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cde365a7cd
The validation includes regular tensor inputs, batched tensor inputs, as well as hybrid tensor inputs. Pull Request resolved: https://github.com/pytorch/pytorch/pull/79385 Approved by: https://github.com/nikitaved, https://github.com/cpuhrsch
379 lines
12 KiB
Text
379 lines
12 KiB
Text
########## torch.float32/torch.int32/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0, 2, 4]),
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row_indices=tensor([0, 1, 0, 2]),
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values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(3, 2), nnz=4,
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layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0, 2, 4], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([0, 1, 0, 2], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([1., 2., 3., 4.], device='cuda:0')
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########## torch.float32/torch.int32/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0]),
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row_indices=tensor([], size=(0,)),
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values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
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layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([], device='cuda:0')
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########## torch.float32/torch.int32/batch_shape=(2,)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[0, 2, 4],
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[0, 3, 4]]),
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row_indices=tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]]),
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values=tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]]), device='cuda:0', size=(2, 3, 2),
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nnz=4, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[0, 2, 4],
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[0, 3, 4]], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]], device='cuda:0')
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########## torch.float32/torch.int32/batch_shape=(2, 3)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]]),
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row_indices=tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]]),
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values=tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]]), device='cuda:0',
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size=(2, 3, 3, 2), nnz=4, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]], device='cuda:0')
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########## torch.float64/torch.int32/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0, 2, 4]),
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row_indices=tensor([0, 1, 0, 2]),
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values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(3, 2), nnz=4,
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dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0, 2, 4], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([0, 1, 0, 2], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([1., 2., 3., 4.], device='cuda:0', dtype=torch.float64)
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########## torch.float64/torch.int32/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0]),
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row_indices=tensor([], size=(0,)),
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values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
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dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([], device='cuda:0', dtype=torch.float64)
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########## torch.float64/torch.int32/batch_shape=(2,)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[0, 2, 4],
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[0, 3, 4]]),
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row_indices=tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]]),
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values=tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]]), device='cuda:0', size=(2, 3, 2),
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nnz=4, dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[0, 2, 4],
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[0, 3, 4]], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]], device='cuda:0', dtype=torch.float64)
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########## torch.float64/torch.int32/batch_shape=(2, 3)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]]),
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row_indices=tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]]),
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values=tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]]), device='cuda:0',
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size=(2, 3, 3, 2), nnz=4, dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]], device='cuda:0', dtype=torch.int32)
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# _row_indices
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tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]], device='cuda:0', dtype=torch.int32)
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# _values
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tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]], device='cuda:0', dtype=torch.float64)
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########## torch.float32/torch.int64/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0, 2, 4]),
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row_indices=tensor([0, 1, 0, 2]),
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values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(3, 2), nnz=4,
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layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0, 2, 4], device='cuda:0')
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# _row_indices
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tensor([0, 1, 0, 2], device='cuda:0')
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# _values
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tensor([1., 2., 3., 4.], device='cuda:0')
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########## torch.float32/torch.int64/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0]),
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row_indices=tensor([], size=(0,)),
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values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
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layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0], device='cuda:0')
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# _row_indices
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tensor([], device='cuda:0', dtype=torch.int64)
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# _values
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tensor([], device='cuda:0')
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########## torch.float32/torch.int64/batch_shape=(2,)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[0, 2, 4],
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[0, 3, 4]]),
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row_indices=tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]]),
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values=tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]]), device='cuda:0', size=(2, 3, 2),
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nnz=4, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[0, 2, 4],
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[0, 3, 4]], device='cuda:0')
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# _row_indices
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tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]], device='cuda:0')
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# _values
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tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]], device='cuda:0')
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########## torch.float32/torch.int64/batch_shape=(2, 3)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]]),
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row_indices=tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]]),
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values=tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]]), device='cuda:0',
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size=(2, 3, 3, 2), nnz=4, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]], device='cuda:0')
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# _row_indices
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tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]], device='cuda:0')
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# _values
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tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]], device='cuda:0')
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########## torch.float64/torch.int64/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0, 2, 4]),
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row_indices=tensor([0, 1, 0, 2]),
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values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(3, 2), nnz=4,
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dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0, 2, 4], device='cuda:0')
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# _row_indices
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tensor([0, 1, 0, 2], device='cuda:0')
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# _values
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tensor([1., 2., 3., 4.], device='cuda:0', dtype=torch.float64)
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########## torch.float64/torch.int64/batch_shape=()/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([0]),
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row_indices=tensor([], size=(0,)),
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values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
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dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([0], device='cuda:0')
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# _row_indices
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tensor([], device='cuda:0', dtype=torch.int64)
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# _values
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tensor([], device='cuda:0', dtype=torch.float64)
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########## torch.float64/torch.int64/batch_shape=(2,)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[0, 2, 4],
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[0, 3, 4]]),
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row_indices=tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]]),
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values=tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]]), device='cuda:0', size=(2, 3, 2),
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nnz=4, dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[0, 2, 4],
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[0, 3, 4]], device='cuda:0')
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# _row_indices
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tensor([[0, 1, 0, 1],
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[0, 1, 2, 0]], device='cuda:0')
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# _values
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tensor([[1., 2., 3., 4.],
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[5., 6., 7., 8.]], device='cuda:0', dtype=torch.float64)
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########## torch.float64/torch.int64/batch_shape=(2, 3)/block_shape=() ##########
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# sparse tensor
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tensor(ccol_indices=tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]]),
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row_indices=tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]]),
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values=tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]]), device='cuda:0',
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size=(2, 3, 3, 2), nnz=4, dtype=torch.float64, layout=torch.sparse_csc)
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# _ccol_indices
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tensor([[[0, 2, 4],
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[0, 3, 4],
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[0, 1, 4]],
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[[0, 1, 4],
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[0, 2, 4],
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[0, 3, 4]]], device='cuda:0')
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# _row_indices
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tensor([[[0, 1, 0, 1],
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[0, 1, 2, 0],
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[0, 0, 1, 2]],
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[[1, 0, 1, 2],
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[0, 2, 0, 1],
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[0, 1, 2, 1]]], device='cuda:0')
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# _values
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tensor([[[ 1., 2., 3., 4.],
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[ 5., 6., 7., 8.],
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[ 9., 10., 11., 12.]],
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[[13., 14., 15., 16.],
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[17., 18., 19., 20.],
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[21., 22., 23., 24.]]], device='cuda:0', dtype=torch.float64)
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