[mlir][tensor] Add canonicalization to fold consecutive tensor.pad ops (#107302)

`tensor.pad(tensor.pad)` with the same constant padding value can be
combined into a single pad that pads to the sum of the high and low
padding amounts.

Author: qedawkins

mlir/lib/Dialect/Tensor/IR/TensorOps.cpp

@@ -3402,12 +3402,90 @@ struct FoldStaticPadding : public OpRewritePattern<PadOp> {
   }
 };
 
+/// Folds a chain of `tensor.pad` ops with the same constant padding value.
+///
+/// Example:
+///
+/// ```mlir
+///   %1 = tensor.pad %0 low[0, 1] high[0, 2] {
+///       tensor.yield %val
+///     } : tensor<1x2xf32> to tensor<2x5xf32>
+///   %res = tensor.pad %1 low[0, 2] high[3, 0] {
+///       tensor.yield %val
+///     } : tensor<1x5xf32> to tensor<5x7xf32>
+/// ```
+///
+/// folds into:
+///
+/// ```mlir
+///   %res = tensor.pad %0 low[0, 3] high[3, 2] {
+///       tensor.yield %val
+///     } : tensor<1x2xf32> to tensor<5x7xf32>
+/// ```
+struct FoldConsecutiveConstantPadding : public OpRewritePattern<tensor::PadOp> {
+  using OpRewritePattern<tensor::PadOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tensor::PadOp padOp,
+                                PatternRewriter &rewriter) const override {
+    if (padOp.getNofold()) {
+      return rewriter.notifyMatchFailure(padOp, "skipping unfoldable pad");
+    }
+
+    auto producerPad = padOp.getSource().getDefiningOp<tensor::PadOp>();
+    if (!producerPad || producerPad.getNofold()) {
+      return rewriter.notifyMatchFailure(
+          padOp, "producer is not a foldable tensor.pad op");
+    }
+
+    // Fail if the tensor::PadOps padding values do not match.
+    Value consumerPadValue = padOp.getConstantPaddingValue();
+    Value producerPadValue = producerPad.getConstantPaddingValue();
+    if (!consumerPadValue || !producerPadValue ||
+        consumerPadValue != producerPadValue) {
+      return rewriter.notifyMatchFailure(
+          padOp,
+          "cannot fold PadOps with different or non-constant padding values");
+    }
+
+    Location loc = padOp.getLoc();
+    AffineExpr d0, d1;
+    bindDims(rewriter.getContext(), d0, d1);
+
+    // Combine the low/high paddings of the two tensor::PadOps.
+    auto addPaddings = [&](ArrayRef<OpFoldResult> consumerPaddings,
+                           ArrayRef<OpFoldResult> producerPaddings) {
+      SmallVector<OpFoldResult> sumPaddings;
+      for (auto [consumerIndex, producerIndex] :
+           llvm::zip_equal(consumerPaddings, producerPaddings)) {
+        sumPaddings.push_back(affine::makeComposedFoldedAffineApply(
+            rewriter, loc, d0 + d1, {consumerIndex, producerIndex}));
+      }
+      return sumPaddings;
+    };
+
+    SmallVector<OpFoldResult> newHighPad =
+        addPaddings(padOp.getMixedHighPad(), producerPad.getMixedHighPad());
+    SmallVector<OpFoldResult> newLowPad =
+        addPaddings(padOp.getMixedLowPad(), producerPad.getMixedLowPad());
+
+    auto newPadOp = rewriter.create<tensor::PadOp>(
+        padOp.getLoc(), padOp.getResultType(), producerPad.getSource(),
+        newLowPad, newHighPad, padOp.getNofold(),
+        getPrunedAttributeList(padOp, tensor::PadOp::getAttributeNames()));
+    rewriter.inlineRegionBefore(padOp.getRegion(), newPadOp.getRegion(),
+                                newPadOp.getRegion().begin());
+    rewriter.replaceOp(padOp, newPadOp.getResult());
+    return success();
+  }
+};
+
 } // namespace
 
 void PadOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                         MLIRContext *context) {
   results.add<FoldStaticZeroPadding, FoldSourceTensorCast, FoldTargetTensorCast,
-              FoldOrthogonalPaddings, FoldStaticPadding>(context);
+              FoldOrthogonalPaddings, FoldStaticPadding,
+              FoldConsecutiveConstantPadding>(context);
 }
 
 /// Return the padding value of the PadOp if it constant. In this context,

mlir/test/Dialect/Tensor/canonicalize.mlir

@@ -1964,6 +1964,88 @@ func.func @dont_fold_pad_chains(%arg0: tensor<64x64xf32>,
 
 // -----
 
+// CHECK-LABEL: func @merge_constant_padding
+//  CHECK-SAME:   %[[ARG0:[A-Za-z0-9]+]]: tensor<2x3xf32>
+//  CHECK-SAME:   %[[PADVAL:[A-Za-z0-9]+]]: f32
+//       CHECK:   %[[PAD:.+]] = tensor.pad %[[ARG0]] low[1, 3] high[4, 2]
+//       CHECK:     tensor.yield %[[PADVAL]]
+//       CHECK:   return %[[PAD]]
+func.func @merge_constant_padding(%arg0: tensor<2x3xf32>, %pad_value: f32) -> tensor<7x8xf32> {
+  %pad0 = tensor.pad %arg0 low[1, 1] high[1, 0] {
+    ^bb0(%b0: index, %b1 : index):
+      tensor.yield %pad_value : f32
+    } : tensor<2x3xf32> to tensor<4x4xf32>
+  %pad1 = tensor.pad %pad0 low[0, 2] high[3, 2] {
+    ^bb0(%b2: index, %b3 : index):
+      tensor.yield %pad_value : f32
+    } : tensor<4x4xf32> to tensor<7x8xf32>
+  return %pad1 : tensor<7x8xf32>
+}
+
+// -----
+
+//       CHECK: #[[$MAP:.*]] = affine_map<()[s0] -> (s0 + 1)>
+// CHECK-LABEL: func @merge_constant_padding_dynamic
+//  CHECK-SAME:   %[[ARG0:[A-Za-z0-9]+]]: tensor<?x?xf32>
+//  CHECK-SAME:   %[[IDX:[A-Za-z0-9]+]]: index
+//  CHECK-SAME:   %[[PADVAL:[A-Za-z0-9]+]]: f32
+//       CHECK:   %[[HIGH:.+]] = affine.apply #[[$MAP]]()[%[[IDX]]]
+//       CHECK:   %[[PAD:.+]] = tensor.pad %[[ARG0]] low[%[[IDX]], 3] high[%[[HIGH]], 2]
+//       CHECK:     tensor.yield %[[PADVAL]]
+//       CHECK:   return %[[PAD]]
+func.func @merge_constant_padding_dynamic(%arg0: tensor<?x?xf32>, %idx: index, %pad_value: f32) -> tensor<?x?xf32> {
+  %pad0 = tensor.pad %arg0 low[%idx, 1] high[1, 0] {
+    ^bb0(%b0: index, %b1 : index):
+      tensor.yield %pad_value : f32
+    } : tensor<?x?xf32> to tensor<?x?xf32>
+  %pad1 = tensor.pad %pad0 low[0, 2] high[%idx, 2] {
+    ^bb0(%b2: index, %b3 : index):
+      tensor.yield %pad_value : f32
+    } : tensor<?x?xf32> to tensor<?x?xf32>
+  return %pad1 : tensor<?x?xf32>
+}
+
+// -----
+
+// Verify that folding does not happen if it would drop a nofold attribute
+// CHECK-LABEL: func @dont_merge_constant_padding_nofold
+//       CHECK:   tensor.pad {{.*}} nofold
+//       CHECK:   tensor.pad
+func.func @dont_merge_constant_padding_nofold(%arg0: tensor<2x3xf32>, %pad_value: f32) -> tensor<7x8xf32> {
+  %pad0 = tensor.pad %arg0 nofold low[1, 1] high[1, 0] {
+    ^bb0(%b0: index, %b1 : index):
+      tensor.yield %pad_value : f32
+    } : tensor<2x3xf32> to tensor<4x4xf32>
+  %pad1 = tensor.pad %pad0 low[0, 2] high[3, 2] {
+    ^bb0(%b2: index, %b3 : index):
+      tensor.yield %pad_value : f32
+    } : tensor<4x4xf32> to tensor<7x8xf32>
+  return %pad1 : tensor<7x8xf32>
+}
+
+// -----
+
+// Verify that folding does not happen if it would drop a nofold attribute
+// CHECK-LABEL: func @dont_merge_constant_padding_different_vals
+//       CHECK:   tensor.pad
+//       CHECK:   tensor.pad
+func.func @dont_merge_constant_padding_different_vals(
+    %arg0: tensor<2x3xf32>,
+    %pad_value0: f32,
+    %pad_value1: f32) -> tensor<7x8xf32> {
+  %pad0 = tensor.pad %arg0 low[1, 1] high[1, 0] {
+    ^bb0(%b0: index, %b1 : index):
+      tensor.yield %pad_value0 : f32
+    } : tensor<2x3xf32> to tensor<4x4xf32>
+  %pad1 = tensor.pad %pad0 low[0, 2] high[3, 2] {
+    ^bb0(%b2: index, %b3 : index):
+      tensor.yield %pad_value1 : f32
+    } : tensor<4x4xf32> to tensor<7x8xf32>
+  return %pad1 : tensor<7x8xf32>
+}
+
+// -----
+
 // CHECK-LABEL: func @fold_collapse_shape_from_elements
 func.func @fold_collapse_shape_from_elements(%arg0: i32) -> tensor<i32> {
   // CHECK: %[[FROM:.+]] = tensor.from_elements %arg0 : tensor<i32>