We implement a function to compute the generating function corresponding
to a full-dimensional parametric polytope whose tangent cones are all
unimodular.
We fix a bug in unimodGenFunc to check the absolute value of the index.
We also implement Matrix<T>::negateMatrix() and Matrix<T>::scaleRow for
convenience.
We implement `computeNumTerms()`, which counts the number of terms in a
generating function by substituting the unit vector in it.
This is the main function in Barvinok's algorithm – the number of points
in a polytope is given by the number of terms in the generating function
corresponding to it.
We also modify the GeneratingFunction class to have `const` getters and
improve the simplification of QuasiPolynomials.
We implement two functions that are needed to compute the constant term
of a GF.
One finds a vector not orthogonal to all the non-null vectors in a given
set.
One computes the coefficient of any term in an arbitrary rational
function (quotient of two polynomials).
We implement a function that computes the generating function
corresponding to a unimodular cone.
The generating function for a polytope is obtained by summing these
generating functions over all tangent cones.
We add some basic type aliases and function definitions relating to
cones for Barvinok's algorithm.
These include functions to get the dual of a cone and find its index.
The dataflow analysis framework within MLIR allows to customize the
transfer function when a `call-like` operation is encuntered.
The check to see if the analysis was executed in intraprocedural mode
was executed after the check to see if the callee had the
CallableOpInterface, and thus intraprocedural analyses would behave as
interpocedural ones when performing indirect calls.
This commit fixes the issue by performing the check for
intraprocedurality first.
Dense forward analyses were already behaving correctly.
https://github.com/llvm/llvm-project/blob/main/mlir/lib/Analysis/DataFlow/DenseAnalysis.cpp#L63
Co-authored-by: massimo <mo.fioravanti@gmail.com>
Fixed a bug where IntMatrix determinant() had a bug where it would try to assign to a null
pointer.
Added a test case that triggers this bug to avoid regressions.
Fixed two issues: A SmallVector size that caused size-differences issue
(8 vs. 12). Thus removed this size restriction. Also a constant
parameter was causing an issue in a function not marked constant.
Define basic types and classes for Barvinok's algorithm, including
polyhedra, generating functions and quasi-polynomials.
The class definitions include methods for arithmetic manipulation,
printing, logical relations, etc.
This enum is used by dataflow analyses to indicate whether further
propagation is necessary to reach the fix point. Accidentally discarding
such a value will likely lead to propagation stopping early, leading to
incomplete or incorrect results. The most egregious example is the
duality between `join` on the analysis class, which triggers propagation
internally, and `join` on the lattice class that does not and expects
the caller to trigger it depending on the returned `ChangeResult`.
The core implementation of the dataflow anlysis framework is
interpocedural by design. While this offers better analysis precision,
it also comes with additional cost as it takes longer for the analysis
to reach the fixpoint state. Add a configuration mechanism to the
dataflow solver to control whether it operates inteprocedurally or not
to offer clients a choice.
As a positive side effect, this change also adds hooks for explicitly
processing external/opaque function calls in the dataflow analyses,
e.g., based off of attributes present in the the function declaration or
call operation such as alias scopes and modref available in the LLVM
dialect.
This change should not affect existing analyses and the default solver
configuration remains interprocedural.
Co-authored-by: Jacob Peng <jacobmpeng@gmail.com>
If the loop bound is not initialized, the analysis crashed, as it only checked for nullity. Also checking for initialization fixes the issue.
Signed-off-by: Victor Perez <victor.perez@codeplay.com>
Co-authored-by: Tsang, Whitney <whitney.tsang@intel.com>
Generalize affine fusion to work at any inner depth; fusing loops inside
other
affine.for or even inside scf.for or scf.while nests. Apply in post
order on
all affine nests on the pass' top-level operation.
Fix MDG init for blocks inside other affine nests.
Relax unnecessary requirement for unique vars during merge and align of
FlatLinearValueConstraints. There are several cases where
FlatLinearValueConstraints need to have duplicate Values for the
dimensions:
for eg. in dependence relation systems with source and destination
accesses
could have common loop IVs. `mergeAndAlign` can be done even in the
presence
of Values reappearing by simply aligning from left to right in that
order.
While at this, drop outdated comments; improve some debug messages.
