NEP 20 — Expansion of generalized universal function signatures#
Marten van Kerkwijk <email@example.com>
The proposal to add fixed (i) and flexible (ii) dimensions was accepted, while that to add broadcastable (iii) ones was deferred.
Generalized universal functions are, as their name indicates, generalization of universal functions: they operate on non-scalar elements. Their signature describes the structure of the elements they operate on, with names linking dimensions of the operands that should be the same. Here, it is proposed to extend the signature to allow the signature to indicate that a dimension (i) has fixed size; (ii) can be absent; and (iii) can be broadcast.
Each part of the proposal is driven by specific needs 1.
Fixed-size dimensions. Code working with spatial vectors often explicitly is for 2 or 3-dimensional space (e.g., the code from the Standards Of Fundamental Astronomy, which the author hopes to wrap using gufuncs for astropy 2). The signature should be able to indicate that. E.g., the signature of a function that converts a polar angle to a two-dimensional cartesian unit vector would currently have to be
()->(n), with there being no way to indicate that
nhas to equal 2. Indeed, this signature is particularly annoying since without putting in an output argument, the current gufunc wrapper code fails because it cannot determine
n. Similarly, the signature for an cross product of two 3-dimensional vectors has to be
(n),(n)->(n), with again no way to indicate that
nhas to equal 3. Hence, the proposal here to allow one to give numerical values in addition to variable names. Thus, angle to two-dimensional unit vector would be
()->(2); two angles to three-dimensional unit vector
(),()->(3); and that for the cross product of two three-dimensional vectors would be
Possibly missing dimensions. This part is almost entirely driven by the wish to wrap
matmulin a gufunc.
matmulstands for matrix multiplication, and if it did only that, it could be covered with the signature
(m,n),(n,p)->(m,p). However, it has special cases for when a dimension is missing, allowing either argument to be treated as a single vector, with the function thus becoming, effectively, vector-matrix, matrix-vector, or vector-vector multiplication (but with no broadcasting). To support this, it is suggested to allow postfixing a dimension name with a question mark to indicate that the dimension does not necessarily have to be present.
With this addition, the signature for
matmulcan be expressed as
(m?,n),(n,p?)->(m?,p?). This indicates that if, e.g., the second operand has only one dimension, for the purposes of the elementary function it will be treated as if that input has core shape
(n, 1), and the output has the corresponding core shape of
(m, 1). The actual output array, however, has the flexible dimension removed, i.e., it will have shape
(..., m). Similarly, if both arguments have only a single dimension, the inputs will be presented as having shapes
(n, 1)to the elementary function, and the output as
(1, 1), while the actual output array returned will have shape
(). In this way, the signature allows one to use a single elementary function for four related but different signatures,
Dimensions that can be broadcast. For some applications, broadcasting between operands makes sense. For instance, an
all_equalfunction that compares vectors in arrays could have a signature
(n),(n)->(), but this forces both operands to be arrays, while it would be useful also to check that, e.g., all parts of a vector are constant (maybe zero). The proposal is to allow the implementer of a gufunc to indicate that a dimension can be broadcast by post-fixing the dimension name with
|1. Hence, the signature for
(n|1),(n|1)->(). The signature seems handy more generally for “chained ufuncs”; e.g., another application might be in a putative ufunc implementing
Another example that arose in the discussion, is of a weighted mean, which might look like
weighted_mean(y, sigma[, axis, ...]), returning the mean and its uncertainty. With a signature of
(n),(n)->(),(), one would be forced to always give as many sigmas as there are data points, while broadcasting would allow one to give a single sigma for all points (which is still useful to calculate the uncertainty on the mean).
The proposed changes have all been implemented 3, 4, 5. These PRs
extend the ufunc structure with two new fields, each of size equal to the
number of distinct dimensions, with
core_dim_sizes holding possibly fixed
core_dim_flags holding flags indicating whether a dimension can
be missing or broadcast. To ensure we can distinguish between this new
version and previous versions, an unused entry
reserved1 is repurposed as
a version number.
In the implementation, care is taken that to the elementary function flagged dimensions are not treated any differently than non-flagged ones: for instance, sizes of fixed-size dimensions are still passed on to the elementary function (but the loop can now count on that size being equal to the fixed one given in the signature).
An implementation detail to be decided upon is whether it might be handy to
have a summary of all flags. This could possibly be stored in
(which currently is a bool), with non-zero continuing to indicate a gufunc,
but specific flags indicating whether or not a gufunc uses fixed, flexible, or
With the above, the formal definition of the syntax would become 4:
<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 | <Core dimension> | <Core dimension> "," <Core dimension list> <Core dimension> ::= <Dimension name> <Dimension modifier> <Dimension name> ::= valid Python variable name | valid integer <Dimension modifier> ::= nil | "|1" | "?"
All quotes are for clarity.
Unmodified core dimensions that share the same name must have the same size. Each dimension name typically corresponds to one level of looping in the elementary function’s implementation.
White spaces are ignored.
An integer as a dimension name freezes that dimension to the value.
If a name if suffixed with the
|1modifier, it is allowed to broadcast against other dimensions with the same name. All input dimensions must share this modifier, while no output dimensions should have it.
If the name is suffixed with the
?modifier, the dimension is a core dimension only if it exists on all inputs and outputs that share it; otherwise it is ignored (and replaced by a dimension of size 1 for the elementary function).
Examples of signatures 4:
Sum over last axis
Test for equality along axis, allowing comparison with a scalar
inner vector product
all four of the above at once,
except vectors cannot have loop
dimensions (ie, like
cross product for 3-vectors
inner over the last dimension, outer over the second to last, and loop/broadcast over the rest.
One possible worry is the change in ufunc structure. For most applications,
PyUFunc_FromDataAndSignature, this is entirely transparent.
Furthermore, by repurposing
reserved1 as a version number, code compiled
against older versions of numpy will continue to work (though one will get a
warning upon import of that code with a newer version of numpy), except if
code explicitly changes the
It was suggested instead of extending the signature, to have multiple
dispatch, so that, e.g.,
matmul would simply have the multiple signatures
it supports, i.e., instead of
(m?,n),(n,p?)->(m?,p?) one would have
(m,n),(n,p)->(m,p) | (n),(n,p)->(p) | (m,n),(n)->(m) | (n),(n)->(). A
disadvantage of this is that the developer now has to make sure that the
elementary function can deal with these different signatures. Furthermore,
the expansion quickly becomes cumbersome. For instance, for the
(n|1),(n|1)->(), one would have to have five entries:
(n),(n)->() | (n),(1)->() | (1),(n)->() | (n),()->() | (),(n)->(). For
(m|1,n|1,o|1),(m|1,n|1,o|1)->() (from the
test case in 4), it is not even worth writing out the expansion.
For broadcasting, the alternative suffix of
^ was suggested (as
broadcasting can be thought of as increasing the size of the array). This
seems less clear. Furthermore, it was wondered whether it should not just be
an all-or-nothing flag. This could be the case, though given the postfix
for flexible dimensions, arguably another postfix is clearer (as is the
The proposals here were discussed at fair length on the mailing list 6, 7. The main points of contention were whether the use cases were sufficiently strong. In particular, for frozen dimensions, it was argued that checks on the right number could be put in loop selection code. This seems much less clear for no benefit.
For broadcasting, the lack of examples of elementary functions that might need
it was noted, with it being questioned whether something like
was best done with a gufunc rather than as a special method on
One counter-argument to this would be that there is an actual PR for
all_equal 8. Another that even if one were to use a method, it would
be good to be able to express their signature (just as is possible at least
A final argument was that we were making the gufuncs too complex. This arguably holds for the dimensions that can be omitted, but that also has the strongest use case. The frozen dimensions has a very simple implementation and its meaning is obvious. The ability to broadcast is simple too, once the flexible dimensions are supported.
References and Footnotes#
Identified needs and suggestions for the implementation are not all by the author. In particular, the suggestion for fixed dimensions and initial implementation was by Jaime Frio (gh-5015), the suggestion of
?to indicate dimensions can be omitted was by Nathaniel Smith, and the initial implementation of that by Matti Picus (gh-11132).
Discusses implementations for
matmul: https://mail.python.org/pipermail/numpy-discussion/2018-May/077972.html, https://mail.python.org/pipermail/numpy-discussion/2018-May/078021.html
Logical gufuncs (includes
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