v1.3.1

版本发布时间: 2019-11-08 01:19:44

pytorch/pytorch最新发布版本:v2.5.1(2024-10-30 01:58:24)

Significant Fixes

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

Version 1.3.0	Version 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]])}

Version 1.3.0

Version 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}

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 1.3.0	Version 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}

Version 1.3.0

Version 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 1.3.0	Version 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}

Version 1.3.0

Version 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)

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查看：2019-11-08发行的版本

v1.3.1

Significant Fixes

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

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)

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)

Other Fixes

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)