Begin to document TensorBase methods (pytorch#466)

docs/source/tensors.rst

@@ -180,7 +180,6 @@ view of a storage and defines numeric operations on it.
    .. autoattribute:: is_cuda
       :annotation:
    .. automethod:: is_pinned
-   .. automethod:: is_same_size
    .. automethod:: is_set_to
    .. automethod:: is_signed
    .. automethod:: kthvalue
@@ -310,4 +309,3 @@ view of a storage and defines numeric operations on it.
    .. automethod:: view
    .. automethod:: view_as
    .. automethod:: zero_
-   .. automethod:: zeros_

torch/_tensor_docs.py

@@ -131,6 +131,10 @@
 
 add_docstr(torch._C.FloatTensorBase.apply_,
 """
+apply_(callable) -> Tensor
+
+Applies the function :attr:`callable` to each element in the tensor, replacing
+each element with the value returned by :attr:`callable`.
 """)
 
 add_docstr(torch._C.FloatTensorBase.asin,
@@ -212,6 +216,13 @@
 
 add_docstr(torch._C.FloatTensorBase.cauchy_,
 """
+cauchy_(generator=None, median=0, sigma=1) -> Tensor
+
+Fills the tensor with numbers drawn from the Cauchy distribution:
+
+.. math::
+
+    P(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - median)^2 + \sigma^2}
 """)
 
 add_docstr(torch._C.FloatTensorBase.ceil,
@@ -244,14 +255,34 @@
 
 add_docstr(torch._C.FloatTensorBase.clone,
 """
+clone() -> Tensor
+
+Returns a copy of the tensor. The copy has the same size and data type as the
+original tensor.
 """)
 
 add_docstr(torch._C.FloatTensorBase.contiguous,
 """
+contiguous() -> Tensor
+
+Returns a contiguous Tensor containing the same data as this tensor. If this
+tensor is contiguous, this function returns the original tensor.
 """)
 
 add_docstr(torch._C.FloatTensorBase.copy_,
 """
+copy_(src, async=False) -> Tensor
+
+Copies the elements from :attr:`src` into this tensor and returns this tensor.
+
+The source tensor should have the same number of elements as this tensor. It
+may be of a different data type or reside on a different device.
+
+Args:
+    src (Tensor): Source tensor to copy
+    async (bool): If True and this copy is between CPU and GPU, then the copy
+                  may occur asynchronously with respect to the host. For other
+                  copies, this argument has no effect.
 """)
 
 add_docstr(torch._C.FloatTensorBase.cos,
@@ -305,6 +336,9 @@
 
 add_docstr(torch._C.FloatTensorBase.data_ptr,
 """
+data_ptr() -> int
+
+Returns the address of the first element of this tensor.
 """)
 
 add_docstr(torch._C.FloatTensorBase.diag,
@@ -316,6 +350,9 @@
 
 add_docstr(torch._C.FloatTensorBase.dim,
 """
+dim() -> int
+
+Returns the number of dimensions of this tensor.
 """)
 
 add_docstr(torch._C.FloatTensorBase.dist,
@@ -355,6 +392,15 @@
 
 add_docstr(torch._C.FloatTensorBase.element_size,
 """
+element_size() -> int
+
+Returns the size in bytes of an individual element.
+
+Example:
+    >>> torch.FloatTensor().element_size()
+    4
+    >>> torch.ByteTensor().element_size()
+    1
 """)
 
 add_docstr(torch._C.FloatTensorBase.eq,
@@ -394,10 +440,20 @@
 
 add_docstr(torch._C.FloatTensorBase.exponential_,
 """
+exponential_(generator=None, lambd=1) -> Tensor
+
+Fills this tensor with elements drawn from the exponential distribution:
+
+.. math::
+
+    P(x) = \lambda e^{-\lambda x}
 """)
 
 add_docstr(torch._C.FloatTensorBase.fill_,
 """
+fill_(value) -> Tensor
+
+Fills this tensor with the specified value.
 """)
 
 add_docstr(torch._C.FloatTensorBase.floor,
@@ -472,26 +528,33 @@
 
 add_docstr(torch._C.FloatTensorBase.geometric_,
 """
+geometric_(generator=None, p) -> Tensor
+
+Fills this tensor with elements drawn from the geometric distribution:
+
+.. math::
+
+    P(X=k) = (1 - p)^{k - 1} p
+
 """)
 
 add_docstr(torch._C.FloatTensorBase.geqrf,
 """
 geqrf() -> (Tensor, Tensor)
 
-TODO: fix signature
 See :func:`torch.geqrf`
 """)
 
 add_docstr(torch._C.FloatTensorBase.ger,
 """
-
+ger(vec2) -> Tensor
 
 See :func:`torch.ger`
 """)
 
 add_docstr(torch._C.FloatTensorBase.gesv,
 """
-
+gesv(A) -> Tensor, Tensor
 
 See :func:`torch.gesv`
 """)
@@ -519,18 +582,86 @@
 
 add_docstr(torch._C.FloatTensorBase.index,
 """
+index(m) -> Tensor
+
+Selects elements from this tensor using a binary mask or along a given
+dimension. The expression ``tensor.index(m)`` is equivalent to ``tensor[m]``.
+
+Args:
+    m (int or ByteTensor or slice): The dimension or mask used to select elements
 """)
 
 add_docstr(torch._C.FloatTensorBase.index_add_,
 """
+index_add_(dim, index, tensor) -> Tensor
+
+Accumulate the elements of tensor into the original tensor by adding to the
+indices in the order given in index. The shape of tensor must exactly match the
+elements indexed or an error will be raised.
+
+Args:
+    dim (int): Dimension along which to index
+    index (LongTensor): Indices to select from tensor
+    tensor (Tensor): Tensor containing values to add
+
+Example:
+    >>> x = torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
+    >>> t = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    >>> index = torch.LongTensor([0, 2, 1])
+    >>> x.index_add_(0, index, t)
+    >>> x
+      2   3   4
+      8   9  10
+      5   6   7
+    [torch.FloatTensor of size 3x3]
 """)
 
 add_docstr(torch._C.FloatTensorBase.index_copy_,
 """
+index_copy_(dim, index, tensor) -> Tensor
+
+Copies the elements of tensor into the original tensor by selecting the
+indices in the order given in index. The shape of tensor must exactly match the
+elements indexed or an error will be raised.
+
+Args:
+    dim (int): Dimension along which to index
+    index (LongTensor): Indices to select from tensor
+    tensor (Tensor): Tensor containing values to copy
+
+Example:
+    >>> x = torch.Tensor(3, 3)
+    >>> t = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    >>> index = torch.LongTensor([0, 2, 1])
+    >>> x.index_copy_(0, index, t)
+    >>> x
+     1  2  3
+     7  8  9
+     4  5  6
+    [torch.FloatTensor of size 3x3]
 """)
 
 add_docstr(torch._C.FloatTensorBase.index_fill_,
 """
+index_fill_(dim, index, tensor) -> Tensor
+
+Fills the elements of the original tensor with value :attr:`val` by selecting
+the indices in the order given in index.
+
+Args:
+    dim (int): Dimension along which to index
+    index (LongTensor): Indices
+    val (float): Value to fill
+
+Example:
+    >>> x = torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    >>> index = torch.LongTensor([0, 2])
+    >>> x.index_fill_(1, index, -1)
+    >>> x
+    -1  2 -1
+    -1  5 -1
+    -1  8 -1
+    [torch.FloatTensor of size 3x3]
 """)
 
 add_docstr(torch._C.FloatTensorBase.index_select,
@@ -542,17 +673,16 @@
 
 add_docstr(torch._C.FloatTensorBase.inverse,
 """
-
+inverse() -> Tensor
 
 See :func:`torch.inverse`
 """)
 
 add_docstr(torch._C.FloatTensorBase.is_contiguous,
 """
-""")
+is_contiguous() -> bool
 
-add_docstr(torch._C.FloatTensorBase.is_same_size,
-"""
+Returns True if this tensor is contiguous in memory in C order.
 """)
 
 add_docstr(torch._C.FloatTensorBase.is_set_to,
@@ -1111,14 +1241,14 @@
 
 add_docstr(torch._C.FloatTensorBase.svd,
 """
-
+svd(some=True) -> (Tensor, Tensor, Tensor)
 
 See :func:`torch.svd`
 """)
 
 add_docstr(torch._C.FloatTensorBase.symeig,
 """
-
+symeig(eigenvectors=False, upper=True) -> (Tensor, Tensor)
 
 See :func:`torch.symeig`
 """)
@@ -1223,7 +1353,7 @@
 
 add_docstr(torch._C.FloatTensorBase.trtrs,
 """
-
+trtrs(A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)
 
 See :func:`torch.trtrs`
 """)
@@ -1244,13 +1374,20 @@
 
 add_docstr(torch._C.FloatTensorBase.unfold,
 """
-
+unfold(dim, size, step) -> Tensor
 
 See :func:`torch.unfold`
 """)
 
 add_docstr(torch._C.FloatTensorBase.uniform_,
 """
+uniform_(from=0, to=1) -> Tensor
+
+Fills this tensor with numbers sampled from the uniform distribution:
+
+.. math:
+
+    P(x) = \dfrac{1}{to - from}
 """)
 
 add_docstr(torch._C.FloatTensorBase.var,
@@ -1262,8 +1399,7 @@
 
 add_docstr(torch._C.FloatTensorBase.zero_,
 """
-""")
+zero_()
 
-add_docstr(torch._C.FloatTensorBase.zeros_,
-"""
+Fills this tensor with zeros.
 """)

torch/csrc/generic/methods/Tensor.cwrap

@@ -93,16 +93,6 @@ PyObject * THPTensor_(setIndex)(THPTensor *self, PyObject *args)
       long_args: True
 ]]
 
-[[
-  name: zeros_
-  cname: zeros
-  return: self
-  arguments:
-    - THTensor* self
-    - arg: THSize* size
-      long_args: True
-]]
-
 [[
   name: ones
   only_stateless: True

torch/csrc/generic/methods/TensorRandom.cwrap

@@ -164,9 +164,9 @@ static void THTensor_(normal_means_stddevs)(THTensor *self, THGenerator *gen, TH
     - THTensor* self
     - arg: THGenerator* generator
       default: THPDefaultGenerator->cdata
-    - arg: real location
+    - arg: real median
       default: 0
-    - arg: real scale
+    - arg: real sigma
       default: 1
 ]]
 
@@ -335,9 +335,9 @@ static void THTensor_(normal_means_stddevs)(THCState *_, THTensor *self, THTenso
   return: self
   arguments:
     - THTensor* self
-    - arg: double location
+    - arg: double median
       default: 0
-    - arg: double scale
+    - arg: double sigma
       default: 1
 ]]