[Numpy-svn] r6038 - trunk/doc/neps

numpy-svn@scip... numpy-svn@scip...
Sun Nov 16 02:49:18 CST 2008

Author: jarrod.millman
Date: 2008-11-16 02:49:17 -0600 (Sun, 16 Nov 2008)
New Revision: 6038

moved generalized ufunc proposal from the wiki

Added: trunk/doc/neps/generalized-ufuncs.rst
--- trunk/doc/neps/generalized-ufuncs.rst	2008-11-16 08:34:36 UTC (rev 6037)
+++ trunk/doc/neps/generalized-ufuncs.rst	2008-11-16 08:49:17 UTC (rev 6038)
@@ -0,0 +1,170 @@
+Generalized Universal Functions
+There is a general need for looping over not only functions on scalars
+but also over functions on vectors (or arrays), as explained on
+http://scipy.org/scipy/numpy/wiki/GeneralLoopingFunctions.  We propose
+to realize this concept by generalizing the universal functions
+(ufuncs), and provide a C implementation that adds ~500 lines
+to the numpy code base.  In current (specialized) ufuncs, the elementary
+function is limited to element-by-element operations, whereas the
+generalized version supports "sub-array" by "sub-array" operations.
+The Perl vector library PDL provides a similar functionality and its
+terms are re-used in the following.
+Each generalized ufunc has information associated with it that states
+what the "core" dimensionality of the inputs is, as well as the
+corresponding dimensionality of the outputs (the element-wise ufuncs
+have zero core dimensions).  The list of the core dimensions for all
+arguments is called the "signature" of a ufunc.  For example, the
+ufunc numpy.add has signature ``"(),()->()"`` defining two scalar inputs
+and one scalar output.
+Another example is (see the GeneralLoopingFunctions page) the function
+``inner1d(a,b)`` with a signature of ``"(i),(i)->()"``.  This applies the
+inner product along the last axis of each input, but keeps the
+remaining indices intact.  For example, where ``a`` is of shape ``(3,5,N)``
+and ``b`` is of shape ``(5,N)``, this will return an output of shape ``(3,5)``.
+The underlying elementary function is called 3*5 times.  In the
+signature, we specify one core dimension ``"(i)"`` for each input and zero core
+dimensions ``"()"`` for the output, since it takes two 1-d arrays and
+returns a scalar.  By using the same name ``"i"``, we specify that the two
+corresponding dimensions should be of the same size (or one of them is
+of size 1 and will be broadcasted).
+The dimensions beyond the core dimensions are called "loop" dimensions.  In
+the above example, this corresponds to ``(3,5)``.
+The usual numpy "broadcasting" rules apply, where the signature
+determines how the dimensions of each input/output object are split
+into core and loop dimensions:
+#. While an input array has a smaller dimensionality than the corresponding
+   number of core dimensions, 1's are pre-pended to its shape.
+#. The core dimensions are removed from all inputs and the remaining
+   dimensions are broadcasted; defining the loop dimensions.
+#. The output is given by the loop dimensions plus the output core dimensions.
+Elementary Function
+    Each ufunc consists of an elementary function that performs the
+    most basic operation on the smallest portion of array arguments
+    (e.g. adding two numbers is the most basic operation in adding two
+    arrays).  The ufunc applies the elementary function multiple times
+    on different parts of the arrays.  The input/output of elementary
+    functions can be vectors; e.g., the elementary function of inner1d
+    takes two vectors as input.
+    A signature is a string describing the input/output dimensions of
+    the elementary function of a ufunc.  See section below for more
+    details.
+Core Dimension
+    The dimensionality of each input/output of an elementary function
+    is defined by its core dimensions (zero core dimensions correspond
+    to a scalar input/output).  The core dimensions are mapped to the
+    last dimensions of the input/output arrays.
+Dimension Name
+    A dimension name represents a core dimension in the signature.
+    Different dimensions may share a name, indicating that they are of
+    the same size (or are broadcastable).
+Dimension Index
+    A dimension index is an integer representing a dimension name. It
+    enumerates the dimension names according to the order of the first
+    occurrence of each name in the signature.
+Details of Signature
+The signature defines "core" dimensionality of input and output
+variables, and thereby also defines the contraction of the
+dimensions.  The signature is represented by a string of the
+following format:
+* Core dimensions of each input or output array are represented by a
+  list of dimension names in parentheses, ``"(i_1,...,i_N)"``; a scalar 
+  input/output is denoted by ``"()"``.  Instead of ``"i_1"``, ``"i_2"``,
+  etc, one can use any valid Python variable name.
+* Dimension lists for different arguments are separated by ``","``.
+  Input/output arguments are separated by ``"->"``.
+* If one uses the same dimension name in multiple locations, this
+  enforces the same size (or broadcastable size) of the corresponding
+  dimensions.  
+The formal syntax of signatures is as follows::
+    <Signature>            ::= <Input arguments> "->" <Output arguments>
+    <Input arguments>      ::= <Argument list>
+    <Output arguments>     ::= <Argument list>
+    <Argument list>        ::= nil | <Argument> | <Argument> "," <Argument list>
+    <Argument>             ::= "(" <Core dimension list> ")"
+    <Core dimension list>  ::= nil | <Dimension name> |
+                               <Dimension name> "," <Core dimension list>
+    <Dimension name>       ::= valid Python variable name
+#. All quotes are for clarity.
+#. Core dimensions that share the same name must be broadcastable, as
+   the two ``i`` in our example above.  Each dimension name typically
+   corresponding to one level of looping in the elementary function's
+   implementation.
+#. White spaces are ignored.
+Here are some examples of signatures:
+  || add         || `"(),()->()"`           || ||
+  || inner1d     || `"(i),(i)->()"`         || ||
+  || sum1d       || `"(i)->()"`             || ||
+  || dot2d       || `"(m,n),(n,p)->(m,p)"`  || (matrix multiplication) ||
+  || outer_inner || `"(i,t),(j,t)->(i,j)"`  || (inner over the last dimension, outer over the second to last, and loop/broadcast over the rest.) ||
+C-API for implementing Elementary Functions
+The current interface remains unchanged, and ``PyUFunc_FromFuncAndData``
+can still be used to implement (specialized) ufuncs, consisting of
+scalar elementary functions.
+One can use ``PyUFunc_FromFuncAndDataAndSignature`` to declare a more
+general ufunc.  The argument list is the same as
+``PyUFunc_FromFuncAndData``, with an additional argument specifying the
+signature as C string.
+Furthermore, the callback function is of the same type as before,
+``void (*foo)(char **args, intp *dimensions, intp *steps, void *func)``.
+When invoked, ``args`` is a list of length ``nargs`` containing
+the data of all input/output arguments.  For a scalar elementary
+function, ``steps`` is also of length ``nargs``, denoting the strides used
+for the arguments. ``dimensions`` is a pointer to a single integer
+defining the size of the axis to be looped over.
+For a non-trivial signature, ``dimensions`` will also contain the sizes
+of the core dimensions as well, starting at the second entry.  Only
+one size is provided for each unique dimension name and the sizes are
+given according to the first occurrence of a dimension name in the
+The first ``nargs`` elements of ``steps`` remain the same as for scalar
+ufuncs.  The following elements contain the strides of all core
+dimensions for all arguments in order.
+For example, consider a ufunc with signature ``"(i,j),(i)->()"``.  In
+this case, ``args`` will contain three pointers to the data of the
+input/output arrays ``a``, ``b``, ``c``.  Furthermore, ``dimensions`` will be
+``[N, I, J]`` to define the size of ``N`` of the loop and the sizes ``I`` and ``J``
+for the core dimensions ``i`` and ``j``.  Finally, ``steps`` will be
+``[a_N, b_N, c_N, a_i, a_j, b_i]``, containing all necessary strides.

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