196 lines
7.7 KiB
C++
196 lines
7.7 KiB
C++
//===- AffineCanonicalizationUtils.cpp - Affine Canonicalization in SCF ---===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// Utility functions to canonicalize affine ops within SCF op regions.
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//
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//===----------------------------------------------------------------------===//
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#include <utility>
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#include "mlir/Dialect/SCF/Utils/AffineCanonicalizationUtils.h"
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#include "mlir/Dialect/Affine/Analysis/AffineStructures.h"
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#include "mlir/Dialect/Affine/Analysis/Utils.h"
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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Affine/IR/AffineValueMap.h"
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#include "mlir/Dialect/SCF/IR/SCF.h"
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#include "mlir/Dialect/Utils/StaticValueUtils.h"
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#include "mlir/IR/AffineMap.h"
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#include "mlir/IR/Matchers.h"
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#include "mlir/IR/PatternMatch.h"
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#include "llvm/Support/Debug.h"
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#define DEBUG_TYPE "mlir-scf-affine-utils"
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using namespace mlir;
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using namespace presburger;
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static FailureOr<AffineApplyOp>
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canonicalizeMinMaxOp(RewriterBase &rewriter, Operation *op,
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FlatAffineValueConstraints constraints) {
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RewriterBase::InsertionGuard guard(rewriter);
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rewriter.setInsertionPoint(op);
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FailureOr<AffineValueMap> simplified =
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mlir::simplifyConstrainedMinMaxOp(op, std::move(constraints));
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if (failed(simplified))
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return failure();
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return rewriter.replaceOpWithNewOp<AffineApplyOp>(
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op, simplified->getAffineMap(), simplified->getOperands());
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}
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static LogicalResult
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addLoopRangeConstraints(FlatAffineValueConstraints &constraints, Value iv,
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OpFoldResult lb, OpFoldResult ub, OpFoldResult step,
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RewriterBase &rewriter) {
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// IntegerPolyhedron does not support semi-affine expressions.
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// Therefore, only constant step values are supported.
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auto stepInt = getConstantIntValue(step);
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if (!stepInt)
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return failure();
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unsigned dimIv = constraints.appendDimVar(iv);
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auto lbv = lb.dyn_cast<Value>();
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unsigned symLb = lbv ? constraints.appendSymbolVar(lbv)
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: constraints.appendSymbolVar(/*num=*/1);
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auto ubv = ub.dyn_cast<Value>();
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unsigned symUb = ubv ? constraints.appendSymbolVar(ubv)
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: constraints.appendSymbolVar(/*num=*/1);
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// If loop lower/upper bounds are constant: Add EQ constraint.
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std::optional<int64_t> lbInt = getConstantIntValue(lb);
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std::optional<int64_t> ubInt = getConstantIntValue(ub);
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if (lbInt)
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constraints.addBound(IntegerPolyhedron::EQ, symLb, *lbInt);
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if (ubInt)
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constraints.addBound(IntegerPolyhedron::EQ, symUb, *ubInt);
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// Lower bound: iv >= lb (equiv.: iv - lb >= 0)
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SmallVector<int64_t> ineqLb(constraints.getNumCols(), 0);
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ineqLb[dimIv] = 1;
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ineqLb[symLb] = -1;
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constraints.addInequality(ineqLb);
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// Upper bound
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AffineExpr ivUb;
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if (lbInt && ubInt && (*lbInt + *stepInt >= *ubInt)) {
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// The loop has at most one iteration.
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// iv < lb + 1
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// TODO: Try to derive this constraint by simplifying the expression in
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// the else-branch.
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ivUb =
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rewriter.getAffineSymbolExpr(symLb - constraints.getNumDimVars()) + 1;
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} else {
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// The loop may have more than one iteration.
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// iv < lb + step * ((ub - lb - 1) floorDiv step) + 1
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AffineExpr exprLb =
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lbInt
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? rewriter.getAffineConstantExpr(*lbInt)
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: rewriter.getAffineSymbolExpr(symLb - constraints.getNumDimVars());
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AffineExpr exprUb =
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ubInt
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? rewriter.getAffineConstantExpr(*ubInt)
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: rewriter.getAffineSymbolExpr(symUb - constraints.getNumDimVars());
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ivUb = exprLb + 1 + (*stepInt * ((exprUb - exprLb - 1).floorDiv(*stepInt)));
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}
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auto map = AffineMap::get(
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/*dimCount=*/constraints.getNumDimVars(),
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/*symbolCount=*/constraints.getNumSymbolVars(), /*result=*/ivUb);
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return constraints.addBound(IntegerPolyhedron::UB, dimIv, map);
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}
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/// Canonicalize min/max operations in the context of for loops with a known
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/// range. Call `canonicalizeMinMaxOp` and add the following constraints to
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/// the constraint system (along with the missing dimensions):
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///
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/// * iv >= lb
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/// * iv < lb + step * ((ub - lb - 1) floorDiv step) + 1
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///
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/// Note: Due to limitations of IntegerPolyhedron, only constant step sizes
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/// are currently supported.
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LogicalResult scf::canonicalizeMinMaxOpInLoop(RewriterBase &rewriter,
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Operation *op,
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LoopMatcherFn loopMatcher) {
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FlatAffineValueConstraints constraints;
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DenseSet<Value> allIvs;
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// Find all iteration variables among `minOp`'s operands add constrain them.
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for (Value operand : op->getOperands()) {
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// Skip duplicate ivs.
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if (llvm::is_contained(allIvs, operand))
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continue;
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// If `operand` is an iteration variable: Find corresponding loop
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// bounds and step.
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Value iv = operand;
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OpFoldResult lb, ub, step;
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if (failed(loopMatcher(operand, lb, ub, step)))
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continue;
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allIvs.insert(iv);
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if (failed(
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addLoopRangeConstraints(constraints, iv, lb, ub, step, rewriter)))
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return failure();
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}
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return canonicalizeMinMaxOp(rewriter, op, constraints);
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}
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/// Try to simplify the given affine.min/max operation `op` after loop peeling.
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/// This function can simplify min/max operations such as (ub is the previous
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/// upper bound of the unpeeled loop):
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/// ```
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/// #map = affine_map<(d0)[s0, s1] -> (s0, -d0 + s1)>
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/// %r = affine.min #affine.min #map(%iv)[%step, %ub]
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/// ```
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/// and rewrites them into (in the case the peeled loop):
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/// ```
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/// %r = %step
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/// ```
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/// min/max operations inside the partial iteration are rewritten in a similar
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/// way.
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///
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/// This function builds up a set of constraints, capable of proving that:
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/// * Inside the peeled loop: min(step, ub - iv) == step
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/// * Inside the partial iteration: min(step, ub - iv) == ub - iv
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///
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/// Returns `success` if the given operation was replaced by a new operation;
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/// `failure` otherwise.
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///
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/// Note: `ub` is the previous upper bound of the loop (before peeling).
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/// `insideLoop` must be true for min/max ops inside the loop and false for
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/// affine.min ops inside the partial iteration. For an explanation of the other
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/// parameters, see comment of `canonicalizeMinMaxOpInLoop`.
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LogicalResult scf::rewritePeeledMinMaxOp(RewriterBase &rewriter, Operation *op,
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Value iv, Value ub, Value step,
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bool insideLoop) {
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FlatAffineValueConstraints constraints;
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constraints.appendDimVar({iv, ub, step});
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if (auto constUb = getConstantIntValue(ub))
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constraints.addBound(IntegerPolyhedron::EQ, 1, *constUb);
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if (auto constStep = getConstantIntValue(step))
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constraints.addBound(IntegerPolyhedron::EQ, 2, *constStep);
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// Add loop peeling invariant. This is the main piece of knowledge that
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// enables AffineMinOp simplification.
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if (insideLoop) {
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// ub - iv >= step (equiv.: -iv + ub - step + 0 >= 0)
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// Intuitively: Inside the peeled loop, every iteration is a "full"
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// iteration, i.e., step divides the iteration space `ub - lb` evenly.
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constraints.addInequality({-1, 1, -1, 0});
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} else {
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// ub - iv < step (equiv.: iv + -ub + step - 1 >= 0)
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// Intuitively: `iv` is the split bound here, i.e., the iteration variable
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// value of the very last iteration (in the unpeeled loop). At that point,
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// there are less than `step` elements remaining. (Otherwise, the peeled
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// loop would run for at least one more iteration.)
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constraints.addInequality({1, -1, 1, -1});
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}
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return canonicalizeMinMaxOp(rewriter, op, constraints);
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}
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