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dcf385395d
Moves Sparse MPS testing from test_mps to test_sparse. Lots of skips now but I expect to remove them iteratively once ops are implemented Pull Request resolved: https://github.com/pytorch/pytorch/pull/161852 Approved by: https://github.com/malfet
266 lines
9.0 KiB
Plaintext
266 lines
9.0 KiB
Plaintext
# 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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device='mps:0', size=(), nnz=2, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([], device='mps:0', size=(0, 2), dtype=torch.int64)
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# _values
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tensor([0, 1], device='mps:0', 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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device='mps:0', 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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device='mps:0', size=(), nnz=2, 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, 1)),
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values=tensor([2.]),
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device='mps:0', size=(), nnz=1, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([], device='mps:0', size=(0, 2), dtype=torch.int64)
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# _values
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tensor([0., 1.], device='mps:0')
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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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device='mps:0', size=(0,), nnz=10, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([], device='mps:0', size=(0, 10), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', size=(10, 0), dtype=torch.int32)
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########## torch.float32 ##########
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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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device='mps:0', size=(0,), nnz=10, 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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device='mps:0', size=(0,), nnz=10, 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, 1)),
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values=tensor([], size=(1, 0)),
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device='mps:0', size=(0,), nnz=1, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([], device='mps:0', size=(0, 10), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', size=(10, 0))
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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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device='mps:0', size=(2,), nnz=3, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([], device='mps:0', 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]], device='mps:0', 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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device='mps:0', 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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device='mps:0', size=(2,), nnz=3, 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, 1)),
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values=tensor([[4.0000, 6.0000]]),
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device='mps:0', size=(2,), nnz=1, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([], device='mps:0', 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]], device='mps:0')
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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, 0]]),
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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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device='mps:0', size=(100, 3), nnz=3, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([[0, 1, 0]], device='mps:0')
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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]], device='mps:0', dtype=torch.int32)
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########## torch.float32 ##########
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# sparse tensor
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tensor(indices=tensor([[0, 1, 0]]),
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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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device='mps:0', size=(100, 3), nnz=3, layout=torch.sparse_coo)
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# after requires_grad_
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tensor(indices=tensor([[0, 1, 0]]),
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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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device='mps:0', size=(100, 3), nnz=3, 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]]),
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values=tensor([[2.6667, 3.5556, 4.4444],
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[1.3333, 1.7778, 2.2222]]),
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device='mps:0', size=(100, 3), nnz=2, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([[0, 1, 0]], device='mps:0')
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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]], device='mps:0')
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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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device='mps:0', size=(100, 20, 3), nnz=0, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([], device='mps:0', size=(2, 0), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', 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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device='mps:0', 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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device='mps:0', size=(100, 20, 3), nnz=0, 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=(2, 0)),
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values=tensor([], size=(0, 3)),
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device='mps:0', size=(100, 20, 3), nnz=0, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([], device='mps:0', size=(2, 0), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', 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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device='mps:0', size=(10, 0, 3), nnz=3, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([], device='mps:0', size=(0, 3), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', size=(3, 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, 3)),
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values=tensor([], size=(3, 10, 0, 3)),
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device='mps:0', size=(10, 0, 3), 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([], size=(3, 10, 0, 3)),
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device='mps:0', size=(10, 0, 3), nnz=3, 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, 1)),
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values=tensor([], size=(1, 10, 0, 3)),
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device='mps:0', size=(10, 0, 3), nnz=1, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([], device='mps:0', size=(0, 3), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', size=(3, 10, 0, 3))
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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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device='mps:0', size=(10, 0, 3), nnz=0, dtype=torch.int32,
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layout=torch.sparse_coo)
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# _indices
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tensor([], device='mps:0', size=(0, 0), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', 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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device='mps:0', 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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device='mps:0', size=(10, 0, 3), nnz=0, 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, 0)),
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values=tensor([], size=(0, 10, 0, 3)),
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device='mps:0', size=(10, 0, 3), nnz=0, layout=torch.sparse_coo,
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grad_fn=<AddBackward0>)
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# _indices
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tensor([], device='mps:0', size=(0, 0), dtype=torch.int64)
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# _values
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tensor([], device='mps:0', size=(0, 10, 0, 3))
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