Re-posted from: https://bkamins.github.io/julialang/2022/06/10/view.html
Introduction
Macros in Julia are a very nice element of the language. You can easily visually
identify macros as they are called using the @ prefix. I most often use the
@assert, @show, @spawn, @edit, and @time macros. However, one needs to
understand how macros work to confidently use them.
Today I want to write about typical situations when using
the @view and @views macros to create views can lead to surprising results.
The post was tested under Julia 1.7.2.
The @view macro and expression range
Assume we have a vector representing 24 months of data and we want to compute
correlation between the first 12 observations and the last 12 observations.
Here is a simple way to do it:
julia> using Random
julia> using Statistics
julia> Random.seed!(1234);
julia> x = randn(24);
julia> cor(x[1:12], x[13:24])
-0.018264675865149734
However, this operation copies data. If we want to avoid this we can use
views. To start let us check the view function:
julia> cor(view(x, 1:12), view(x, 13:24))
-0.018264675865149734
Let us check if indeed this reduces allocations first (using the @allocated
macro):
julia> @allocated cor(x[1:12], x[13:24])
336
julia> @allocated cor(view(x, 1:12), view(x, 13:24))
112
Indeed it does. Of course in our toy example the benefit is minimal.
Now we are ready to get to the main point of my post. Suppose we have
cor(x[1:12], x[13:24]) and want to do use views. As you can see in
codes above turning indexing into view call requires re-writing of the
expressions. This is where the @view macro comes handy.
julia> cor(@view x[1:12], @view x[13:24])
ERROR: LoadError: ArgumentError: Invalid use of @view macro: argument must be a reference expression A[...].
Or does it? We have some problem when using the macro. Let us check the
Julia Manual:
Macros are invoked with the following general syntax:
@name expr1 expr2 ...or@name(expr1, expr2, ...)
Since we invoked @view using the first style Julia eagerly considers
everything that follows it as a single expression. In this case the whole
x[1:12], @view x[13:24] part of code is passed to @view as a single
expression and we get an error.
We need to use the second macro invocation style in this case:
julia> cor(@view(x[1:12]), @view(x[13:24]))
-0.018264675865149734
Now all worked as expected. The only downside is that it feels a bit
inconvenient to write @view(...). Fortunately, Julia’s creators have thought
about it and designed a @views macro which turns every array slicing
(i.e., array[...]) operation in a passed expression to a view. In our case
this would be:
julia> @views cor(x[1:12], x[13:24])
-0.018264675865149734
The @views macro surprises
Let us check using the @macroexpand macro if indeed @views works as I promised:
julia> @macroexpand @views cor(x[1:12], x[13:24])
:(cor((Base.maybeview)(x, 1:12), (Base.maybeview)(x, 13:24)))
What is this strange Base.maybeview function? Is like getindex, but returns
a view for array slicing operations, while remaining equivalent to getindex
for scalar indices and non-array types. So it is almost the same as view.
Let us see the difference between using @view and @views:
julia> @view x[1]
0-dimensional view(::Vector{Float64}, 1) with eltype Float64:
0.9706563288552144
julia> @views x[1]
0.9706563288552144
Most of the time using a 0-dimensional view or a scalar will not make a
difference, however, sometimes it does. Let us have a look:
julia> similar(@view x[1])
0-dimensional Array{Float64, 0}:
1.40721121e-315
julia> similar(@views x[1])
ERROR: MethodError: no method matching similar(::Float64)
So things are, unfortunately, not as simple as you might expect, especially if
you are writing generic code and do not know upfront if you will use a scalar
index or not.
Having learned what I have written above you might think that the following code
will not work:
julia> x, y, z = [1, 2, 3], [2, 3, 4], [4, 5, 6]
([1, 2, 3], [2, 3, 4], [4, 5, 6])
julia> @views x[1] = y[1] + z[1]
6
julia> x
3-element Vector{Int64}:
6
2
3
However, it produces a correct result. What is the reason? Let us check:
julia> @macroexpand @views x[1] = y[1] + z[1]
:(x[1] = (Base.maybeview)(y, 1) + (Base.maybeview)(z, 1))
As we can see @views is smart enough not to apply the view transformation
to the left hand side of the assignment. This feature is not documented in its
docstring, but can be found (and is even commented about) in the source code of
the @views macro (in the _views function to be precise).
Interestingly, this rule is in play even in the example given in
the docsting of the @views macro. Let us check it:
julia> A = zeros(3, 3);
julia> @macroexpand @views for row in 1:3
b = A[row, :]
b[:] .= row
end
:(for row = 1:3
#= REPL[67]:2 =#
b = (Base.maybeview)(A, row, :)
#= REPL[67]:3 =#
b[:] .= row
end)
We can see that since b[:] is on left hand side of the assignment it is not
touched by @views.
Conclusions
What are the lessons learned?
- Macros in Julia are powerful. Well designed macros can make developer’s
life easier and code more readable. - Macros can be tricky. The most common problem when using macros is the
@name expr1style of invocation, which can process “too much” of your code
unexpectedly. @viewsis not equivalent to multiple invocations of@view. There are
subtle differences between them. Fortunately these differences matter
mostly in generic code and thus package developers need to be aware of them.