pytorch

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# shape: torch.Size([])
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# nnz: 2
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# sparse_dim: 0
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# indices shape: torch.Size([0, 2])
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# values shape: torch.Size([2])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 2)),
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       values=tensor([0, 1]),
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       size=(), nnz=2, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([], size=(0, 2), dtype=torch.int64)
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# _values
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tensor([0, 1], dtype=torch.int32)
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########## torch.float32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 2)),
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       values=tensor([0., 1.]),
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       size=(), nnz=2, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([], size=(0, 2)),
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       values=tensor([0., 1.]),
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       size=(), nnz=2, layout=torch.sparse_coo, requires_grad=True)
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# after addition
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tensor(indices=tensor([], size=(0, 2)),
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       values=tensor([0., 2.]),
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       size=(), nnz=2, layout=torch.sparse_coo, grad_fn=<AddBackward0>)
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# _indices
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tensor([], size=(0, 2), dtype=torch.int64)
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# _values
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tensor([0., 1.])
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# shape: torch.Size([0])
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# nnz: 10
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# sparse_dim: 0
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# indices shape: torch.Size([0, 10])
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# values shape: torch.Size([10, 0])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 10)),
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       values=tensor([], size=(10, 0)),
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       size=(0,), nnz=10, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([], size=(0, 10), dtype=torch.int64)
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# _values
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tensor([], size=(10, 0), dtype=torch.int32)
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########## torch.float64 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 10)),
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       values=tensor([], size=(10, 0)),
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       size=(0,), nnz=10, dtype=torch.float64, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([], size=(0, 10)),
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       values=tensor([], size=(10, 0)),
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       size=(0,), nnz=10, dtype=torch.float64, layout=torch.sparse_coo,
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       requires_grad=True)
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# after addition
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tensor(indices=tensor([], size=(0, 10)),
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       values=tensor([], size=(10, 0)),
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       size=(0,), nnz=10, dtype=torch.float64, layout=torch.sparse_coo,
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       grad_fn=<AddBackward0>)
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# _indices
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tensor([], size=(0, 10), dtype=torch.int64)
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# _values
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tensor([], size=(10, 0), dtype=torch.float64)
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# shape: torch.Size([2])
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# nnz: 3
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# sparse_dim: 0
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# indices shape: torch.Size([0, 3])
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# values shape: torch.Size([3, 2])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([[0, 0],
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                      [0, 1],
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                      [1, 1]]),
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       size=(2,), nnz=3, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([], size=(0, 3), dtype=torch.int64)
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# _values
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tensor([[0, 0],
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        [0, 1],
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        [1, 1]], dtype=torch.int32)
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########## torch.float32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([[0.0000, 0.3333],
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                      [0.6667, 1.0000],
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                      [1.3333, 1.6667]]),
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       size=(2,), nnz=3, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([[0.0000, 0.3333],
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                      [0.6667, 1.0000],
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                      [1.3333, 1.6667]]),
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       size=(2,), nnz=3, layout=torch.sparse_coo, requires_grad=True)
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# after addition
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([[0.0000, 0.6667],
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                      [1.3333, 2.0000],
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                      [2.6667, 3.3333]]),
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       size=(2,), nnz=3, layout=torch.sparse_coo, grad_fn=<AddBackward0>)
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# _indices
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tensor([], size=(0, 3), dtype=torch.int64)
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# _values
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tensor([[0.0000, 0.3333],
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        [0.6667, 1.0000],
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        [1.3333, 1.6667]])
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# shape: torch.Size([100, 3])
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# nnz: 3
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# sparse_dim: 1
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# indices shape: torch.Size([1, 3])
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# values shape: torch.Size([3, 3])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([[0, 1, 2]]),
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       values=tensor([[0, 0, 0],
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                      [0, 0, 1],
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                      [1, 1, 1]]),
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       size=(100, 3), nnz=3, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([[0, 1, 2]])
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# _values
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tensor([[0, 0, 0],
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        [0, 0, 1],
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        [1, 1, 1]], dtype=torch.int32)
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########## torch.float64 ##########
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# sparse tensor
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tensor(indices=tensor([[0, 1, 2]]),
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       values=tensor([[0.0000, 0.2222, 0.4444],
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                      [0.6667, 0.8889, 1.1111],
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                      [1.3333, 1.5556, 1.7778]]),
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       size=(100, 3), nnz=3, dtype=torch.float64, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([[0, 1, 2]]),
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       values=tensor([[0.0000, 0.2222, 0.4444],
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                      [0.6667, 0.8889, 1.1111],
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                      [1.3333, 1.5556, 1.7778]]),
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       size=(100, 3), nnz=3, dtype=torch.float64, layout=torch.sparse_coo,
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       requires_grad=True)
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# after addition
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tensor(indices=tensor([[0, 1, 2]]),
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       values=tensor([[0.0000, 0.4444, 0.8889],
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                      [1.3333, 1.7778, 2.2222],
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                      [2.6667, 3.1111, 3.5556]]),
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       size=(100, 3), nnz=3, dtype=torch.float64, layout=torch.sparse_coo,
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       grad_fn=<AddBackward0>)
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# _indices
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tensor([[0, 1, 2]])
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# _values
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tensor([[0.0000, 0.2222, 0.4444],
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        [0.6667, 0.8889, 1.1111],
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        [1.3333, 1.5556, 1.7778]], dtype=torch.float64)
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# shape: torch.Size([100, 20, 3])
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# nnz: 0
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# sparse_dim: 2
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# indices shape: torch.Size([2, 0])
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# values shape: torch.Size([0, 3])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(2, 0)),
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       values=tensor([], size=(0, 3)),
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       size=(100, 20, 3), nnz=0, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([], size=(2, 0), dtype=torch.int64)
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# _values
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tensor([], size=(0, 3), dtype=torch.int32)
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########## torch.float32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(2, 0)),
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       values=tensor([], size=(0, 3)),
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       size=(100, 20, 3), nnz=0, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([], size=(2, 0)),
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       values=tensor([], size=(0, 3)),
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       size=(100, 20, 3), nnz=0, layout=torch.sparse_coo, requires_grad=True)
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# after addition
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tensor(indices=tensor([], size=(2, 0)),
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       values=tensor([], size=(0, 3)),
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       size=(100, 20, 3), nnz=0, layout=torch.sparse_coo, grad_fn=<AddBackward0>)
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# _indices
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tensor([], size=(2, 0), dtype=torch.int64)
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# _values
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tensor([], size=(0, 3))
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# shape: torch.Size([10, 0, 3])
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# nnz: 3
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# sparse_dim: 0
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# indices shape: torch.Size([0, 3])
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# values shape: torch.Size([3, 10, 0, 3])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([], size=(3, 10, 0, 3)),
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       size=(10, 0, 3), nnz=3, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([], size=(0, 3), dtype=torch.int64)
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# _values
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tensor([], size=(3, 10, 0, 3), dtype=torch.int32)
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########## torch.float64 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([], size=(3, 10, 0, 3)),
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       size=(10, 0, 3), nnz=3, dtype=torch.float64, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([], size=(3, 10, 0, 3)),
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       size=(10, 0, 3), nnz=3, dtype=torch.float64, layout=torch.sparse_coo,
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       requires_grad=True)
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# after addition
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tensor(indices=tensor([], size=(0, 3)),
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       values=tensor([], size=(3, 10, 0, 3)),
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       size=(10, 0, 3), nnz=3, dtype=torch.float64, layout=torch.sparse_coo,
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       grad_fn=<AddBackward0>)
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# _indices
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tensor([], size=(0, 3), dtype=torch.int64)
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# _values
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tensor([], size=(3, 10, 0, 3), dtype=torch.float64)
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# shape: torch.Size([10, 0, 3])
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# nnz: 0
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# sparse_dim: 0
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# indices shape: torch.Size([0, 0])
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# values shape: torch.Size([0, 10, 0, 3])
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########## torch.int32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 0)),
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       values=tensor([], size=(0, 10, 0, 3)),
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       size=(10, 0, 3), nnz=0, dtype=torch.int32, layout=torch.sparse_coo)
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# _indices
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tensor([], size=(0, 0), dtype=torch.int64)
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# _values
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tensor([], size=(0, 10, 0, 3), dtype=torch.int32)
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########## torch.float32 ##########
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# sparse tensor
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tensor(indices=tensor([], size=(0, 0)),
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       values=tensor([], size=(0, 10, 0, 3)),
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       size=(10, 0, 3), nnz=0, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([], size=(0, 0)),
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       values=tensor([], size=(0, 10, 0, 3)),
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       size=(10, 0, 3), nnz=0, layout=torch.sparse_coo, requires_grad=True)
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# after addition
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tensor(indices=tensor([], size=(0, 0)),
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       values=tensor([], size=(0, 10, 0, 3)),
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       size=(10, 0, 3), nnz=0, layout=torch.sparse_coo, grad_fn=<AddBackward0>)
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# _indices
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tensor([], size=(0, 0), dtype=torch.int64)
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# _values
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tensor([], size=(0, 10, 0, 3))
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