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android / platform / external / pytorch / ccc991ba29 / . / test / expect / TestSparseCompressedCUDA.test_print_SparseCSR_cuda.expect
blob: 918f2570807f7a3f859ec4c8f629caf9f28e8782 [file] [log] [blame]
########## torch.float32/torch.int32/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0, 2, 4]),
col_indices=tensor([0, 1, 0, 1]),
values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(2, 2), nnz=4,
layout=torch.sparse_csr)
# _crow_indices
tensor([0, 2, 4], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([0, 1, 0, 1], device='cuda:0', dtype=torch.int32)
# _values
tensor([1., 2., 3., 4.], device='cuda:0')
########## torch.float32/torch.int32/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0]),
col_indices=tensor([], size=(0,)),
values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
layout=torch.sparse_csr)
# _crow_indices
tensor([0], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([], device='cuda:0', dtype=torch.int32)
# _values
tensor([], device='cuda:0')
########## torch.float32/torch.int32/batch_shape=(2,)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[0, 2, 4],
[0, 2, 4]]),
col_indices=tensor([[0, 1, 0, 1],
[0, 1, 0, 1]]),
values=tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]]), device='cuda:0', size=(2, 2, 2),
nnz=4, layout=torch.sparse_csr)
# _crow_indices
tensor([[0, 2, 4],
[0, 2, 4]], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([[0, 1, 0, 1],
[0, 1, 0, 1]], device='cuda:0', dtype=torch.int32)
# _values
tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]], device='cuda:0')
########## torch.float32/torch.int32/batch_shape=(2, 3)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]]),
col_indices=tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]]),
values=tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]]), device='cuda:0', size=(2, 3, 2, 2),
nnz=4, layout=torch.sparse_csr)
# _crow_indices
tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]], device='cuda:0', dtype=torch.int32)
# _values
tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]], device='cuda:0')
########## torch.float64/torch.int32/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0, 2, 4]),
col_indices=tensor([0, 1, 0, 1]),
values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(2, 2), nnz=4,
dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([0, 2, 4], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([0, 1, 0, 1], device='cuda:0', dtype=torch.int32)
# _values
tensor([1., 2., 3., 4.], device='cuda:0', dtype=torch.float64)
########## torch.float64/torch.int32/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0]),
col_indices=tensor([], size=(0,)),
values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([0], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([], device='cuda:0', dtype=torch.int32)
# _values
tensor([], device='cuda:0', dtype=torch.float64)
########## torch.float64/torch.int32/batch_shape=(2,)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[0, 2, 4],
[0, 2, 4]]),
col_indices=tensor([[0, 1, 0, 1],
[0, 1, 0, 1]]),
values=tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]]), device='cuda:0', size=(2, 2, 2),
nnz=4, dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([[0, 2, 4],
[0, 2, 4]], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([[0, 1, 0, 1],
[0, 1, 0, 1]], device='cuda:0', dtype=torch.int32)
# _values
tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]], device='cuda:0', dtype=torch.float64)
########## torch.float64/torch.int32/batch_shape=(2, 3)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]]),
col_indices=tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]]),
values=tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]]), device='cuda:0', size=(2, 3, 2, 2),
nnz=4, dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]], device='cuda:0', dtype=torch.int32)
# _col_indices
tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]], device='cuda:0', dtype=torch.int32)
# _values
tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]], device='cuda:0', dtype=torch.float64)
########## torch.float32/torch.int64/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0, 2, 4]),
col_indices=tensor([0, 1, 0, 1]),
values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(2, 2), nnz=4,
layout=torch.sparse_csr)
# _crow_indices
tensor([0, 2, 4], device='cuda:0')
# _col_indices
tensor([0, 1, 0, 1], device='cuda:0')
# _values
tensor([1., 2., 3., 4.], device='cuda:0')
########## torch.float32/torch.int64/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0]),
col_indices=tensor([], size=(0,)),
values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
layout=torch.sparse_csr)
# _crow_indices
tensor([0], device='cuda:0')
# _col_indices
tensor([], device='cuda:0', dtype=torch.int64)
# _values
tensor([], device='cuda:0')
########## torch.float32/torch.int64/batch_shape=(2,)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[0, 2, 4],
[0, 2, 4]]),
col_indices=tensor([[0, 1, 0, 1],
[0, 1, 0, 1]]),
values=tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]]), device='cuda:0', size=(2, 2, 2),
nnz=4, layout=torch.sparse_csr)
# _crow_indices
tensor([[0, 2, 4],
[0, 2, 4]], device='cuda:0')
# _col_indices
tensor([[0, 1, 0, 1],
[0, 1, 0, 1]], device='cuda:0')
# _values
tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]], device='cuda:0')
########## torch.float32/torch.int64/batch_shape=(2, 3)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]]),
col_indices=tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]]),
values=tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]]), device='cuda:0', size=(2, 3, 2, 2),
nnz=4, layout=torch.sparse_csr)
# _crow_indices
tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]], device='cuda:0')
# _col_indices
tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]], device='cuda:0')
# _values
tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]], device='cuda:0')
########## torch.float64/torch.int64/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0, 2, 4]),
col_indices=tensor([0, 1, 0, 1]),
values=tensor([1., 2., 3., 4.]), device='cuda:0', size=(2, 2), nnz=4,
dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([0, 2, 4], device='cuda:0')
# _col_indices
tensor([0, 1, 0, 1], device='cuda:0')
# _values
tensor([1., 2., 3., 4.], device='cuda:0', dtype=torch.float64)
########## torch.float64/torch.int64/batch_shape=()/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([0]),
col_indices=tensor([], size=(0,)),
values=tensor([], size=(0,)), device='cuda:0', size=(0, 0), nnz=0,
dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([0], device='cuda:0')
# _col_indices
tensor([], device='cuda:0', dtype=torch.int64)
# _values
tensor([], device='cuda:0', dtype=torch.float64)
########## torch.float64/torch.int64/batch_shape=(2,)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[0, 2, 4],
[0, 2, 4]]),
col_indices=tensor([[0, 1, 0, 1],
[0, 1, 0, 1]]),
values=tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]]), device='cuda:0', size=(2, 2, 2),
nnz=4, dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([[0, 2, 4],
[0, 2, 4]], device='cuda:0')
# _col_indices
tensor([[0, 1, 0, 1],
[0, 1, 0, 1]], device='cuda:0')
# _values
tensor([[1., 2., 3., 4.],
[1., 2., 3., 4.]], device='cuda:0', dtype=torch.float64)
########## torch.float64/torch.int64/batch_shape=(2, 3)/block_shape=() ##########
# sparse tensor
tensor(crow_indices=tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]]),
col_indices=tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]]),
values=tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]]), device='cuda:0', size=(2, 3, 2, 2),
nnz=4, dtype=torch.float64, layout=torch.sparse_csr)
# _crow_indices
tensor([[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]],
[[0, 2, 4],
[0, 2, 4],
[0, 2, 4]]], device='cuda:0')
# _col_indices
tensor([[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]],
[[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 0, 1]]], device='cuda:0')
# _values
tensor([[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]],
[[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]]], device='cuda:0', dtype=torch.float64)