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@gchanan gchanan released this 07 Nov 17:19
v1.3.1
ee77ccb

Significant Fixes

Type Promotion: fixed a bug where type promotion, combined with non-contiguous tensors could compute incorrect results. (28253)

Version 1.3.0Version 1.3.1
>>> a = torch.tensor([[True,  True],
                      [False, True]])
# get a non-contiguous tensor
>>> a_transpose = a.t()
# type promote by comparing across dtypes (bool -> long)
>>> a_transpose == 0
# POTENTIALLY INCORRECT VALUES
      
>>> a = torch.tensor([[True,  True],
                      [False, True]])
# get a non-contiguous tensor
>>> a_transpose = a.t()
# type promote by comparing across dtypes (bool -> long)
>>> a_transpose == 0
tensor([[False,  True],
        [False, False]])

Type Promotion / Indexing: Fixed a Bug that Allowed Mixed-Dtype Indexing and assignment could lead to incorrect results. Mixed dtype operations of this form are currently disabled, as they were in 1.2. (28231)

Version 1.3.0Version 1.3.1
>>> a = torch.ones(5, 2, dtype=torch.float)
>>> b = torch.zeros(5, dtype=torch.long)
>>> a[:, [1]] = b.unsqueeze(-1)
>>> a
# POTENTIALLY INCORRECT VALUES
      
>>> a = torch.ones(5, 2, dtype=torch.float)
>>> b = torch.zeros(5, dtype=torch.long)
>>> a[:, [1]] = b.unsqueeze(-1)
RuntimeError: expected dtype Float but got dtype Long

torch.where(condition, x, y): fixed a bug on CPU where incorrect results could be returned if x and y were of different dtypes. Mixed dtype operations of this form are currently disabled, as they were in version 1.2. (29078)

Version 1.3.0Version 1.3.1
>>> x = torch.randn(2, 3)
>>> y = torch.randint(0, 10, (2, 3))
>>> torch.where(x < 0, x, y)
tensor(...)
# POTENTIALLY INCORRECT VALUES
      
>>> x = torch.randn(2, 3)
>>> y = torch.randint(0, 10, (2, 3))
>>> torch.where(x < 0, x, y)
RuntimeError: expected scalar type Float but found Long

Other Fixes

  • torch.argmax: fix regression on CUDA that disabled support for torch.float16 inputs. (28915)
  • NamedTensor: fix Python refcounting bug with Tensor.names. (28922)
  • Quantization: support deepcopy for quantized tensors. (28612)
  • Quantization: support nn.quantized.ReLU with inplace=True. (28710)
  • Documentation: torch.lgamma and torch.polygamma are now documented. (28964)
Assets 2