[mlir][linalg] Fix padding size calculation for Conv2d ops.

[pzread]

Summary

This patch fixed the padding size calculation for Conv2d ops when the stride > 1. It contains the changes below:

• Use addBound to add constraint for AffineApplyOp in getUpperBoundForIndex. So the result value can be mapped and retrieved later.

• Fixed the bound from AffineMinOp by adding as a closed bound. Originally the bound was added as an open upper bound, which results in the incorrect bounds when we multiply the values. For example:

%0 = affine.min affine_map<()[s0] -> (4, -s0 + 11)>()[iv0]
%1 = affine.apply affine_map<()[s0] -> (s0 * 2)>()[%0]

If we add the affine.min as an open bound, addBound will internally transform it into the close bound "%0 <= 3". The following sliceBounds will derive the bound of %1 as "%1 <= 6" and return the open bound "%1 < 7", while the correct bound should be "%1 <= 8".

• In addition to addBound, I also changed sliceBounds to support returning closed upper bound, since for the size computation, we usually care about the closed bounds.

• Change the getUpperBoundForIndex to favor constant bounds when required. The sliceBounds will return a tighter but non-constant bounds, which can't be used for padding. The constantRequired option requires getUpperBoundForIndex to get the constant bounds when possible.

[pzread]

Improve comments.

[hanchung]

Nice fix!

mlir/lib/Dialect/Linalg/Utils/Utils.cpp

259

[optional] we don't need the comment because the code (AffineMap::getConstantMap) describes it.

[pzread]

Remove unnecessary comments.

[Groverkss]

mlir/include/mlir/Dialect/Affine/Analysis/AffineStructures.h

171–172

Can you update the docs about what isClosedBound does? The requirement that isClosedBound needs to be true for BoundType::EQ is not obvious.

mlir/lib/Dialect/Linalg/Utils/Utils.cpp

256

[pzread]

Improve docs.

[pzread]

Replace the enum.

mlir/include/mlir/Dialect/Affine/Analysis/AffineStructures.h

public:
 
   /// Adds a bound for the identifier at the specified position with constraints
   /// being drawn from the specified bound map. In case of an EQ bound, the
   /// bound map is expected to have exactly one result. In case of a LB/UB, the
   /// bound map may have more than one result, for each of which an inequality
   /// is added.
   /// Note: The dimensions/symbols of this FlatAffineConstraints must match the
   /// dimensions/symbols of the affine map.
+  LogicalResult addBound(BoundType type, unsigned pos, AffineMap boundMap,
+                         bool isClosedBound);
+
+  /// Adds a bound for the identifier at the specified position with constraints
+  /// being drawn from the specified bound map. In case of an EQ bound, the
+  /// bound map is expected to have exactly one result. In case of a LB/UB, the
+  /// bound map may have more than one result, for each of which an inequality
+  /// is added.
+  /// Note: The dimensions/symbols of this FlatAffineConstraints must match the
+  /// dimensions/symbols of the affine map. By default the lower bound is closed
+  /// and the upper bound is open.
   LogicalResult addBound(BoundType type, unsigned pos, AffineMap boundMap);
 
   /// Adds a bound for the identifier at the specified position with constraints
   /// being drawn from the specified bound map and operands. In case of an
   /// EQ bound, the  bound map is expected to have exactly one result. In case
   /// of a LB/UB, the bound map may have more than one result, for each of which
   /// an inequality is added.
   LogicalResult addBound(BoundType type, unsigned pos, AffineMap boundMap,
public:
   IntegerSet getAsIntegerSet(MLIRContext *context) const;
 
   /// Computes the lower and upper bounds of the first `num` dimensional
   /// identifiers (starting at `offset`) as an affine map of the remaining
   /// identifiers (dimensional and symbolic). This method is able to detect
   /// identifiers as floordiv's and mod's of affine expressions of other
   /// identifiers with respect to (positive) constants. Sets bound map to a
   /// null AffineMap if such a bound can't be found (or yet unimplemented).
+  ///
+  /// By default the returned lower bounds are closed and upper bounds are open.
+  /// This can be changed by getClosedUB.
   void getSliceBounds(unsigned offset, unsigned num, MLIRContext *context,
                       SmallVectorImpl<AffineMap> *lbMaps,
-                      SmallVectorImpl<AffineMap> *ubMaps);
+                      SmallVectorImpl<AffineMap> *ubMaps,
+                      bool getClosedUB = false);
 
   /// Composes an affine map whose dimensions and symbols match one to one with
   /// the dimensions and symbols of this FlatAffineConstraints. The results of
   /// the map `other` are added as the leading dimensions of this constraint
   /// system. Returns failure if `other` is a semi-affine map.
   LogicalResult composeMatchingMap(AffineMap other);
 
   /// Gets the lower and upper bound of the `offset` + `pos`th identifier

mlir/include/mlir/Dialect/Linalg/Utils/Utils.h

 SmallVector<Value, 4> getDynOperands(Location loc, Value val, OpBuilder &b);
 
 /// Computes an upper bound for the result `value` of an index computation.
 /// Translates AffineMinOps and AffineApplyOps along the use-def chains of the
 /// index computation to affine constraints and projects out intermediate
 /// values. The method sets `boundMap` to an affine map that given
 /// `boundOperands` evaluates to an upper bound for the index computation.
 ///
+/// If constantRequired is true, only returns the constant bounds (potentially
+/// over-approximating) and fails when not possible.
+///
 /// Example:
 /// ```
 /// %dim0 = dim %tensor, %c0
 /// %dim1 = dim %tensor, %c1
 /// %0 = affine.min affine.map<(d0) -> (40, d0)> (%dim0)
 /// %1 = affine.apply affine.map<(d0, d1) -> (d0 + d1)> (%0, %dim1)
 /// ```
 /// getUpperBoundForIndex(%1, boundMap, boundOperands)
 /// set the output parameters to:
 /// - boundMap = affine.map<(d0) -> (d0 + 40)>
 /// - boundOperands = [%dim1]
 void getUpperBoundForIndex(Value value, AffineMap &boundMap,
-                           SmallVectorImpl<Value> &boundOperands);
+                           SmallVectorImpl<Value> &boundOperands,
+                           bool constantRequired = false);
 
 /// Returns a constant upper bound for the result `value` of an index
 /// computation. Calls `getUpperBoundForIndex` and returns a constant upper
 /// bound if the result of `boundMap` is a constant expression and failure
 /// otherwise.
 ///
 /// Example:
 /// ```

mlir/lib/Dialect/Affine/Analysis/AffineStructures.cpp

 
 /// Computes the lower and upper bounds of the first 'num' dimensional
 /// identifiers (starting at 'offset') as affine maps of the remaining
 /// identifiers (dimensional and symbolic identifiers). Local identifiers are
 /// themselves explicitly computed as affine functions of other identifiers in
 /// this process if needed.
 void FlatAffineValueConstraints::getSliceBounds(
     unsigned offset, unsigned num, MLIRContext *context,
-    SmallVectorImpl<AffineMap> *lbMaps, SmallVectorImpl<AffineMap> *ubMaps) {
+    SmallVectorImpl<AffineMap> *lbMaps, SmallVectorImpl<AffineMap> *ubMaps,
+    bool getClosedUB) {
   assert(num < getNumDimIds() && "invalid range");
 
   // Basic simplification.
   normalizeConstraintsByGCD();
 
   LLVM_DEBUG(llvm::dbgs() << "getSliceBounds for first " << num
                           << " identifiers\n");
   LLVM_DEBUG(dump());
for (unsigned pos = 0; pos < getNumIds(); pos++) {
       memo[pos] = expr;
       changed = true;
     }
     // This loop is guaranteed to reach a fixed point - since once an
     // identifier's explicit form is computed (in memo[pos]), it's not updated
     // again.
   } while (changed);
 
+  int64_t ubAdjustment = getClosedUB ? 0 : 1;
+
   // Set the lower and upper bound maps for all the identifiers that were
   // computed as affine expressions of the rest as the "detected expr" and
   // "detected expr + 1" respectively; set the undetected ones to null.
   Optional<FlatAffineValueConstraints> tmpClone;
   for (unsigned pos = 0; pos < num; pos++) {
     unsigned numMapDims = getNumDimIds() - num;
     unsigned numMapSymbols = getNumSymbolIds();
     AffineExpr expr = memo[pos + offset];
     if (expr)
       expr = simplifyAffineExpr(expr, numMapDims, numMapSymbols);
 
     AffineMap &lbMap = (*lbMaps)[pos];
     AffineMap &ubMap = (*ubMaps)[pos];
 
     if (expr) {
       lbMap = AffineMap::get(numMapDims, numMapSymbols, expr);
-      ubMap = AffineMap::get(numMapDims, numMapSymbols, expr + 1);
+      ubMap = AffineMap::get(numMapDims, numMapSymbols, expr + ubAdjustment);
     } else {
       // TODO: Whenever there are local identifiers in the dependence
       // constraints, we'll conservatively over-approximate, since we don't
       // always explicitly compute them above (in the while loop).
       if (getNumLocalIds() == 0) {
         // Work on a copy so that we don't update this constraint system.
         if (!tmpClone) {
           tmpClone.emplace(FlatAffineValueConstraints(*this));
if (expr) {
               getAffineConstantExpr(lbConst.getValue(), context));
         }
       }
       if (!ubMap || ubMap.getNumResults() > 1) {
         LLVM_DEBUG(llvm::dbgs()
                    << "WARNING: Potentially over-approximating slice ub\n");
         auto ubConst = getConstantBound(BoundType::UB, pos + offset);
         if (ubConst.hasValue()) {
-          (ubMap) = AffineMap::get(
-              numMapDims, numMapSymbols,
-              getAffineConstantExpr(ubConst.getValue() + 1, context));
+          ubMap =
+              AffineMap::get(numMapDims, numMapSymbols,
+                             getAffineConstantExpr(
+                                 ubConst.getValue() + ubAdjustment, context));
         }
       }
     }
     LLVM_DEBUG(llvm::dbgs()
                << "lb map for pos = " << Twine(pos + offset) << ", expr: ");
     LLVM_DEBUG(lbMap.dump(););
     LLVM_DEBUG(llvm::dbgs()
                << "ub map for pos = " << Twine(pos + offset) << ", expr: ");
if (localCst.getNumLocalIds() > 0) {
     // this constraint set.
     append(localCst);
   }
 
   return success();
 }
 
 LogicalResult FlatAffineValueConstraints::addBound(BoundType type, unsigned pos,
-                                                   AffineMap boundMap) {
+                                                   AffineMap boundMap,
+                                                   bool isClosedBound) {
   assert(boundMap.getNumDims() == getNumDimIds() && "dim mismatch");
   assert(boundMap.getNumSymbols() == getNumSymbolIds() && "symbol mismatch");
   assert(pos < getNumDimAndSymbolIds() && "invalid position");
+  assert((type != BoundType::EQ || isClosedBound) &&
+         "EQ bound must be closed.");
 
   // Equality follows the logic of lower bound except that we add an equality
   // instead of an inequality.
   assert((type != BoundType::EQ || boundMap.getNumResults() == 1) &&
          "single result expected");
   bool lower = type == BoundType::LB || type == BoundType::EQ;
 
   std::vector<SmallVector<int64_t, 8>> flatExprs;
if (ineq[pos] != 0)
       continue;
     ineq[pos] = lower ? 1 : -1;
     // Local columns of `ineq` are at the beginning.
     unsigned j = getNumDimIds() + getNumSymbolIds();
     unsigned end = flatExpr.size() - 1;
     for (unsigned i = boundMap.getNumInputs(); i < end; i++, j++) {
       ineq[j] = lower ? -flatExpr[i] : flatExpr[i];
     }
+    // Make the bound closed in if flatExpr is open. The inequality is always
+    // created in the upper bound form, so the adjustment is -1.
+    int64_t boundAdjustment = (isClosedBound || type == BoundType::EQ) ? 0 : -1;
     // Constant term.
-    ineq[getNumCols() - 1] =
-        lower ? -flatExpr[flatExpr.size() - 1]
-              // Upper bound in flattenedExpr is an exclusive one.
-              : flatExpr[flatExpr.size() - 1] - 1;
+    ineq[getNumCols() - 1] = (lower ? -flatExpr[flatExpr.size() - 1]
+                                    : flatExpr[flatExpr.size() - 1]) +
+                             boundAdjustment;
     type == BoundType::EQ ? addEquality(ineq) : addInequality(ineq);
   }
 
   return success();
 }
 
+LogicalResult FlatAffineValueConstraints::addBound(BoundType type, unsigned pos,
+                                                   AffineMap boundMap) {
+  return addBound(type, pos, boundMap, /*isClosedBound=*/type != BoundType::UB);
+}
+
 AffineMap
 FlatAffineValueConstraints::computeAlignedMap(AffineMap map,
                                               ValueRange operands) const {
   assert(map.getNumInputs() == operands.size() && "number of inputs mismatch");
 
   SmallVector<Value> dims, syms;
 #ifndef NDEBUG
   SmallVector<Value> newSyms;

mlir/lib/Dialect/Linalg/Utils/Utils.cpp

SmallVector<Value, 4> getDynOperands(Location loc, Value val, OpBuilder &b) {
   for (const auto &dim : llvm::enumerate(shapedType.getShape())) {
     if (dim.value() == ShapedType::kDynamicSize)
       dynOperands.push_back(createOrFoldDimOp(b, loc, val, dim.index()));
   }
   return dynOperands;
 }
 
 void getUpperBoundForIndex(Value value, AffineMap &boundMap,
-                           SmallVectorImpl<Value> &boundOperands) {
+                           SmallVectorImpl<Value> &boundOperands,
+                           bool constantRequired) {
   // Initialize `boundMap` and `boundOperands` to the identity returning
   // `value`. This combination is the default result of the method if no
   // simplification is possible.
   assert(value.getType().isIndex() && "expect value to have index type");
   boundMap = AffineMap::getMultiDimIdentityMap(1, value.getContext());
   boundOperands.assign({value});
   canonicalizeMapAndOperands(&boundMap, &boundOperands);
 
void getUpperBoundForIndex(Value value, AffineMap &boundMap,
   };
 
   // Add the affine operations in `backwardSlice` to the constraints.
   for (Operation *op : llvm::reverse(backwardSlice)) {
     // Add an identifier for all op results and operands.
     if (!(llvm::all_of(op->getResults(), findOrCreateId) &&
           llvm::all_of(op->getOperands(), findOrCreateId)))
       return;
+
     // Add AffineApplyOps to the constraints.
     if (auto applyOp = dyn_cast<AffineApplyOp>(op)) {
-      AffineValueMap valueMap(applyOp.getAffineMap(), applyOp.getOperands(),
-                              applyOp.getResult());
-      if (failed(constraints.composeMap(&valueMap)))
+      AffineMap map = constraints.computeAlignedMap(applyOp.getAffineMap(),
+                                                    applyOp.getOperands());
+      if (failed(constraints.addBound(IntegerPolyhedron::EQ,
+                                      getPosition(applyOp.getResult()), map)))
         return;
       continue;
     }
     // Add AffineMinOps to the constraints.
     auto minOp = cast<AffineMinOp>(op);
     AffineMap map = constraints.computeAlignedMap(minOp.getAffineMap(),
                                                   minOp.getOperands());
     if (failed(constraints.addBound(IntegerPolyhedron::UB,
-                                    getPosition(minOp.getResult()), map)))
+                                    getPosition(minOp.getResult()), map,
+                                    /*isClosedBound=*/true)))
       return;
   }
 
   // Obtain an upper bound for the affine index computation by projecting out
   // all temporary results and expressing the upper bound for `value` in terms
   // of the terminals of the index computation.
-  SmallVector<AffineMap> lowerBounds(1), upperBounds(1);
-  constraints.getSliceBounds(getPosition(value), 1, value.getContext(),
-                             &lowerBounds, &upperBounds);
+  unsigned pos = getPosition(value);
+  if (constantRequired) {
+    auto ubConst =
+        constraints.getConstantBound(FlatAffineRelation::BoundType::UB, pos);
+    if (!ubConst.hasValue())
+      return;
 
+    boundMap =
+        AffineMap::getConstantMap(ubConst.getValue(), value.getContext());
+    return;
+  }
+
+  SmallVector<AffineMap> lowerBounds(1), upperBounds(1);
+  constraints.getSliceBounds(pos, 1, value.getContext(), &lowerBounds,
+                             &upperBounds,
+                             /*getClosedUB=*/true);
   // Verify `upperBounds[0]` is valid and has at least one result.
   if (!upperBounds[0] || upperBounds[0].getNumResults() == 0)
     return;
 
   // Set `boundMap` and `boundOperands` to the computed upper bound.
   boundMap = upperBounds[0];
   constraints.getAllValues(&boundOperands);
   erase_value(boundOperands, value);
   canonicalizeMapAndOperands(&boundMap, &boundOperands);
 }
 
 FailureOr<int64_t> getConstantUpperBoundForIndex(Value value) {
   // Compute an upper bound for `value`.
   AffineMap boundMap;
   SmallVector<Value> boundOperands;
-  getUpperBoundForIndex(value, boundMap, boundOperands);
+  getUpperBoundForIndex(value, boundMap, boundOperands,
+                        /*constantRequired=*/true);
 
   // Search the results of `boundMap` for constant upper bounds.
   SmallVector<int64_t> constantBounds;
   for (AffineExpr result : boundMap.getResults())
     if (auto constExpr = result.dyn_cast<AffineConstantExpr>())
       constantBounds.push_back(constExpr.getValue());
 
   // Return the minimal upper bound or failure if none is found.

mlir/test/Dialect/Linalg/pad.mlir

 // RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad padding-values=0.:f32,0.:f32,0.:f32 padding-dimensions=0,1,2 pack-paddings=1,1,0 run-enable-pass=false" -cse -split-input-file | FileCheck %s --check-prefix=MATMUL
 // RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.fill pad padding-values=0.:f32,1.:f32 pack-paddings=0,1 padding-dimensions=0,1,2 run-enable-pass=false" -cse -split-input-file | FileCheck %s --check-prefix=FILL
 // RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.fill pad padding-values=0.:f32,0.:f32 pack-paddings=0,1 padding-dimensions=0,1,2 run-enable-pass=false" -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad padding-values=0.:f32,0.:f32,0.:f32 padding-dimensions=0,1,2 pack-paddings=0,1 run-enable-pass=false" -cse -split-input-file | FileCheck %s --check-prefix=FILL-MATMUL
 // RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad padding-values=0.:f32,0.:f32 pack-paddings=1,1,0 padding-dimensions=0,1,2 run-enable-pass=false" -cse -split-input-file | FileCheck %s --check-prefix=INPUTS-ONLY
 // RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad padding-values=0.:f32,0.:f32,0.:f32 padding-dimensions=0,1 pack-paddings=1,1,1 run-enable-pass=false" -cse -split-input-file | FileCheck %s --check-prefix=PARTIAL
+// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.depthwise_conv_2d_nhwc_hwc pad padding-values=0.:f32,0.:f32,0.:f32 padding-dimensions=1,2 pack-paddings=1,0,1 run-enable-pass=false" -cse -split-input-file | FileCheck %s --check-prefix=DEPTHWISE_CONV_2D
 
 // MATMUL-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<()[s0] -> (-s0 + 12, 7)>
 // MATMUL-DAG: #[[MAP1:[0-9a-z]+]] = affine_map<()[s0] -> (-s0 + 7)>
 #map = affine_map<()[s0] -> (-s0 + 12, 7)>
 
 //      MATMUL:  static_sizes_output_divisible
 // MATMUL-SAME:    %[[ARG0:[0-9a-zA-Z]*]]: tensor<24x12xf32>
 // MATMUL-SAME:    %[[ARG1:[0-9a-zA-Z]*]]: tensor<12x25xf32>
func.func @padding_the_output_dims_only(%arg0: tensor<24x12xf32>,
   %3 = tensor.extract_slice %arg2[%iv0, %iv1] [%0, %0] [1, 1] : tensor<24x25xf32> to tensor<?x?xf32>
 
   //      PARTIAL:  = linalg.matmul ins(%[[T0]], %[[T1]]
   // PARTIAL-SAME:             outs(%[[T2]]
   %4 = linalg.matmul ins(%1, %2 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%3 : tensor<?x?xf32>) -> tensor<?x?xf32>
   %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [%0, %0] [1, 1] : tensor<?x?xf32> into tensor<24x25xf32>
   func.return %5 : tensor<24x25xf32>
 }
+
+// -----
+
+//  DEPTHWISE_CONV_2D-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<()[s0] -> (4, -s0 + 11)>
+//  DEPTHWISE_CONV_2D-DAG: #[[MAP1:[0-9a-z]+]] = affine_map<()[s0] -> (s0 * 2)>
+//  DEPTHWISE_CONV_2D-DAG: #[[MAP2:[0-9a-z]+]] = affine_map<()[s0] -> (s0 * 2 + 1)>
+//  DEPTHWISE_CONV_2D-DAG: #[[MAP3:[0-9a-z]+]] = affine_map<()[s0] -> (s0 * -2 + 8)>
+//  DEPTHWISE_CONV_2D-DAG: #[[MAP4:[0-9a-z]+]] = affine_map<()[s0] -> (-s0 + 4)>
+
+#map0 = affine_map<()[s0] -> (4, -s0 + 11)>
+#map1 = affine_map<()[s0] -> (s0 * 2)>
+#map2 = affine_map<()[s0] -> (s0 * 2 + 1)>
+
+//      DEPTHWISE_CONV_2D: depthwise_conv_2d_padding
+// DEPTHWISE_CONV_2D-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<1x23x3x16xf32>
+// DEPTHWISE_CONV_2D-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<3x3x16xf32>
+// DEPTHWISE_CONV_2D-SAME: %[[ARG2:[0-9a-zA-Z]*]]: tensor<1x13x1x16xf32>
+// DEPTHWISE_CONV_2D-SAME: %[[IV0:[0-9a-zA-Z]*]]: index
+func.func @depthwise_conv_2d_padding(%arg0: tensor<1x23x3x16xf32>,
+                                     %arg1: tensor<3x3x16xf32>,
+                                     %arg2: tensor<1x13x1x16xf32>,
+                                     %iv0: index) -> tensor<1x?x1x16xf32> {
+  //  DEPTHWISE_CONV_2D-DAG: %[[CST:.*]] = arith.constant 0.
+  //  DEPTHWISE_CONV_2D-DAG: %[[C0:.*]] = arith.constant 0 : index
+  //  DEPTHWISE_CONV_2D-DAG: %[[T0:.*]] = affine.min #[[MAP0]]()[%[[IV0]]]
+  %0 = affine.min #map0()[%iv0]
+  %1 = affine.apply #map1()[%iv0]
+  %2 = affine.apply #map2()[%0]
+
+  //      DEPTHWISE_CONV_2D: %[[T3:.*]] = tensor.extract_slice %[[ARG0]]
+  //      DEPTHWISE_CONV_2D: %[[T4:.*]] = tensor.extract_slice %[[ARG2]]
+  %3 = tensor.extract_slice %arg0[0, %1, 0, 0] [1, %2, 3, 16] [1, 1, 1, 1] : tensor<1x23x3x16xf32> to tensor<1x?x3x16xf32>
+  %4 = tensor.extract_slice %arg2[0, %iv0, 0, 0] [1, %0, 1, 16] [1, 1, 1, 1] : tensor<1x13x1x16xf32> to tensor<1x?x1x16xf32>
+
+  // Check the padding on the input.
+  //      DEPTHWISE_CONV_2D: %[[T5:.*]] = affine.apply #[[MAP3]]()[%[[T0]]]
+  //      DEPTHWISE_CONV_2D: %[[T6:.*]] = tensor.pad %[[T3]]
+  // DEPTHWISE_CONV_2D-SAME:                low[%[[C0]], %[[C0]], %[[C0]], %[[C0]]]
+  // DEPTHWISE_CONV_2D-SAME:                high[%[[C0]], %[[T5]], %[[C0]], %[[C0]]]
+  //      DEPTHWISE_CONV_2D: tensor.yield %[[CST]] : f32
+
+  // Check the padding on the output.
+  //      DEPTHWISE_CONV_2D: %[[T7:.*]] = affine.apply #[[MAP4]]()[%[[T0]]]
+  //      DEPTHWISE_CONV_2D: %[[T8:.*]] = tensor.pad %[[T4]]
+  // DEPTHWISE_CONV_2D-SAME:                low[%[[C0]], %[[C0]], %[[C0]], %[[C0]]]
+  // DEPTHWISE_CONV_2D-SAME:                high[%[[C0]], %[[T7]], %[[C0]], %[[C0]]]
+  //      DEPTHWISE_CONV_2D: tensor.yield %[[CST]] : f32
+
+  //      DEPTHWISE_CONV_2D: %[[T9:.*]] = linalg.depthwise_conv_2d_nhwc_hwc
+  // DEPTHWISE_CONV_2D-SAME: ins(%[[T6]], %[[ARG1]] : tensor<1x9x3x16xf32>, tensor<3x3x16xf32>)
+  // DEPTHWISE_CONV_2D-SAME: outs(%[[T8]] : tensor<1x4x1x16xf32>)
+  %5 = linalg.depthwise_conv_2d_nhwc_hwc
+      {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>}
+      ins(%3, %arg1 : tensor<1x?x3x16xf32>, tensor<3x3x16xf32>)
+      outs(%4 : tensor<1x?x1x16xf32>) -> tensor<1x?x1x16xf32>
+
+  // Check the extract_slice to crop the padded output before return.
+  //      DEPTHWISE_CONV_2D: %[[T10:.*]] = tensor.extract_slice %[[T9]][0, 0, 0, 0]
+  // DEPTHWISE_CONV_2D-SAME:                 [1, %[[T0]], 1, 16]
+  //      DEPTHWISE_CONV_2D: return %[[T10]] : tensor<1x?x1x16xf32>
+  return %5 : tensor<1x?x1x16xf32>
+}