect)
|
|
|
|
@_sparse_unary_ops
|
|
def test_sparse_zero_dims(self, device, dtype, op):
|
|
# test 0x0 sparse_coo_tensor
|
|
indices = torch.empty(2, 0, dtype=torch.int64)
|
|
values = torch.empty(0, dtype=dtype)
|
|
sparse_0x0 = torch.sparse_coo_tensor(indices, values, (0, 0))
|
|
expected = torch.sparse_coo_tensor(indices, op(values), (0, 0))
|
|
actual = op(sparse_0x0)
|
|
self.assertEqual(expected, actual)
|
|
|
|
@_sparse_unary_ops
|
|
def test_sparse_zeros(self, device, dtype, op):
|
|
samples = op.sample_inputs(device, dtype)
|
|
|
|
zero_input = torch.zeros((), device=device, dtype=dtype)
|
|
sparse_input = torch.zeros((), dtype=dtype, device=device,
|
|
layout=torch.sparse_coo)
|
|
|
|
expect = op(zero_input)
|
|
actual = op(sparse_input)
|
|
self.assertEqual(expect, _sparse_to_dense(actual))
|
|
|
|
@ops(sparse_unary_ufuncs, dtypes=OpDTypes.supported,
|
|
allowed_dtypes=[torch.double, torch.cdouble])
|
|
def test_sparse_fn_grad(self, device, dtype, op):
|
|
if not op.supports_autograd:
|
|
self.skipTest("Skipped! Op doesn't support autograd")
|
|
|
|
for sample in op.sample_inputs(device, dtype):
|
|
sparse_input = sample.input.to_sparse().detach().requires_grad_(True)
|
|
|
|
def fn(x):
|
|
return _sparse_to_dense(
|
|
op(x, *sample.args, **sample.kwargs))
|
|
|
|
self.assertTrue(gradcheck(
|
|
fn,
|
|
(sparse_input,),
|
|
check_batched_grad=False,
|
|
check_grad_dtypes=True,
|
|
check_sparse_nnz=True,
|
|
nondet_tol=op.gradcheck_nondet_tol,
|
|
fast_mode=op.gradcheck_fast_mode))
|
|
|
|
|
|
class TestSparseMaskedReductions(TestCase):
|
|
exact_dtype = True
|
|
|
|
@ops(sparse_masked_reduction_ops)
|
|
def test_future_empty_dim(self, device, dtype, op):
|
|
"""Currently, `dim=()` in reductions operations means "reduce over
|
|
all dimensions" while in future, it will read "no reduce". See
|
|
https://github.com/pytorch/pytorch/issues/29137
|
|
|
|
For sparse masked reductions, we'll implement the current behavior.
|
|
|
|
For testing, we'll use samples with `dim=0` and map it to
|
|
`dim=()` until
|
|
torch.testing._internal.common_methods_invocations._generate_reduction_kwargs
|
|
is made to generate samples with `dim=()` for non-scalar
|
|
inputs. With this and after gh-29137 is resolved, this test
|
|
can be deleted. See also `torch._masked._canonical_dim`
|
|
implementation about changing the `dim=()` behavior.
|
|
"""
|
|
|
|
samples = op.sample_inputs_func(op, device, dtype, requires_grad=False)
|
|
op_name = op.name.replace('_masked.', '')
|
|
for sample_input in samples:
|
|
if sample_input.kwargs.get('dim') != 0:
|
|
continue
|
|
sample_input_kwargs = dict(sample_input.kwargs)
|
|
sample_input_kwargs['dim'] = () # reduce over all dimensions
|
|
|
|
t = sample_input.input
|
|
mask = sample_input_kwargs.get('mask')
|
|
if mask is None and op_name in {'prod', 'amax', 'amin'}:
|
|
# FIXME: for now reductions with non-zero reduction identity and
|
|
# unspecified mask are not supported for sparse COO
|
|
# tensors, see torch._masked.prod implementation
|
|
# for details.
|
|
continue
|
|
sparse_op_kwargs = dict(sample_input_kwargs)
|
|
actual = op(t.to_sparse(), *sample_input.args, **sample_input_kwargs)
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self.assertEqual(actual.layout, torch.sparse_coo)
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|
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expected = op(t, *sample_input.args, **sample_input_kwargs).to_sparse()
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self.assertEqual(actual, expected)
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|
|
|
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class TestSparseMeta(TestCase):
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exact_dtype = True
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|
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def test_basic(self):
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r = torch.empty(4, 4, layout=torch.sparse_coo, device='meta')
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self.assertTrue(r.is_meta)
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self.assertEqual(r.device.type, "meta")
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|
r2 = torch.empty_like(r)
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|
self.assertTrue(r2.is_meta)
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|
self.assertEqual(r, r2)
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|
r3 = torch.sparse_coo_tensor(size=(4, 4), device='meta')
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|
self.assertTrue(r3.is_meta)
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|
self.assertEqual(r, r3)
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|
r.sparse_resize_((4, 4), 1, 1)
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|
r.sparse_resize_and_clear_((4, 4, 4), 2, 1)
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|
self.assertEqual(r.sparse_dim(), 2)
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|
self.assertEqual(r.dense_dim(), 1)
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|
self.assertEqual(r._dimV(), 1)
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|
self.assertEqual(r._nnz(), 0)
|
|
# TODO: nnz zero sparse tensors should always be coalesced...
|
|
self.assertEqual(r.is_coalesced(), False)
|
|
r._coalesced_(True)
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|
self.assertEqual(r.is_coalesced(), True)
|
|
# TODO: this sort of aliasing will need to be handled by
|
|
# functionalization
|
|
self.assertEqual(r._indices(), torch.empty(2, 0, device='meta', dtype=torch.int64))
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|
self.assertEqual(r._values(), torch.empty(0, 4, device='meta'))
|
|
self.assertEqual(r.indices(), torch.empty(2, 0, device='meta', dtype=torch.int64))
|
|
self.assertEqual(r.values(), torch.empty(0, 4, device='meta'))
|
|
|
|
|
|
# e.g., TestSparseUnaryUfuncsCPU and TestSparseUnaryUfuncsCUDA
|
|
instantiate_device_type_tests(TestSparseUnaryUfuncs, globals(), except_for='meta')
|
|
|
|
instantiate_device_type_tests(TestSparseMaskedReductions, globals(), except_for='meta')
|
|
|
|
# e.g., TestSparseCPU and TestSparseCUDA
|
|
instantiate_device_type_tests(TestSparse, globals(), except_for='meta')
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|