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The multi-function network system was able to compose multiple
multi-functions into a new one and to evaluate that efficiently.
This functionality was heavily used by the particle nodes prototype
a year ago. However, since then we only used multi-functions
without the need to compose them in geometry nodes.
The upcoming "fields" in geometry nodes will need a way to
compose multi-functions again. Unfortunately, the code removed
in this commit was not ideal for this different kind of function
composition. I've been working on an alternative that will be added
separately when it becomes needed.
I've had to update all the function nodes, because their interface
depended on the multi-function network data structure a bit.
The actual multi-function implementations are still the same though.
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* Reduce code duplication.
* Give methods more standardized names (e.g. `move_to_initialized` -> `move_assign`).
* Support wrapping arbitrary C++ types, even those that e.g. are not copyable.
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This fixes a bad mistake by myself. Thanks Lukas Tönne for telling me.
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macOS Clang
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Part of a rename change in rBc5d38a2be8 was lost when committing.
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This is very similar to rB5613c61275fe6 and rB0061150e4c90d, basically
just exposing a `VMutableArray` method to its generic counterpart. This
is quite important for curve point attributes to avoid a lookup for
every point when there are multiple splines.
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Similar to how `GVArray_For_VArray` implements `materialize_impl` to
forward the work to its non-generic virtual array, we can do the same
thing for the mutable version, `GVMutableArray_For_VMutableArray`.
This commit should have no visible changes, since as far as I can tell
the only user of this class does not implement special materialize
methods anyway.
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Creating a shallow copy is sometimes useful to get a unique ptr
for a virtual array when one only has a reference. It shouldn't
be used usually, but sometimes its the fastest way to do correct
ownership handling.
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Sometimes functions expect a span instead of a virtual array.
If the virtual array is a span internally already, great. But if it is
not (e.g. the position attribute on a mesh), the elements have
to be copied over to a span.
This patch makes the copying process more efficient by giving
the compiler more opportunity for optimization.
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A virtual array is a data structure that is similar to a normal array
in that its elements can be accessed by an index. However, a virtual
array does not have to be a contiguous array internally. Instead, its
elements can be layed out arbitrarily while element access happens
through a virtual function call. However, the virtual array data
structures are designed so that the virtual function call can be avoided
in cases where it could become a bottleneck.
Most commonly, a virtual array is backed by an actual array/span or
is a single value internally, that is the same for every index.
Besides those, there are many more specialized virtual arrays like the
ones that provides vertex positions based on the `MVert` struct or
vertex group weights.
Not all attributes used by geometry nodes are stored in simple contiguous
arrays. To provide uniform access to all kinds of attributes, the attribute
API has to provide virtual array functionality that hides the implementation
details of attributes.
Before this refactor, the attribute API provided its own virtual array
implementation as part of the `ReadAttribute` and `WriteAttribute` types.
That resulted in unnecessary code duplication with the virtual array system.
Even worse, it bound many algorithms used by geometry nodes to the specifics
of the attribute API, even though they could also use different data sources
(such as data from sockets, default values, later results of expressions, ...).
This refactor removes the `ReadAttribute` and `WriteAttribute` types and
replaces them with `GVArray` and `GVMutableArray` respectively. The `GV`
stands for "generic virtual". The "generic" means that the data type contained
in those virtual arrays is only known at run-time. There are the corresponding
statically typed types `VArray<T>` and `VMutableArray<T>` as well.
No regressions are expected from this refactor. It does come with one
improvement for users. The attribute API can convert the data type
on write now. This is especially useful when writing to builtin attributes
like `material_index` with e.g. the Attribute Math node (which usually
just writes to float attributes, while `material_index` is an integer attribute).
Differential Revision: https://developer.blender.org/D10994
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This adds support for mutable virtual arrays and provides many utilities
for creating virtual arrays for various kinds of data. This commit is
preparation for D10994.
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When a function is executed for many elements (e.g. per point) it is often the case
that some parameters are different for every element and other parameters are
the same (there are some more less common cases). To simplify writing such
functions one can use a "virtual array". This is a data structure that has a value
for every index, but might not be stored as an actual array internally. Instead, it
might be just a single value or is computed on the fly. There are various tradeoffs
involved when using this data structure which are mentioned in `BLI_virtual_array.hh`.
It is called "virtual", because it uses inheritance and virtual methods.
Furthermore, there is a new virtual vector array data structure, which is an array
of vectors. Both these types have corresponding generic variants, which can be used
when the data type is not known at compile time. This is typically the case when
building a somewhat generic execution system. The function system used these virtual
data structures before, but now they are more versatile.
I've done this refactor in preparation for the attribute processor and other features of
geometry nodes. I moved the typed virtual arrays to blenlib, so that they can be used
independent of the function system.
One open question for me is whether all the generic data structures (and `CPPType`)
should be moved to blenlib as well. They are well isolated and don't really contain
any business logic. That can be done later if necessary.
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