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e3e43d9d57
I did this a long time ago with a janky python script, but now clang-format has built-in support for this. I fed clang-format every line with a #include and let it re-sort things according to the precise LLVM rules for include ordering baked into clang-format these days. I've reverted a number of files where the results of sorting includes isn't healthy. Either places where we have legacy code relying on particular include ordering (where possible, I'll fix these separately) or where we have particular formatting around #include lines that I didn't want to disturb in this patch. This patch is *entirely* mechanical. If you get merge conflicts or anything, just ignore the changes in this patch and run clang-format over your #include lines in the files. Sorry for any noise here, but it is important to keep these things stable. I was seeing an increasing number of patches with irrelevant re-ordering of #include lines because clang-format was used. This patch at least isolates that churn, makes it easy to skip when resolving conflicts, and gets us to a clean baseline (again). git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@304787 91177308-0d34-0410-b5e6-96231b3b80d8
119 lines
4.6 KiB
C++
119 lines
4.6 KiB
C++
//===- ScalarEvolutionNormalization.cpp - See below -----------------------===//
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//
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// The LLVM Compiler Infrastructure
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements utilities for working with "normalized" expressions.
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// See the comments at the top of ScalarEvolutionNormalization.h for details.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/Analysis/ScalarEvolutionNormalization.h"
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#include "llvm/Analysis/LoopInfo.h"
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#include "llvm/Analysis/ScalarEvolutionExpressions.h"
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using namespace llvm;
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/// TransformKind - Different types of transformations that
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/// TransformForPostIncUse can do.
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enum TransformKind {
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/// Normalize - Normalize according to the given loops.
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Normalize,
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/// Denormalize - Perform the inverse transform on the expression with the
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/// given loop set.
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Denormalize
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};
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namespace {
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struct NormalizeDenormalizeRewriter
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: public SCEVRewriteVisitor<NormalizeDenormalizeRewriter> {
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const TransformKind Kind;
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// NB! Pred is a function_ref. Storing it here is okay only because
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// we're careful about the lifetime of NormalizeDenormalizeRewriter.
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const NormalizePredTy Pred;
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NormalizeDenormalizeRewriter(TransformKind Kind, NormalizePredTy Pred,
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ScalarEvolution &SE)
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: SCEVRewriteVisitor<NormalizeDenormalizeRewriter>(SE), Kind(Kind),
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Pred(Pred) {}
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const SCEV *visitAddRecExpr(const SCEVAddRecExpr *Expr);
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};
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} // namespace
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const SCEV *
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NormalizeDenormalizeRewriter::visitAddRecExpr(const SCEVAddRecExpr *AR) {
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SmallVector<const SCEV *, 8> Operands;
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transform(AR->operands(), std::back_inserter(Operands),
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[&](const SCEV *Op) { return visit(Op); });
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if (!Pred(AR))
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return SE.getAddRecExpr(Operands, AR->getLoop(), SCEV::FlagAnyWrap);
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// Normalization and denormalization are fancy names for decrementing and
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// incrementing a SCEV expression with respect to a set of loops. Since
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// Pred(AR) has returned true, we know we need to normalize or denormalize AR
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// with respect to its loop.
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if (Kind == Denormalize) {
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// Denormalization / "partial increment" is essentially the same as \c
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// SCEVAddRecExpr::getPostIncExpr. Here we use an explicit loop to make the
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// symmetry with Normalization clear.
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for (int i = 0, e = Operands.size() - 1; i < e; i++)
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Operands[i] = SE.getAddExpr(Operands[i], Operands[i + 1]);
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} else {
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assert(Kind == Normalize && "Only two possibilities!");
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// Normalization / "partial decrement" is a bit more subtle. Since
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// incrementing a SCEV expression (in general) changes the step of the SCEV
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// expression as well, we cannot use the step of the current expression.
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// Instead, we have to use the step of the very expression we're trying to
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// compute!
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//
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// We solve the issue by recursively building up the result, starting from
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// the "least significant" operand in the add recurrence:
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//
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// Base case:
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// Single operand add recurrence. It's its own normalization.
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//
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// N-operand case:
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// {S_{N-1},+,S_{N-2},+,...,+,S_0} = S
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//
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// Since the step recurrence of S is {S_{N-2},+,...,+,S_0}, we know its
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// normalization by induction. We subtract the normalized step
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// recurrence from S_{N-1} to get the normalization of S.
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for (int i = Operands.size() - 2; i >= 0; i--)
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Operands[i] = SE.getMinusSCEV(Operands[i], Operands[i + 1]);
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}
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return SE.getAddRecExpr(Operands, AR->getLoop(), SCEV::FlagAnyWrap);
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}
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const SCEV *llvm::normalizeForPostIncUse(const SCEV *S,
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const PostIncLoopSet &Loops,
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ScalarEvolution &SE) {
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auto Pred = [&](const SCEVAddRecExpr *AR) {
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return Loops.count(AR->getLoop());
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};
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return NormalizeDenormalizeRewriter(Normalize, Pred, SE).visit(S);
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}
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const SCEV *llvm::normalizeForPostIncUseIf(const SCEV *S, NormalizePredTy Pred,
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ScalarEvolution &SE) {
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return NormalizeDenormalizeRewriter(Normalize, Pred, SE).visit(S);
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}
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const SCEV *llvm::denormalizeForPostIncUse(const SCEV *S,
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const PostIncLoopSet &Loops,
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ScalarEvolution &SE) {
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auto Pred = [&](const SCEVAddRecExpr *AR) {
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return Loops.count(AR->getLoop());
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};
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return NormalizeDenormalizeRewriter(Denormalize, Pred, SE).visit(S);
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}
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