[mlir][linalg] Replace AffineMinSCFCanonicalizationPattern with SCF reimplementation

[springerm]

Summary

Use the new canonicalization pattern in the SCF dialect.

Depends On D107731

[ftynse]

Nice!

[springerm]

rebase

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rebase

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rebase

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rebase

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

 struct LinalgCopyVTWForwardingPattern
     : public OpRewritePattern<vector::TransferWriteOp> {
   using OpRewritePattern<vector::TransferWriteOp>::OpRewritePattern;
 
   LogicalResult matchAndRewrite(vector::TransferWriteOp xferOp,
                                 PatternRewriter &rewriter) const override;
 };
 
-using GetMinMaxExprFn =
-    std::function<Optional<std::pair<AffineExpr, AffineExpr>>(
-        Value value, SmallVectorImpl<Value> &dims,
-        SmallVectorImpl<Value> &symbols)>;
-
-/// Canonicalize AffineMinOp operations in the context of ops with a known range
-/// by:
-///   1. building an affine map where uses of the known ops are replaced by
-///   their min annd max expressions returned by the lambda `getMinMaxFn`.
-///   2. checking whether any of the results of this affine map is known to be
-///   greater than all other results.
-///   3. replacing the AffineMinOp by the result of (2).
-struct AffineMinRangeCanonicalizationPattern
-    : public OpRewritePattern<AffineMinOp> {
-  AffineMinRangeCanonicalizationPattern(MLIRContext *context,
-                                        GetMinMaxExprFn getMinMaxFn)
-      : OpRewritePattern<AffineMinOp>(context), getMinMaxFn(getMinMaxFn) {}
-  LogicalResult matchAndRewrite(AffineMinOp minOp,
-                                PatternRewriter &rewriter) const override;
-
-protected:
-  GetMinMaxExprFn getMinMaxFn;
-};
-
-/// Specialized version of `AffineMinRangeCanonicalizationPattern` pattern
-/// using `getSCFMinMaxExpr` to know the min and max expression of induction
-/// variables from scf loops.
-// TODO: move to a more appropriate place when it is determined. For now Linalg
-// depends both on Affine and SCF but they do not depend on each other.
-struct AffineMinSCFCanonicalizationPattern
-    : public AffineMinRangeCanonicalizationPattern {
-  static Optional<std::pair<AffineExpr, AffineExpr>>
-  getMinMax(Value value, SmallVectorImpl<Value> &dims,
-            SmallVectorImpl<Value> &symbols) {
-    return getSCFMinMaxExpr(value, dims, symbols);
-  }
-  AffineMinSCFCanonicalizationPattern(MLIRContext *context)
-      : AffineMinRangeCanonicalizationPattern(context, getMinMax) {}
-};
-
-/// Helper struct to return the results of `substituteMin`.
-struct AffineMapAndOperands {
-  AffineMap map;
-  SmallVector<Value> dims;
-  SmallVector<Value> symbols;
-};
-
-/// Traverse the dims of the AffineMap of `affineMinOp` and substitute
-/// dimensions with known range by new expressions involving the min or max
-/// expression:
-///   - If the AffineDimExpr mapped to a known value has a positive sign, it
-///     is replaced by the min expression.
-///   - If the AffineDimExpr mapped to a known value has a negative sign, it is
-///     replaced by the max expression.
-/// All known values are iteratively replaced.
-/// This is used as an intermediate step in computing bounding boxes and
-/// canonicalize AffineMinOps. All dim and symbol operands are assumed to have
-/// positive values (positive orthant assumptions).
-/// Return a new AffineMap, dims and symbols that have been canonicalized and
-/// simplified.
-AffineMapAndOperands substituteMin(AffineMinOp affineMinOp,
-                                   GetMinMaxExprFn getMinMaxExpr);
-
 /// Converts Convolution op into vector contraction.
 ///
 /// Conversion expects ConvOp to have dimensions marked in the *mask* as
 /// false of size 1. This ensures that the ConvOp can be lowered to vector
 /// contraction of dimensions marked in the *mask* as true.
 ///
 /// A good example for vectorization is ConvNHWCOp which is 2D Conv op
 /// with channels as the last dimension. Let's vectorize last 3 dimensions.

mlir/lib/Dialect/Linalg/Transforms/CodegenStrategy.cpp

 //===- CodegenStrategy.cpp - Linalg programmable codegen strategy ---------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements logic and helpers to expose Linalg transforms as
 // composable rewrite patterns through a programmable CodegenStrategy object.
 //
 //===----------------------------------------------------------------------===//
 
 #include "mlir/Dialect/Linalg/Transforms/CodegenStrategy.h"
 
 #include "mlir/Dialect/Linalg/Transforms/Hoisting.h"
+#include "mlir/Dialect/SCF/Transforms.h"
 #include "mlir/Dialect/Vector/VectorOps.h"
 #include "mlir/Dialect/Vector/VectorTransforms.h"
 #include "mlir/Pass/PassManager.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "mlir/Transforms/LoopUtils.h"
 #include "mlir/Transforms/Passes.h"
 
 using namespace mlir;
auto marker = (currentState == zeroState)
                       : linalg::LinalgTransformationFilter(
                             t->filter, currentState, nextState);
     stage1Patterns.emplace_back(t->buildRewritePatterns(context, marker));
     currentState = nextState;
   }
 
   RewritePatternSet stage2Patterns =
       linalg::getLinalgTilingCanonicalizationPatterns(context);
-  stage2Patterns.add<AffineMinSCFCanonicalizationPattern>(context);
+  scf::populateSCFLoopBodyCanonicalizationPatterns(stage2Patterns);
 
   auto stage3Transforms = [&](Operation *op) {
     // Some of these may be too aggressive as a stage 3 that is applied on each
     // stage 1 application and may have to be split out to post staged patterns
     // application (in which case they could just be passes, TBD).
     if (lateCodegenStrategyOptions.enableLICM) {
       op->walk([&](LoopLikeOpInterface loopLike) {
         LLVM_DEBUG(loopLike.print(llvm::dbgs() << "\nOriginal loop:\n"));

mlir/lib/Dialect/Linalg/Transforms/Tiling.cpp

 //===----------------------------------------------------------------------===//
 
 #include "PassDetail.h"
 #include "mlir/Dialect/Linalg/IR/LinalgTypes.h"
 #include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/SCF/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/AffineExpr.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/Transforms/FoldUtils.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 
 #include "llvm/Support/CommandLine.h"
 
applyTilingToLoopPatterns(LinalgTilingLoopType loopType, FuncOp funcOp,
                           ArrayRef<StringRef> distributionTypes = {}) {
   auto options = LinalgTilingOptions()
                      .setTileSizes(tileSizes)
                      .setLoopType(loopType)
                      .setDistributionTypes(distributionTypes);
   MLIRContext *ctx = funcOp.getContext();
   RewritePatternSet patterns(ctx);
   insertTilingPatterns(patterns, options);
-  patterns.add<AffineMinSCFCanonicalizationPattern>(patterns.getContext());
+  scf::populateSCFLoopBodyCanonicalizationPatterns(patterns);
   (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
   (void)applyPatternsAndFoldGreedily(
       funcOp, getLinalgTilingCanonicalizationPatterns(ctx));
   // Drop the marker.
   funcOp.walk([](LinalgOp op) {
     op->removeAttr(LinalgTransforms::kLinalgTransformMarker);
   });
 

mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp

if (stage3Lambda) {
         return failure();
       LLVM_DEBUG(DBGS() << "After 3rd stage, iter : " << iteration << "\n"
                         << *op);
     }
   }
   return success();
 }
 
-/// Traverse the `dims` and substitute known min or max expressions returned by
-/// the lambda |getMinMaxExpr|.
-static AffineMap substitute(AffineMap map, SmallVectorImpl<Value> &dims,
-                            SmallVectorImpl<Value> &symbols,
-                            GetMinMaxExprFn getMinMaxExpr) {
-  auto exprs = llvm::to_vector<4>(map.getResults());
-  for (AffineExpr &expr : exprs) {
-    bool substituted = true;
-    while (substituted) {
-      substituted = false;
-      for (unsigned dimIdx = 0; dimIdx < dims.size(); ++dimIdx) {
-        Value dim = dims[dimIdx];
-        auto minMax = getMinMaxExpr(dim, dims, symbols);
-        if (!minMax)
-          continue;
-        AffineExpr dimExpr = getAffineDimExpr(dimIdx, expr.getContext());
-        LLVM_DEBUG(DBGS() << "Subst: " << dim << " @ " << dimExpr << "\n");
-        LLVM_DEBUG(DBGS() << "Before: " << expr << "\n");
-        // Substitute occurrences of `dimExpr` by either the min expression or
-        // the max expression depending on whether the value is used with a
-        // positive or negative  coefficient.
-        AffineExpr substitutedExpr =
-            substWithMin(expr, dimExpr, minMax->first, minMax->second);
-        LLVM_DEBUG(DBGS() << "After: " << substitutedExpr << "\n");
-        substituted = (substitutedExpr != expr);
-        expr = substitutedExpr;
-      }
-    }
-
-    // Cleanup and simplify the results.
-    // This needs to happen outside of the loop iterating on dims.size() since
-    // it modifies dims.
-    SmallVector<Value, 4> operands(dims.begin(), dims.end());
-    operands.append(symbols.begin(), symbols.end());
-    auto map = AffineMap::get(dims.size(), symbols.size(), exprs,
-                              exprs.front().getContext());
-
-    LLVM_DEBUG({
-      DBGS() << "Map to simplify: " << map << "\n";
-      DBGS() << "Operands:\n";
-      for (Value v : operands)
-        DBGS() << v << "\n";
-    });
-
-    // Pull in affine.apply operations and compose them fully into the
-    // result.
-    fullyComposeAffineMapAndOperands(&map, &operands);
-    canonicalizeMapAndOperands(&map, &operands);
-    map = simplifyAffineMap(map);
-    // Assign the results.
-    exprs.assign(map.getResults().begin(), map.getResults().end());
-    dims.assign(operands.begin(), operands.begin() + map.getNumDims());
-    symbols.assign(operands.begin() + map.getNumDims(), operands.end());
-
-    LLVM_DEBUG(DBGS() << "Map simplified: " << map << "\n");
-  }
-
-  assert(!exprs.empty() && "Unexpected empty exprs");
-  return AffineMap::get(dims.size(), symbols.size(), exprs, map.getContext());
-}
-
-/// Traverse the dims of the AffineMap of `affineMinOp` and substitute
-/// dimensions with known range by new expressions involving the min or max
-/// expression:
-///   - If the AffineDimExpr mapped to a known value has a positive sign, it
-///     is replaced by the min expression.
-///   - If the AffineDimExpr mapped to a known value has a negative sign, it is
-///     replaced by the max expression.
-/// All known values are iteratively replaced.
-/// This is used as an intermediate step in computing bounding boxes and
-/// canonicalize AffineMinOps. All dim and symbol operands are assumed to have
-/// positive values (positive orthant assumptions).
-/// Return a new AffineMap, dims and symbols that have been canonicalized and
-/// simplified.
-AffineMapAndOperands
-mlir::linalg::substituteMin(AffineMinOp affineMinOp,
-                            GetMinMaxExprFn getMinMaxExpr) {
-  AffineMapAndOperands res{affineMinOp.getAffineMap(),
-                           SmallVector<Value>(affineMinOp.getDimOperands()),
-                           SmallVector<Value>(affineMinOp.getSymbolOperands())};
-  res.map = substitute(affineMinOp.getAffineMap(), res.dims, res.symbols,
-                       getMinMaxExpr);
-  return res;
-}
-
-LogicalResult AffineMinRangeCanonicalizationPattern::matchAndRewrite(
-    AffineMinOp minOp, PatternRewriter &rewriter) const {
-  LLVM_DEBUG(DBGS() << "Canonicalize AffineMinSCF: " << *minOp.getOperation()
-                    << "\n");
-
-  auto affineMapAndOperands = substituteMin(minOp, getMinMaxFn);
-  AffineMap map = affineMapAndOperands.map;
-
-  LLVM_DEBUG(DBGS() << "Resulting map: " << map << "\n");
-
-  // Check whether any of the expressions, when subtracted from all other
-  // expressions, produces only >= 0 constants. If so, it is the min.
-  for (auto e : minOp.getAffineMap().getResults()) {
-    LLVM_DEBUG(DBGS() << "Candidate min: " << e << "\n");
-    if (!e.isSymbolicOrConstant())
-      continue;
-
-    auto isNonPositive = [](AffineExpr e) {
-      if (auto cst = e.dyn_cast<AffineConstantExpr>())
-        return cst.getValue() < 0;
-      return true;
-    };
-
-    // Build the subMap and check everything is statically known to be
-    // positive.
-    SmallVector<AffineExpr, 4> subExprs;
-    subExprs.reserve(map.getNumResults());
-    for (auto ee : map.getResults())
-      subExprs.push_back(ee - e);
-    MLIRContext *ctx = minOp.getContext();
-    AffineMap subMap = simplifyAffineMap(
-        AffineMap::get(map.getNumDims(), map.getNumSymbols(), subExprs, ctx));
-    LLVM_DEBUG(DBGS() << "simplified subMap: " << subMap << "\n");
-    if (llvm::any_of(subMap.getResults(), isNonPositive))
-      continue;
-
-    // Static min found.
-    if (auto cst = e.dyn_cast<AffineConstantExpr>()) {
-      rewriter.replaceOpWithNewOp<ConstantIndexOp>(minOp, cst.getValue());
-    } else {
-      auto resultMap = AffineMap::get(0, map.getNumSymbols(), {e}, ctx);
-      SmallVector<Value> resultOperands = affineMapAndOperands.dims;
-      llvm::append_range(resultOperands, affineMapAndOperands.symbols);
-      canonicalizeMapAndOperands(&resultMap, &resultOperands);
-      resultMap = simplifyAffineMap(resultMap);
-      rewriter.replaceOpWithNewOp<AffineApplyOp>(minOp, resultMap,
-                                                 resultOperands);
-    }
-    return success();
-  }
-
-  return failure();
-}
-
 static SmallVector<StringRef> getNParallelLoopsAttrs(unsigned nParallelLoops) {
   return SmallVector<StringRef>(nParallelLoops, getParallelIteratorTypeName());
 }
 
 /// Rewrite a PadTensorOp into a sequence of InitTensorOp, FillOp (to initialize
 /// with pad_val) and GenericOp (to copy contents).
 LogicalResult PadTensorOpTransformationPattern::matchAndRewrite(
     linalg::PadTensorOp padOp, PatternRewriter &rewriter) const {

mlir/lib/Dialect/SCF/Transforms/Utils.cpp

for (Block &block : region.getBlocks()) {
           if (!enclosesPloops)
             result.push_back(ploop);
         }
       }
     }
   }
   return rootEnclosesPloops;
 }
-
-/// Given the `lbVal`, `ubVal` and `stepVal` of a loop, append `lbVal` and
-/// `ubVal` to `dims` and `stepVal` to `symbols`.
-/// Create new AffineDimExpr (`%lb` and `%ub`) and AffineSymbolExpr (`%step`)
-/// with positions matching the newly appended values. Then create a min
-/// expression (i.e. `%lb`) and a max expression
-/// (i.e. `%lb + %step * floordiv(%ub -1 - %lb, %step)`.
-static std::pair<AffineExpr, AffineExpr>
-getMinMaxLoopIndVar(Value lbVal, Value ubVal, Value stepVal,
-                    SmallVectorImpl<Value> &dims,
-                    SmallVectorImpl<Value> &symbols) {
-  MLIRContext *ctx = lbVal.getContext();
-  AffineExpr lb = getAffineDimExpr(dims.size(), ctx);
-  dims.push_back(lbVal);
-  AffineExpr ub = getAffineDimExpr(dims.size(), ctx);
-  dims.push_back(ubVal);
-  AffineExpr step = getAffineSymbolExpr(symbols.size(), ctx);
-  symbols.push_back(stepVal);
-  return std::make_pair(lb, lb + step * ((ub - 1) - lb).floorDiv(step));
-}
-
-/// Return the min/max expressions for `value` if it is an induction variable
-/// from scf.for or scf.parallel loop.
-/// if `loopFilter` is passed, the filter determines which loop to consider.
-/// Other induction variables are ignored.
-Optional<std::pair<AffineExpr, AffineExpr>> mlir::getSCFMinMaxExpr(
-    Value value, SmallVectorImpl<Value> &dims, SmallVectorImpl<Value> &symbols,
-    llvm::function_ref<bool(Operation *)> substituteOperation) {
-  if (auto forOp = scf::getForInductionVarOwner(value))
-    return getMinMaxLoopIndVar(forOp.lowerBound(), forOp.upperBound(),
-                               forOp.step(), dims, symbols);
-
-  if (auto parallelForOp = scf::getParallelForInductionVarOwner(value))
-    for (unsigned idx = 0, e = parallelForOp.getNumLoops(); idx < e; ++idx)
-      if (parallelForOp.getInductionVars()[idx] == value)
-        return getMinMaxLoopIndVar(parallelForOp.lowerBound()[idx],
-                                   parallelForOp.upperBound()[idx],
-                                   parallelForOp.step()[idx], dims, symbols);
-  return {};
-}

mlir/test/Dialect/Linalg/fold-affine-min-scf.mlir

-// RUN: mlir-opt %s -test-linalg-transform-patterns=test-affine-min-scf-canonicalization-patterns | FileCheck %s
-
-// CHECK-LABEL: scf_for
-func @scf_for(%A : memref<i64>, %step : index) {
-  %c0 = constant 0 : index
-  %c1 = constant 1 : index
-  %c2 = constant 2 : index
-  %c7 = constant 7 : index
-  %c4 = constant 4 : index
-  %c16 = constant 16 : index
-  %c1024 = constant 1024 : index
-
-  // CHECK:      %[[C2:.*]] = constant 2 : i64
-  //      CHECK: scf.for
-  // CHECK-NEXT:   memref.store %[[C2]], %{{.*}}[] : memref<i64>
-  scf.for %i = %c0 to %c4 step %c2 {
-    %1 = affine.min affine_map<(d0, d1)[] -> (2, d1 - d0)> (%i, %c4)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  //      CHECK: scf.for
-  // CHECK-NEXT:   memref.store %[[C2]], %{{.*}}[] : memref<i64>
-  scf.for %i = %c1 to %c7 step %c2 {
-    %1 = affine.min affine_map<(d0)[s0] -> (s0 - d0, 2)> (%i)[%c7]
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // This should not canonicalize because: 4 - %i may take the value 1 < 2.
-  //     CHECK:   scf.for
-  //     CHECK:     affine.min
-  //     CHECK:     index_cast
-  scf.for %i = %c1 to %c4 step %c2 {
-    %1 = affine.min affine_map<(d0)[s0] -> (2, s0 - d0)> (%i)[%c4]
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // This should not canonicalize because: 16 - %i may take the value 15 < 1024.
-  //     CHECK:   scf.for
-  //     CHECK:     affine.min
-  //     CHECK:     index_cast
-  scf.for %i = %c1 to %c16 step %c1024 {
-    %1 = affine.min affine_map<(d0) -> (1024, 16 - d0)> (%i)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // This example should simplify but affine_map is currently missing
-  // semi-affine canonicalizations: `((s0 * 42 - 1) floordiv s0) * s0`
-  // should evaluate to 41 * s0.
-  // Note that this may require positivity assumptions on `s0`.
-  // Revisit when support is added.
-  // CHECK: scf.for
-  // CHECK:   affine.min
-  // CHECK:   index_cast
-  %ub = affine.apply affine_map<(d0) -> (42 * d0)> (%step)
-  scf.for %i = %c0 to %ub step %step {
-    %1 = affine.min affine_map<(d0, d1, d2) -> (d0, d1 - d2)> (%step, %ub, %i)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // This example should simplify but affine_map is currently missing
-  // semi-affine canonicalizations.
-  // This example should simplify but affine_map is currently missing
-  // semi-affine canonicalizations: ` -(((s0 * s0 - 1) floordiv s0) * s0)`
-  // should evaluate to (s0 - 1) * s0.
-  // Note that this may require positivity assumptions on `s0`.
-  // Revisit when support is added.
-  // CHECK: scf.for
-  // CHECK:   affine.min
-  // CHECK:   index_cast
-  %ub2 = affine.apply affine_map<(d0)[s0] -> (s0 * d0)> (%step)[%step]
-  scf.for %i = %c0 to %ub2 step %step {
-    %1 = affine.min affine_map<(d0, d1, d2) -> (d0, d2 - d1)> (%step, %i, %ub2)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  return
-}
-
-// CHECK-LABEL: scf_parallel
-func @scf_parallel(%A : memref<i64>, %step : index) {
-  %c0 = constant 0 : index
-  %c1 = constant 1 : index
-  %c2 = constant 2 : index
-  %c7 = constant 7 : index
-  %c4 = constant 4 : index
-
-  // CHECK:   %[[C2:.*]] = constant 2 : i64
-  // CHECK: scf.parallel
-  // CHECK-NEXT:   memref.store %[[C2]], %{{.*}}[] : memref<i64>
-  scf.parallel (%i) = (%c0) to (%c4) step (%c2) {
-    %1 = affine.min affine_map<(d0, d1)[] -> (2, d1 - d0)> (%i, %c4)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // CHECK: scf.parallel
-  // CHECK-NEXT:   memref.store %[[C2]], %{{.*}}[] : memref<i64>
-  scf.parallel (%i) = (%c1) to (%c7) step (%c2) {
-    %1 = affine.min affine_map<(d0)[s0] -> (2, s0 - d0)> (%i)[%c7]
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // This example should simplify but affine_map is currently missing
-  // semi-affine canonicalizations.
-  // This affine map does not currently evaluate to (0, 0):
-  //   (d0)[s0] -> (s0 mod s0, (-((d0 floordiv s0) * s0) + s0 * 42) mod s0)
-  // TODO: Revisit when support is added.
-  // CHECK: scf.parallel
-  // CHECK:   affine.min
-  // CHECK:   index_cast
-  %ub = affine.apply affine_map<(d0) -> (42 * d0)> (%step)
-  scf.parallel (%i) = (%c0) to (%ub) step (%step) {
-    %1 = affine.min affine_map<(d0, d1, d2) -> (d0, d2 - d1)> (%step, %i, %ub)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  // This example should simplify but affine_map is currently missing
-  // semi-affine canonicalizations.
-  // This affine map does not currently evaluate to (0, 0):
-  //   (d0)[s0] -> (s0 mod s0, (-((d0 floordiv s0) * s0) + s0 * s0) mod s0)
-  // TODO: Revisit when support is added.
-  // CHECK: scf.parallel
-  // CHECK:   affine.min
-  // CHECK:   index_cast
-  %ub2 = affine.apply affine_map<(d0)[s0] -> (s0 * d0)> (%step)[%step]
-  scf.parallel (%i) = (%c0) to (%ub2) step (%step) {
-    %1 = affine.min affine_map<(d0, d1, d2) -> (d0, d2 - d1)> (%step, %i, %ub2)
-    %2 = index_cast %1: index to i64
-    memref.store %2, %A[]: memref<i64>
-  }
-
-  return
-}

mlir/test/Dialect/SCF/canonicalize-scf-affine-min.mlir

 // RUN: mlir-opt %s -canonicalize-scf-affine-min -split-input-file | FileCheck %s
 
-// Note: This is mostly a copy of test/Dialect/Linalg/fold-affine-min-scf.mlir
-
 // CHECK-LABEL: scf_for
 func @scf_for(%A : memref<i64>, %step : index) {
   %c0 = constant 0 : index
   %c1 = constant 1 : index
   %c2 = constant 2 : index
   %c7 = constant 7 : index
   %c4 = constant 4 : index
   %c16 = constant 16 : index

mlir/test/lib/Dialect/Linalg/TestConvVectorization.cpp

 //===- TestConvVectorization.cpp - Vectorization of Conv ops --------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 
 #include "mlir/Conversion/VectorToSCF/VectorToSCF.h"
 #include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/Linalg/Transforms/Hoisting.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
+#include "mlir/Dialect/SCF/Transforms.h"
 #include "mlir/Dialect/Vector/VectorTransforms.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Pass/PassManager.h"
 #include "mlir/Transforms/DialectConversion.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "mlir/Transforms/LoopUtils.h"
 #include "mlir/Transforms/Passes.h"
 
void TestConvVectorization::runOnOperation() {
 
   SmallVector<RewritePatternSet, 4> stage1Patterns;
   linalg::populateConvVectorizationPatterns(context, stage1Patterns, tileSizes);
   SmallVector<FrozenRewritePatternSet, 4> frozenStage1Patterns;
   llvm::move(stage1Patterns, std::back_inserter(frozenStage1Patterns));
 
   RewritePatternSet stage2Patterns =
       linalg::getLinalgTilingCanonicalizationPatterns(context);
-  stage2Patterns.add<linalg::AffineMinSCFCanonicalizationPattern>(context);
+  scf::populateSCFLoopBodyCanonicalizationPatterns(stage2Patterns);
 
   auto stage3Transforms = [](Operation *op) {
     PassManager pm(op->getContext());
     pm.addPass(createLoopInvariantCodeMotionPass());
     if (failed(pm.run(cast<ModuleOp>(op))))
       llvm_unreachable("Unexpected failure in cleanup pass pipeline.");
     op->walk([](FuncOp func) {
       promoteSingleIterationLoops(func);

mlir/test/lib/Dialect/Linalg/TestLinalgFusionTransforms.cpp

 //===- TestLinalgFusionTransforms.cpp - Test Linalg fusion patterns -------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements logic for testing Linalg fusion patterns.
 //
 //===----------------------------------------------------------------------===//
 
 #include "mlir/Dialect/Linalg/Analysis/DependenceAnalysis.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
+#include "mlir/Dialect/SCF/Transforms.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Pass/PassManager.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "mlir/Transforms/Passes.h"
 
 using namespace mlir;
 using namespace mlir::linalg;
 
struct TestLinalgGreedyFusion
   StringRef getArgument() const final { return "test-linalg-greedy-fusion"; }
   StringRef getDescription() const final {
     return "Test Linalg fusion by applying a greedy test transformation.";
   }
   void runOnFunction() override {
     MLIRContext *context = &getContext();
     RewritePatternSet patterns =
         linalg::getLinalgTilingCanonicalizationPatterns(context);
-    patterns.add<AffineMinSCFCanonicalizationPattern,
-                 ExtractSliceOfPadTensorSwapPattern>(context);
+    patterns.add<ExtractSliceOfPadTensorSwapPattern>(context);
+    scf::populateSCFLoopBodyCanonicalizationPatterns(patterns);
     FrozenRewritePatternSet frozenPatterns(std::move(patterns));
     do {
       (void)applyPatternsAndFoldGreedily(getFunction(), frozenPatterns);
       PassManager pm(context);
       pm.addPass(createLoopInvariantCodeMotionPass());
       pm.addPass(createCanonicalizerPass());
       pm.addPass(createCSEPass());
       LogicalResult res = pm.run(getFunction()->getParentOfType<ModuleOp>());

mlir/test/lib/Dialect/Linalg/TestLinalgTransforms.cpp

Option<bool> testVectorTransferForwardingPatterns{
       llvm::cl::desc(
           "Test a fused pass that forwards linalg.copy to vector.transfer"),
       llvm::cl::init(false)};
   Option<bool> testGenericToVectorPattern{
       *this, "test-linalg-to-vector-patterns",
       llvm::cl::desc("Test a set of patterns that rewrite a linalg contraction "
                      "in vector.contract form"),
       llvm::cl::init(false)};
-  Option<bool> testAffineMinSCFCanonicalizationPatterns{
-      *this, "test-affine-min-scf-canonicalization-patterns",
-      llvm::cl::desc("Test affine-min + scf canonicalization patterns."),
-      llvm::cl::init(false)};
   Option<bool> testTileAndPadPattern{
       *this, "test-tile-and-pad-pattern",
       llvm::cl::desc("Test tile and pad pattern"), llvm::cl::init(false)};
   Option<int> testHoistPadding{*this, "test-hoist-padding",
                                llvm::cl::desc("Test hoist padding"),
                                llvm::cl::init(0)};
   Option<bool> testTransformPadTensor{
       *this, "test-transform-pad-tensor",

 }
 
 static void applyExtractSliceOfPadTensorSwapPattern(FuncOp funcOp) {
   RewritePatternSet patterns(funcOp.getContext());
   patterns.add<ExtractSliceOfPadTensorSwapPattern>(funcOp.getContext());
   (void)applyPatternsAndFoldGreedily(funcOp, std::move(patterns));
 }
 
-static void applyAffineMinSCFCanonicalizationPatterns(FuncOp funcOp) {
-  RewritePatternSet foldPattern(funcOp.getContext());
-  foldPattern.add<AffineMinSCFCanonicalizationPattern>(funcOp.getContext());
-  FrozenRewritePatternSet frozenPatterns(std::move(foldPattern));
-
-  // Explicitly apply the pattern on affected ops to avoid more general folding
-  // on the rest of the IR.
-  SmallVector<Operation *, 4> minOps;
-  funcOp.walk([&](AffineMinOp minOp) { minOps.push_back(minOp); });
-  (void)applyOpPatternsAndFold(minOps, frozenPatterns, /*strict=*/false);
-}
-
 // For now, just assume it is the zero of type.
 // In the future, it should be the zero of type + op.
 static Value getNeutralOfLinalgOp(OpBuilder &b, OpOperand &op) {
   auto t = getElementTypeOrSelf(op.get());
   return b.create<ConstantOp>(op.getOwner()->getLoc(), t, b.getZeroAttr(t));
 }
 
 static void applyTileAndPadPattern(FuncOp funcOp, ArrayRef<int64_t> tileSizes) {
void TestLinalgTransforms::runOnFunction() {
   if (testGenericToVectorPattern)
     return applyLinalgToVectorPatterns(getFunction());
   if (testTransformPadTensor)
     return applyPadTensorToGenericPatterns(getFunction());
   if (testGeneralizePadTensor)
     return applyGeneralizePadTensorPatterns(getFunction());
   if (testSwapSubTensorPadTensor)
     return applyExtractSliceOfPadTensorSwapPattern(getFunction());
-  if (testAffineMinSCFCanonicalizationPatterns)
-    return applyAffineMinSCFCanonicalizationPatterns(getFunction());
   if (testTileAndPadPattern)
     return applyTileAndPadPattern(getFunction(), tileSizesForPadding);
   if (testHoistPadding) {
     getFunction().walk([&](linalg::PadTensorOp padTensorOp) {
       (void)linalg::hoistPaddingOnTensors(padTensorOp, testHoistPadding);
     });
   }
   if (testInterchangePattern.hasValue())