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pytorch/test/expect/TestSparseCPU.test_print_uncoalesced_cpu_float64.expect
Alexander 0d41122e61 Eliminate global usage of torch.set_default_dtype in sparse test (#56393) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56393

Fixes for  gh-56369

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D27913266

Pulled By: mruberry

fbshipit-source-id: 2c590d3a2188aae251184f08c1a6a2c4c570d150
2021-04-27 15:23:14 -07:00

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