[mlir][Affine] Add helper functions to allow reordering affine.apply operands and decompose the ops into smaller components

[nicolasvasilache]

Summary

Care is taken to order operands from least hoistable to most hoistable and to process subexpressions in the same
order.

This allows exposing more oppportunities for licm, cse and strength reduction.

Such a step should typically be applied while we still have loops in the IR and just before lowering affine ops to arith.
This is because the affine.apply canonicalization currently tries to maximally compose chains of affine.apply operations
and could undo the effects of these decompositions.

Depends on: D145784

[nicolasvasilache]

add test and fix ordering assumptions to account for local variables.

[nicolasvasilache]

Drop debug spew

[nicolasvasilache]

Update tests

[nicolasvasilache]

Format

[nicolasvasilache]

Trim deps

[nicolasvasilache]

Rebase

[nicolasvasilache]

Add licm and cse to test

[ftynse]

mlir/include/mlir/Dialect/Affine/Transforms/Transforms.h

43–47

Looks unnecessary here.

mlir/lib/Dialect/Affine/Transforms/DecomposeAffineOps.cpp

44

Nit: I think to_vector no longer needs the number of stack elements.

[qcolombet]

mlir/test/lib/Dialect/Affine/TestDecomposeAffineOps.cpp

30

For the integration in IREE, I had to "promote" this test pass into an actual pass (see https://github.com/iree-org/iree-llvm-fork/blob/4ef84146ad72a6a5878697daf9658844cefb0a22/mlir/lib/Dialect/Affine/Transforms/DecomposeAffineOps.cpp#L183).
Could you front load this refactoring in this PR?

Unless you have a different plan for the IREE integration.

[qcolombet]

Food for thoughts:
Since the canonicalization is going to undo the decomposition, should we rewrite the expression (e.g., as part of the canonicalization) to the ordering we want?
The downside is we would still rely on the backend to do the hoisting of the loop invariant stuff.

Right now, what happens is:
Let's say we have:

affine.apply affine_map<()[s0, s1, s2] -> (s0 * 1024 + s1 * 32 + s2)>()[%loopVariant, %inv1, %inv2]

This decomposes in:

%inv1x32 = affine.apply affine_map<()[s0] -> (s0 * 32)>()[%inv1]
%inv2_ = affine.apply affine_map<()[s0] -> (s0)>()[%inv2]
%inv1x32_plus_inv2 = affine.apply affine_map<()[s0, s1] -> (s0 + s1)>()[%inv1x32, %inv2_]
%loopVariantx1024 = affine.apply affine_map<()[s0] -> (s0 * 1024)>()[%loopVariant]
%res = affine.apply affine_map<()[s0, s1] -> (s0 + s1)>()[%loopVariant, %inv1x32_plus_inv2]

Then we run licm and lower. Hooray, we did the hoisting and produce the code we wanted.

Now, let's say we don't lower right away and happen to run through canonicalization, the resulting expression will look like:

affine.apply affine_map<()[s0, s1, s2] -> (s0 + s1 * 32 + s2 * 1024)>()[%inv1, %inv2, %loopVariant]

Now if we lower this expression, the backend is still able to do licm and whatnot.

To summarize, I like the decomposition approach as it is more flexible, but it is easy to undo it (via canonicalization) so I wonder if we should just make the expression with the invariant symbols first the canonical representation.

[nicolasvasilache]

mlir/test/lib/Dialect/Affine/TestDecomposeAffineOps.cpp

30

Given the interaction with other passes and transforms I was thinking we want this functionality to just be called from a more comprehensive pass.

I wouldn't want to chase phase orderings between this, canonicalize, licm, lower affine and stuff related to ldmatrix.
I was thinking we'd have a new pass that puts the things properly together on the IREE side (or even upstream once we know exactly what we need) ?

[gchatelet]

This patch broke the buildkite bot
https://buildkite.com/llvm-project/upstream-bazel/builds/56515#0186dfcd-e044-4adf-992d-b51e688544dd

mlir/include/mlir/Dialect/Affine/Passes.h

 
 #ifndef MLIR_DIALECT_AFFINE_PASSES_H
 #define MLIR_DIALECT_AFFINE_PASSES_H
 
 #include "mlir/Pass/Pass.h"
 #include <limits>
 
 namespace mlir {
+
 namespace func {
 class FuncOp;
 } // namespace func
 
 class AffineForOp;
 
 /// Fusion mode to attempt. The default mode `Greedy` does both
 /// producer-consumer and sibling fusion.

 /// line if provided.
 std::unique_ptr<OperationPass<func::FuncOp>>
 createLoopUnrollAndJamPass(int unrollJamFactor = -1);
 
 /// Creates a pass to pipeline explicit movement of data across levels of the
 /// memory hierarchy.
 std::unique_ptr<OperationPass<func::FuncOp>> createPipelineDataTransferPass();
 
-/// Populate patterns that expand affine index operations into more fundamental
-/// operations (not necessarily restricted to Affine dialect).
-void populateAffineExpandIndexOpsPatterns(RewritePatternSet &patterns);
-
 /// Creates a pass to expand affine index operations into more fundamental
 /// operations (not necessarily restricted to Affine dialect).
 std::unique_ptr<Pass> createAffineExpandIndexOpsPass();
 
 //===----------------------------------------------------------------------===//
 // Registration
 //===----------------------------------------------------------------------===//
 
 /// Generate the code for registering passes.
 #define GEN_PASS_REGISTRATION
 #include "mlir/Dialect/Affine/Passes.h.inc"
 
 } // namespace mlir
 
 #endif // MLIR_DIALECT_AFFINE_PASSES_H

mlir/include/mlir/Dialect/Affine/Transforms/Transforms.h

+//===- Transforms.h - Transforms Entrypoints --------------------*- C++ -*-===//
+//
+// 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 header file defines a set of transforms specific for the AffineOps
+// dialect.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_DIALECT_AFFINE_TRANSFORMS_TRANSFORMS_H
+#define MLIR_DIALECT_AFFINE_TRANSFORMS_TRANSFORMS_H
+
+#include "mlir/Support/LogicalResult.h"
+
+namespace mlir {
+class RewritePatternSet;
+class RewriterBase;
+class AffineApplyOp;
+
+/// Populate patterns that expand affine index operations into more fundamental
+/// operations (not necessarily restricted to Affine dialect).
+void populateAffineExpandIndexOpsPatterns(RewritePatternSet &patterns);
+
+/// Helper function to rewrite `op`'s affine map and reorder its operands such
+/// that they are in increasing order of hoistability (i.e. the least hoistable)
+/// operands come first in the operand list.
+void reorderOperandsByHoistability(RewriterBase &rewriter, AffineApplyOp op);
+
+/// Split an "affine.apply" operation into smaller ops.
+/// This reassociates a large AffineApplyOp into an ordered list of smaller
+/// AffineApplyOps. This can be used right before lowering affine ops to arith
+/// to exhibit more opportunities for CSE and LICM.
+/// Return the sink AffineApplyOp on success or failure if `op` does not
+/// decompose into smaller AffineApplyOps.
+/// Note that this can be undone by canonicalization which tries to
+/// maximally compose chains of AffineApplyOps.
+FailureOr<AffineApplyOp> decompose(RewriterBase &rewriter, AffineApplyOp op);
+
+} // namespace mlir
+
+#endif // MLIR_DIALECT_AFFINE_TRANSFORMS_TRANSFORMS_H

mlir/lib/Dialect/Affine/Transforms/AffineExpandIndexOps.cpp

 //===- AffineExpandIndexOps.cpp - Affine expand index ops pass ------------===//
 //
 // 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 a pass to expand affine index ops into one or more more
 // fundamental operations.
 //===----------------------------------------------------------------------===//
 
 #include "mlir/Dialect/Affine/Passes.h"
 
 #include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Affine/Transforms/Transforms.h"
 #include "mlir/Dialect/Affine/Utils.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 
 namespace mlir {
 #define GEN_PASS_DEF_AFFINEEXPANDINDEXOPS
 #include "mlir/Dialect/Affine/Passes.h.inc"
 } // namespace mlir
 

mlir/lib/Dialect/Affine/Transforms/CMakeLists.txt

 add_mlir_dialect_library(MLIRAffineTransforms
   AffineDataCopyGeneration.cpp
   AffineExpandIndexOps.cpp
   AffineLoopInvariantCodeMotion.cpp
   AffineLoopNormalize.cpp
   AffineParallelize.cpp
   AffineScalarReplacement.cpp
+  DecomposeAffineOps.cpp
   LoopCoalescing.cpp
   LoopFusion.cpp
   LoopTiling.cpp
   LoopUnroll.cpp
   LoopUnrollAndJam.cpp
   PipelineDataTransfer.cpp
   SuperVectorize.cpp
   SimplifyAffineStructures.cpp

mlir/lib/Dialect/Affine/Transforms/DecomposeAffineOps.cpp

+//===- DecomposeAffineOps.cpp - Decompose affine ops into finer-grained ---===//
+//
+// 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 functionality to progressively decompose coarse-grained
+// affine ops into finer-grained ops.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Affine/Transforms/Transforms.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "llvm/Support/Debug.h"
+
+using namespace mlir;
+
+#define DEBUG_TYPE "decompose-affine-ops"
+#define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE "]: ")
+#define DBGSNL() (llvm::dbgs() << "\n")
+
+/// Count the number of loops surrounding `operand` such that operand could be
+/// hoisted above.
+/// Stop counting at the first loop over which the operand cannot be hoisted.
+static int64_t numEnclosingInvariantLoops(OpOperand &operand) {
+  int64_t count = 0;
+  Operation *currentOp = operand.getOwner();
+  while (auto loopOp = currentOp->getParentOfType<LoopLikeOpInterface>()) {
+    if (!loopOp.isDefinedOutsideOfLoop(operand.get()))
+      break;
+    currentOp = loopOp;
+    count++;
+  }
+  return count;
+}
+
+void mlir::reorderOperandsByHoistability(RewriterBase &rewriter,
+                                         AffineApplyOp op) {
+  SmallVector<int64_t> numInvariant = llvm::to_vector(
+      llvm::map_range(op->getOpOperands(), [&](OpOperand &operand) {
+        return numEnclosingInvariantLoops(operand);
+      }));
+
+  int64_t numOperands = op.getNumOperands();
+  SmallVector<int64_t> operandPositions =
+      llvm::to_vector(llvm::seq<int64_t>(0, numOperands));
+  std::sort(operandPositions.begin(), operandPositions.end(),
+            [&numInvariant](size_t i1, size_t i2) {
+              return numInvariant[i1] > numInvariant[i2];
+            });
+
+  SmallVector<AffineExpr> replacements(numOperands);
+  SmallVector<Value> operands(numOperands);
+  for (int64_t i = 0; i < numOperands; ++i) {
+    operands[i] = op.getOperand(operandPositions[i]);
+    replacements[operandPositions[i]] = getAffineSymbolExpr(i, op.getContext());
+  }
+
+  AffineMap map = op.getAffineMap();
+  ArrayRef<AffineExpr> repls{replacements};
+  map = map.replaceDimsAndSymbols(repls.take_front(map.getNumDims()),
+                                  repls.drop_front(map.getNumDims()),
+                                  /*numResultDims=*/0,
+                                  /*numResultSyms=*/numOperands);
+  map = AffineMap::get(0, numOperands,
+                       simplifyAffineExpr(map.getResult(0), 0, numOperands),
+                       op->getContext());
+  canonicalizeMapAndOperands(&map, &operands);
+
+  rewriter.startRootUpdate(op);
+  op.setMap(map);
+  op->setOperands(operands);
+  rewriter.finalizeRootUpdate(op);
+}
+
+/// Build an affine.apply that is a subexpression `expr` of `originalOp`s affine
+/// map and with the same operands.
+/// Canonicalize the map and operands to deduplicate and drop dead operands
+/// before returning but do not perform maximal composition of AffineApplyOp
+/// which would defeat the purpose.
+static AffineApplyOp createSubApply(RewriterBase &rewriter,
+                                    AffineApplyOp originalOp, AffineExpr expr) {
+  MLIRContext *ctx = originalOp->getContext();
+  AffineMap m = originalOp.getAffineMap();
+  auto rhsMap = AffineMap::get(m.getNumDims(), m.getNumSymbols(), expr, ctx);
+  SmallVector<Value> rhsOperands = originalOp->getOperands();
+  canonicalizeMapAndOperands(&rhsMap, &rhsOperands);
+  return rewriter.create<AffineApplyOp>(originalOp.getLoc(), rhsMap,
+                                        rhsOperands);
+}
+
+FailureOr<AffineApplyOp> mlir::decompose(RewriterBase &rewriter,
+                                         AffineApplyOp op) {
+  // 1. Preconditions: only handle dimensionless AffineApplyOp maps with a
+  // top-level binary expression that we can reassociate (i.e. add or mul).
+  AffineMap m = op.getAffineMap();
+  if (m.getNumDims() > 0)
+    return rewriter.notifyMatchFailure(op, "expected no dims");
+
+  AffineExpr remainingExp = m.getResult(0);
+  auto binExpr = remainingExp.dyn_cast<AffineBinaryOpExpr>();
+  if (!binExpr)
+    return rewriter.notifyMatchFailure(op, "terminal affine.apply");
+
+  if (!binExpr.getLHS().isa<AffineBinaryOpExpr>() &&
+      !binExpr.getRHS().isa<AffineBinaryOpExpr>())
+    return rewriter.notifyMatchFailure(op, "terminal affine.apply");
+
+  bool supportedKind = ((binExpr.getKind() == AffineExprKind::Add) ||
+                        (binExpr.getKind() == AffineExprKind::Mul));
+  if (!supportedKind)
+    return rewriter.notifyMatchFailure(
+        op, "only add or mul binary expr can be reassociated");
+
+  LLVM_DEBUG(DBGS() << "Start decomposeIntoFinerGrainedOps: " << op << "\n");
+
+  // 2. Iteratively extract the RHS subexpressions while the top-level binary
+  // expr kind remains the same.
+  MLIRContext *ctx = op->getContext();
+  SmallVector<AffineExpr> subExpressions;
+  while (true) {
+    auto currentBinExpr = remainingExp.dyn_cast<AffineBinaryOpExpr>();
+    if (!currentBinExpr || currentBinExpr.getKind() != binExpr.getKind()) {
+      subExpressions.push_back(remainingExp);
+      LLVM_DEBUG(DBGS() << "--terminal: " << subExpressions.back() << "\n");
+      break;
+    }
+    subExpressions.push_back(currentBinExpr.getRHS());
+    LLVM_DEBUG(DBGS() << "--subExpr: " << subExpressions.back() << "\n");
+    remainingExp = currentBinExpr.getLHS();
+  }
+
+  // 3. Reorder subExpressions by the min symbol they are a function of.
+  // This also takes care of properly reordering local variables.
+  // This however won't be able to split expression that cannot be reassociated
+  // such as ones that involve divs and multiple symbols.
+  auto getMaxSymbol = [&](AffineExpr e) -> int64_t {
+    for (int64_t i = m.getNumSymbols(); i >= 0; --i)
+      if (e.isFunctionOfSymbol(i))
+        return i;
+    return -1;
+  };
+  std::sort(subExpressions.begin(), subExpressions.end(),
+            [&](AffineExpr e1, AffineExpr e2) {
+              return getMaxSymbol(e1) < getMaxSymbol(e2);
+            });
+  LLVM_DEBUG(
+      llvm::interleaveComma(subExpressions, DBGS() << "--sorted subexprs: ");
+      llvm::dbgs() << "\n");
+
+  // 4. Merge sorted subExpressions iteratively, thus achieving reassociation.
+  auto s0 = getAffineSymbolExpr(0, ctx);
+  auto s1 = getAffineSymbolExpr(1, ctx);
+  AffineMap binMap = AffineMap::get(
+      /*dimCount=*/0, /*symbolCount=*/2,
+      getAffineBinaryOpExpr(binExpr.getKind(), s0, s1), ctx);
+
+  auto current = createSubApply(rewriter, op, subExpressions[0]);
+  for (int64_t i = 1, e = subExpressions.size(); i < e; ++i) {
+    Value tmp = createSubApply(rewriter, op, subExpressions[i]);
+    current = rewriter.create<AffineApplyOp>(op.getLoc(), binMap,
+                                             ValueRange{current, tmp});
+    LLVM_DEBUG(DBGS() << "--reassociate into: " << current << "\n");
+  }
+
+  // 5. Replace original op.
+  rewriter.replaceOp(op, current.getResult());
+  return current;
+}

mlir/test/Dialect/Affine/decompose-affine-ops.mlir

+// RUN: mlir-opt %s -allow-unregistered-dialect -test-decompose-affine-ops -split-input-file -loop-invariant-code-motion -cse | FileCheck %s
+
+// CHECK-DAG: #[[$c42:.*]] = affine_map<() -> (42)>
+// CHECK-DAG: #[[$div32mod4:.*]] = affine_map<()[s0] -> ((s0 floordiv 32) mod 4)>
+// CHECK-DAG: #[[$add:.*]] = affine_map<()[s0, s1] -> (s0 + s1)>
+
+// CHECK-LABEL:  func.func @simple_test_1
+//  CHECK-SAME:  %[[I0:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[I1:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[I2:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[LB:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[UB:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[STEP:[0-9a-zA-Z]+]]: index
+func.func @simple_test_1(%0: index, %1: index, %2: index, %lb: index, %ub: index, %step: index) {
+  // CHECK: %[[c42:.*]] = affine.apply #[[$c42]]()
+  // CHECK: %[[R1:.*]] = affine.apply #[[$div32mod4]]()[%[[I1]]]
+  // CHECK: %[[a:.*]] = affine.apply #[[$add]]()[%[[c42]], %[[R1]]]
+  %a = affine.apply affine_map<(d0) -> ((d0 floordiv 32) mod 4 + 42)>(%1)
+
+  // CHECK:     "some_side_effecting_consumer"(%[[a]]) : (index) -> ()
+  "some_side_effecting_consumer"(%a) : (index) -> ()
+  return
+}
+
+// -----
+
+// CHECK-DAG: #[[$c42:.*]] = affine_map<() -> (42)>
+// CHECK-DAG: #[[$id:.*]] = affine_map<()[s0] -> (s0)>
+// CHECK-DAG: #[[$add:.*]] = affine_map<()[s0, s1] -> (s0 + s1)>
+// CHECK-DAG: #[[$div32div4timesm4:.*]] = affine_map<()[s0] -> (((s0 floordiv 32) floordiv 4) * -4)>
+// CHECK-DAG: #[[$div32:.*]] = affine_map<()[s0] -> (s0 floordiv 32)>
+
+// CHECK-LABEL:  func.func @simple_test_2
+//  CHECK-SAME:  %[[I0:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[I1:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[I2:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[LB:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[UB:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[STEP:[0-9a-zA-Z]+]]: index
+func.func @simple_test_2(%0: index, %1: index, %2: index, %lb: index, %ub: index, %step: index) {
+  // CHECK: %[[c42:.*]] = affine.apply #[[$c42]]()
+  // CHECK: scf.for %[[i:.*]] =
+  scf.for %i = %lb to %ub step %step {
+    // CHECK:   %[[R1:.*]] = affine.apply #[[$id]]()[%[[i]]]
+    // CHECK:   %[[R2:.*]] = affine.apply #[[$add]]()[%[[c42]], %[[R1]]]
+    // CHECK:   scf.for %[[j:.*]] =
+    scf.for %j = %lb to %ub step %step {
+      // CHECK:     %[[R3:.*]] = affine.apply #[[$div32div4timesm4]]()[%[[j]]]
+      // CHECK:     %[[R4:.*]] = affine.apply #[[$add]]()[%[[R2]], %[[R3]]]
+      // CHECK:     %[[R5:.*]] = affine.apply #[[$div32]]()[%[[j]]]
+      // CHECK:      %[[a:.*]] = affine.apply #[[$add]]()[%[[R4]], %[[R5]]]
+      %a = affine.apply affine_map<(d0)[s0] -> ((d0 floordiv 32) mod 4 + s0 + 42)>(%j)[%i]
+
+      // CHECK:     "some_side_effecting_consumer"(%[[a]]) : (index) -> ()
+      "some_side_effecting_consumer"(%a) : (index) -> ()
+    }
+  }
+  return
+}
+
+// -----
+
+// CHECK-DAG: #[[$div4:.*]] = affine_map<()[s0] -> (s0 floordiv 4)>
+// CHECK-DAG: #[[$times32:.*]] = affine_map<()[s0] -> (s0 * 32)>
+// CHECK-DAG: #[[$times16:.*]] = affine_map<()[s0] -> (s0 * 16)>
+// CHECK-DAG: #[[$add:.*]] = affine_map<()[s0, s1] -> (s0 + s1)>
+// CHECK-DAG: #[[$div4timesm32:.*]] = affine_map<()[s0] -> ((s0 floordiv 4) * -32)>
+// CHECK-DAG: #[[$times8:.*]] = affine_map<()[s0] -> (s0 * 8)>
+// CHECK-DAG: #[[$id:.*]] = affine_map<()[s0] -> (s0)>
+// CHECK-DAG: #[[$div32div4timesm4:.*]] = affine_map<()[s0] -> (((s0 floordiv 32) floordiv 4) * -4)>
+// CHECK-DAG: #[[$div32:.*]] = affine_map<()[s0] -> (s0 floordiv 32)>
+
+// CHECK-LABEL:  func.func @larger_test
+//  CHECK-SAME:  %[[I0:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[I1:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[I2:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[LB:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[UB:[0-9a-zA-Z]+]]: index,
+//  CHECK-SAME:  %[[STEP:[0-9a-zA-Z]+]]: index
+func.func @larger_test(%0: index, %1: index, %2: index, %lb: index, %ub: index, %step: index) {
+    %c2 = arith.constant 2 : index
+    %c6 = arith.constant 6 : index
+
+    //      CHECK: %[[R0:.*]] = affine.apply #[[$div4]]()[%[[I0]]]
+    // CHECK-NEXT: %[[R1:.*]] = affine.apply #[[$times16]]()[%[[I1]]]
+    // CHECK-NEXT: %[[R2:.*]] = affine.apply #[[$add]]()[%[[R0]], %[[R1]]]
+    // CHECK-NEXT: %[[R3:.*]] = affine.apply #[[$times32]]()[%[[I2]]]
+
+    // I1 * 16 + I2 * 32 + I0 floordiv 4
+    // CHECK-NEXT: %[[b:.*]] = affine.apply #[[$add]]()[%[[R2]], %[[R3]]]
+
+    // (I0 floordiv 4) * 32
+    // CHECK-NEXT: %[[R5:.*]] = affine.apply #[[$div4timesm32]]()[%[[I0]]]
+    // 8 * I0
+    // CHECK-NEXT: %[[R6:.*]] = affine.apply #[[$times8]]()[%[[I0]]]
+    // 8 * I0 + (I0 floordiv 4) * 32
+    // CHECK-NEXT: %[[c:.*]] = affine.apply #[[$add]]()[%[[R5]], %[[R6]]]
+
+    // CHECK-NEXT: scf.for %[[i:.*]] =
+    scf.for %i = %lb to %ub step %step {
+      // remainder from %a not hoisted above %i.
+      // CHECK-NEXT: %[[R8:.*]] = affine.apply #[[$times32]]()[%[[i]]]
+      // CHECK-NEXT: %[[a:.*]] = affine.apply #[[$add]]()[%[[b]], %[[R8]]]
+
+      // CHECK-NEXT: scf.for %[[j:.*]] =
+      scf.for %j = %lb to %ub step %step {
+        // Gets hoisted partially to i and rest outermost.
+        // The hoisted part is %b.
+        %a = affine.apply affine_map<()[s0, s1, s2, s3] -> (s1 * 16 + s2 * 32 + s3 * 32 + s0 floordiv 4)>()[%0, %1, %2, %i]
+
+        // Gets completely hoisted 
+        %b = affine.apply affine_map<()[s0, s1, s2] -> (s1 * 16 + s2 * 32 + s0 floordiv 4)>()[%0, %1, %2]
+
+        // Gets completely hoisted 
+        %c = affine.apply affine_map<()[s0] -> (s0 * 8 - (s0 floordiv 4) * 32)>()[%0]
+ 
+        // 32 * %j + %c remains here, the rest is hoisted.
+        // CHECK-DAG: %[[R10:.*]] = affine.apply #[[$times32]]()[%[[j]]]
+        // CHECK-DAG: %[[d:.*]] = affine.apply #[[$add]]()[%[[c]], %[[R10]]]
+        %d = affine.apply affine_map<()[s0, s1] -> (s0 * 8 + s1 * 32 - (s0 floordiv 4) * 32)>()[%0, %j]
+
+        // CHECK-DAG: %[[idj:.*]] = affine.apply #[[$id]]()[%[[j]]]
+        // CHECK-NEXT: scf.for %[[k:.*]] =
+        scf.for %k = %lb to %ub step %step {
+          // CHECK-NEXT: %[[idk:.*]] = affine.apply #[[$id]]()[%[[k]]]
+          // CHECK-NEXT: %[[e:.*]] = affine.apply #[[$add]]()[%[[c]], %[[idk]]]
+          %e = affine.apply affine_map<()[s0, s1] -> (s0 + s1 * 8 - (s1 floordiv 4) * 32)>()[%k, %0]
+
+          // CHECK-NEXT: %[[R15:.*]] = affine.apply #[[$div32div4timesm4]]()[%[[k]]]
+          // CHECK-NEXT: %[[R16:.*]] = affine.apply #[[$add]]()[%[[idj]], %[[R15]]]
+          // CHECK-NEXT: %[[R17:.*]] = affine.apply #[[$div32]]()[%[[k]]]
+          // CHECK-NEXT: %[[f:.*]] = affine.apply #[[$add]]()[%[[R16]], %[[R17]]]
+          %f = affine.apply affine_map<(d0)[s0] -> ((d0 floordiv 32) mod 4 + s0)>(%k)[%j]
+
+          // CHECK-NEXT: %[[g:.*]] = affine.apply #[[$add]]()[%[[b]], %[[idk]]]
+          %g = affine.apply affine_map<()[s0, s1, s2, s3] -> (s0 + s2 * 16 + s3 * 32 + s1 floordiv 4)>()[%k, %0, %1, %2]
+          
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[a]]) : (index) -> ()
+          "some_side_effecting_consumer"(%a) : (index) -> ()
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[b]]) : (index) -> ()
+          "some_side_effecting_consumer"(%b) : (index) -> ()
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[c]]) : (index) -> ()
+          "some_side_effecting_consumer"(%c) : (index) -> ()
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[d]]) : (index) -> ()
+          "some_side_effecting_consumer"(%d) : (index) -> ()
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[e]]) : (index) -> ()
+          "some_side_effecting_consumer"(%e) : (index) -> ()
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[f]]) : (index) -> ()
+          "some_side_effecting_consumer"(%f) : (index) -> ()
+          // CHECK-NEXT: "some_side_effecting_consumer"(%[[g]]) : (index) -> ()
+          "some_side_effecting_consumer"(%g) : (index) -> ()
+        }
+    }
+  }   
+  return
+}

mlir/test/lib/Dialect/Affine/CMakeLists.txt

 # Exclude tests from libMLIR.so
 add_mlir_library(MLIRAffineTransformsTestPasses
   TestAffineDataCopy.cpp
   TestAffineLoopUnswitching.cpp
   TestAffineLoopParametricTiling.cpp
+  TestDecomposeAffineOps.cpp
   TestLoopFusion.cpp
   TestLoopMapping.cpp
   TestLoopPermutation.cpp
   TestVectorizationUtils.cpp
 
   EXCLUDE_FROM_LIBMLIR
 
   ADDITIONAL_HEADER_DIRS

mlir/test/lib/Dialect/Affine/TestDecomposeAffineOps.cpp

+//===- TestDecomposeAffineOps.cpp - Test affine ops decomposition utility -===//
+//
+// 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 a pass to test affine data copy utility functions and
+// options.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
+#include "mlir/Dialect/Affine/Transforms/Transforms.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Pass/Pass.h"
+#include "mlir/Transforms/Passes.h"
+
+#define PASS_NAME "test-decompose-affine-ops"
+
+using namespace mlir;
+
+namespace {
+
+struct TestDecomposeAffineOps
+    : public PassWrapper<TestDecomposeAffineOps, OperationPass<func::FuncOp>> {
+  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestDecomposeAffineOps)
+
+  StringRef getArgument() const final { return PASS_NAME; }
+  StringRef getDescription() const final {
+    return "Tests affine ops decomposition utility functions.";
+  }
+  TestDecomposeAffineOps() = default;
+  TestDecomposeAffineOps(const TestDecomposeAffineOps &pass)
+      : PassWrapper(pass){};
+
+  void runOnOperation() override;
+};
+
+} // namespace
+
+void TestDecomposeAffineOps::runOnOperation() {
+  IRRewriter rewriter(&getContext());
+  this->getOperation().walk([&](AffineApplyOp op) {
+    rewriter.setInsertionPoint(op);
+    reorderOperandsByHoistability(rewriter, op);
+    (void)decompose(rewriter, op);
+  });
+}
+
+namespace mlir {
+void registerTestDecomposeAffineOpPass() {
+  PassRegistration<TestDecomposeAffineOps>();
+}
+} // namespace mlir

mlir/tools/mlir-opt/mlir-opt.cpp

 void registerPrintSpirvAvailabilityPass();
 void registerLoopLikeInterfaceTestPasses();
 void registerShapeFunctionTestPasses();
 void registerSideEffectTestPasses();
 void registerSliceAnalysisTestPass();
 void registerSymbolTestPasses();
 void registerRegionTestPasses();
 void registerTestAffineDataCopyPass();
+void registerTestDecomposeAffineOpPass();
 void registerTestAffineLoopUnswitchingPass();
 void registerTestAllReduceLoweringPass();
 void registerTestFunc();
 void registerTestGpuMemoryPromotionPass();
 void registerTestLoopPermutationPass();
 void registerTestMatchers();
 void registerTestOperationEqualPass();
 void registerTestPrintDefUsePass();
void registerTestPasses() {
   registerPrintSpirvAvailabilityPass();
   registerLoopLikeInterfaceTestPasses();
   registerShapeFunctionTestPasses();
   registerSideEffectTestPasses();
   registerSliceAnalysisTestPass();
   registerSymbolTestPasses();
   registerRegionTestPasses();
   registerTestAffineDataCopyPass();
+  registerTestDecomposeAffineOpPass();
   registerTestAffineLoopUnswitchingPass();
   registerTestAllReduceLoweringPass();
   registerTestFunc();
   registerTestGpuMemoryPromotionPass();
   registerTestLoopPermutationPass();
   registerTestMatchers();
   registerTestOperationEqualPass();
   registerTestPrintDefUsePass();