Fix handling of unicode in torch._C._add_docstr (pytorch#487)

Author: colesbury

docs/source/torch.rst

@@ -32,13 +32,11 @@ Indexing, Slicing, Joining, Mutating Ops
 .. autofunction:: index_select
 .. autofunction:: masked_select
 .. autofunction:: nonzero
-.. autofunction:: scatter
 .. autofunction:: split
 .. autofunction:: squeeze
 .. autofunction:: stack
 .. autofunction:: t
 .. autofunction:: transpose
-.. autofunction:: unfold
 
 
 Random sampling

torch/_tensor_docs.py

@@ -760,17 +760,17 @@
 In-place version of :meth:`~Tensor.log`
 """)
 
-add_docstr(torch._C.FloatTensorBase.log_normal_, r"""
+add_docstr(torch._C.FloatTensorBase.log_normal_, u"""
 log_normal_(generator=None, mean=1, stdv=2)
 
 Fills this tensor with numbers samples from the log-normal distribution
-parameterized by the given mean (µ) and standard deviation (σ). Note that
+parameterized by the given mean (\u00B5) and standard deviation (\u03C3). Note that
 :attr:`mean` and :attr:`stdv` are the mean and standard deviation of the
 underlying normal distribution, and not of the returned distribution:
 
 .. math::
 
-    P(x) = \dfrac{1}{x \sigma \sqrt{2\pi}} e^{-\dfrac{(\ln x - \mu)^2}{2\sigma^2}}
+    P(x) = \\dfrac{1}{x \\sigma \\sqrt{2\\pi}} e^{-\\dfrac{(\\ln x - \\mu)^2}{2\\sigma^2}}
 """)
 
 add_docstr(torch._C.FloatTensorBase.lt,
@@ -1199,8 +1199,8 @@ def callable(a, b) -> number
 scatter_(input, dim, index, src) -> Tensor
 
 Writes all values from the Tensor :attr:`src` into self at the indices specified
-in the :attr:`index` Tensor. The indices are specified with respect to the 
-given dimension, dim, in the manner described in :meth:`~Tensor.gather`. 
+in the :attr:`index` Tensor. The indices are specified with respect to the
+given dimension, dim, in the manner described in :meth:`~Tensor.gather`.
 
 Note that, as for gather, the values of index must be between `0` and `(self.size(dim) -1)`
 inclusive and all values in a row along the specified dimension must be unique.
@@ -1215,25 +1215,25 @@ def callable(a, b) -> number
 
     >>> x = torch.rand(2, 5)
     >>> x
-    
+
      0.4319  0.6500  0.4080  0.8760  0.2355
      0.2609  0.4711  0.8486  0.8573  0.1029
     [torch.FloatTensor of size 2x5]
-    
+
     >>> torch.zeros(3, 5).scatter_(0, torch.LongTensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]]), x)
-    
+
      0.4319  0.4711  0.8486  0.8760  0.2355
      0.0000  0.6500  0.0000  0.8573  0.0000
      0.2609  0.0000  0.4080  0.0000  0.1029
     [torch.FloatTensor of size 3x5]
-    
+
     >>> z = torch.zeros(2, 4).scatter_(1, torch.LongTensor([[2], [3]]), 1.23)
     >>> z
-    
+
      0.0000  0.0000  1.2300  0.0000
      0.0000  0.0000  0.0000  1.2300
     [torch.FloatTensor of size 2x4]
-    
+
 """)
 
 add_docstr(torch._C.FloatTensorBase.select,
@@ -1580,12 +1580,12 @@ def callable(a, b) -> number
 """
 unfold(dim, size, step) -> Tensor
 
-Returns a tensor which contains all slices of size :attr:`size` in 
-the dimension :attr:`dim`. 
+Returns a tensor which contains all slices of size :attr:`size` in
+the dimension :attr:`dim`.
 
 Step between two slices is given by :attr:`step`.
 
-If `sizedim` is the original size of dimension dim, the size of dimension `dim` 
+If `sizedim` is the original size of dimension dim, the size of dimension `dim`
 in the returned tensor will be `(sizedim - size) / step + 1`
 
 An additional dimension of size size is appended in the returned tensor.
@@ -1599,7 +1599,7 @@ def callable(a, b) -> number
 
     >>> x = torch.range(1, 7)
     >>> x
-    
+
      1
      2
      3
@@ -1608,24 +1608,24 @@ def callable(a, b) -> number
      6
      7
     [torch.FloatTensor of size 7]
-    
+
     >>> x.unfold(0, 2, 1)
-    
+
      1  2
      2  3
      3  4
      4  5
      5  6
      6  7
     [torch.FloatTensor of size 6x2]
-    
+
     >>> x.unfold(0, 2, 2)
-    
+
      1  2
      3  4
      5  6
     [torch.FloatTensor of size 3x2]
-    
+
 """)
 
 add_docstr(torch._C.FloatTensorBase.uniform_,

torch/_torch_docs.py

@@ -2387,29 +2387,30 @@
 """)
 
 add_docstr(torch._C.multinomial,
-"""
+u"""
 multinomial(input, num_samples, replacement=False, out=None) -> LongTensor
 
 Returns a Tensor where each row
 contains :attr:`num_samples` indices sampled from the multinomial probability distribution
 located in the corresponding row of Tensor :attr:`input`.
 
-.. note:: The rows of :attr:`input` do not need to sum to one (in which case we use the values
-as weights), but must be non-negative and have a non-zero sum.
+.. note::
+    The rows of :attr:`input` do not need to sum to one (in which case we use the values
+    as weights), but must be non-negative and have a non-zero sum.
 
 Indices are ordered from left to right according to when each was sampled
 (first samples are placed in first column).
 
 If :attr:`input` is a vector, :attr:`out` is a matrix of size `num_samples`.
 
-If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape `m × n`.
+If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape `m \u00D7 n`.
 
 If replacement is `True`, samples are drawn with replacement.
 
 If not, they are drawn without replacement, which means that when a
 sample index is drawn for a row, it cannot be drawn again for that row.
 
-This implies the constraint that :attr:`num_samples` must be lower than :attr:`input` length 
+This implies the constraint that :attr:`num_samples` must be lower than :attr:`input` length
 (or number of columns of :attr:`input` if it is a matrix).
 
 Args:
@@ -2422,21 +2423,21 @@
 
     >>> weights = torch.Tensor([0, 10, 3, 0]) # create a Tensor of weights
     >>> torch.multinomial(weights, 4)
-    
+
      1
      2
      0
      0
     [torch.LongTensor of size 4]
-    
+
     >>> torch.multinomial(weights, 4, replacement=True)
-    
+
      1
      2
      1
      2
     [torch.LongTensor of size 4]
-    
+
 """)
 
 add_docstr(torch._C.mv,
@@ -2627,10 +2628,10 @@
 Returns a Tensor of random numbers drawn from separate normal distributions
 who's mean and standard deviation are given.
 
-The :attr:`means` is a Tensor with the mean of 
+The :attr:`means` is a Tensor with the mean of
 each output element's normal distribution
 
-The :attr:`stddevs` is a Tensor with the standard deviation of 
+The :attr:`stddevs` is a Tensor with the standard deviation of
 each output element's normal distribution
 
 The shapes of :attr:`means` and :attr:`stddevs` don't need to match.
@@ -2647,7 +2648,7 @@
 Example::
 
     torch.normal(means=torch.range(1, 10), stddevs=torch.range(1, 0.1, -0.1))
-    
+
      1.5104
      1.6955
      2.4895
@@ -2672,7 +2673,7 @@
 Example::
 
     >>> torch.normal(mean=0.5, stddevs=torch.range(1, 5))
-    
+
       0.5723
       0.0871
      -0.3783
@@ -2685,21 +2686,21 @@
 Similar to the function above, but the standard-deviations are shared among all drawn elements.
 
 Args:
-    means (Tensor): the Tensor of per-element means 
+    means (Tensor): the Tensor of per-element means
     stddevs (float, optional): the standard deviation for all distributions
     out (Tensor): the optional result Tensor
 
 Example::
 
     >>> torch.normal(means=torch.range(1, 5))
-    
+
      1.1681
      2.8884
      3.7718
      2.5616
      4.2500
     [torch.FloatTensor of size 5]
-    
+
 """)
 
 add_docstr(torch._C.numel,
@@ -2730,26 +2731,26 @@
 by the varargs :attr:`sizes`.
 
 Args:
-    sizes (*int): a set of ints defining the shape of the output Tensor.
+    sizes (int...): a set of ints defining the shape of the output Tensor.
     out (Tensor, optional): the result Tensor
 
 Example::
 
     >>> torch.ones(2, 3)
-    
+
      1  1  1
      1  1  1
     [torch.FloatTensor of size 2x3]
-    
+
     >>> torch.ones(5)
-    
+
      1
      1
      1
      1
      1
     [torch.FloatTensor of size 5]
-    
+
 """)
 
 add_docstr(torch._C.orgqr,
@@ -2938,25 +2939,25 @@
 The shape of the Tensor is defined by the varargs :attr:`sizes`.
 
 Args:
-    sizes (*int): a set of ints defining the shape of the output Tensor.
+    sizes (int...): a set of ints defining the shape of the output Tensor.
     out (Tensor, optional): the result Tensor
 
 Example::
 
     >>> torch.rand(4)
-    
+
      0.9193
      0.3347
      0.3232
      0.7715
     [torch.FloatTensor of size 4]
-    
+
     >>> torch.rand(2, 3)
-    
+
      0.5010  0.5140  0.0719
      0.1435  0.5636  0.0538
     [torch.FloatTensor of size 2x3]
-    
+
 """)
 
 add_docstr(torch._C.randn,
@@ -2969,25 +2970,25 @@
 The shape of the Tensor is defined by the varargs :attr:`sizes`.
 
 Args:
-    sizes (*int): a set of ints defining the shape of the output Tensor.
+    sizes (int...): a set of ints defining the shape of the output Tensor.
     out (Tensor, optional): the result Tensor
 
 Example::
 
     >>> torch.randn(4)
-    
+
     -0.1145
      0.0094
     -1.1717
      0.9846
     [torch.FloatTensor of size 4]
-    
+
     >>> torch.randn(2, 3)
-    
+
      1.4339  0.3351 -1.0999
      1.5458 -0.9643 -0.3558
     [torch.FloatTensor of size 2x3]
-    
+
 """)
 
 add_docstr(torch._C.randperm,
@@ -3939,24 +3940,24 @@
 by the varargs :attr:`sizes`.
 
 Args:
-    sizes (*int): a set of ints defining the shape of the output Tensor.
+    sizes (int...): a set of ints defining the shape of the output Tensor.
     out (Tensor, optional): the result Tensor
 
 Example::
 
     >>> torch.zeros(2, 3)
-    
+
      0  0  0
      0  0  0
     [torch.FloatTensor of size 2x3]
-    
+
     >>> torch.zeros(5)
-    
+
      0
      0
      0
      0
      0
     [torch.FloatTensor of size 5]
-    
+
 """)