[mlir][linalg] Fold tensor.pad when inserting into linalg.fill

[antiagainst]

Summary

Fold tensor.insert_slice(tensor.pad(<input>), linalg.fill) into
tensor.insert_slice(<input>, linalg.fill) if the padding value and
the filling value are the same.

[ThomasRaoux]

mlir/test/Dialect/Linalg/canonicalize.mlir

786

nit: you can still use CHECK-LABEL if you make the map name $MAP (as it prevents the variable to get cleared by CHECK-LABEL)

[antiagainst]

mlir/test/Dialect/Linalg/canonicalize.mlir

786

I didn't know about that! Thanks a lot for the tip!

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

auto newFillOp =
         rewriter.create<FillOp>(fillOp.getLoc(), padValue, newInitOp);
     rewriter.replaceOpWithNewOp<tensor::CastOp>(padOp, oldResultType,
                                                 newFillOp.result());
 
     return success();
   }
 };
 
+/// Fold tensor.insert_slice(tensor.pad(<input>), linalg.fill) into
+/// tensor.insert_slice(<input>, linalg.fill) if the padding value and the
+/// filling value are the same.
+struct FoldInsertPadIntoFill : public OpRewritePattern<tensor::InsertSliceOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tensor::InsertSliceOp insertOp,
+                                PatternRewriter &rewriter) const override {
+    auto srcPadOp = insertOp.source().getDefiningOp<tensor::PadOp>();
+    if (!srcPadOp)
+      return failure();
+
+    auto dstFillOp = insertOp.dest().getDefiningOp<linalg::FillOp>();
+    if (!dstFillOp)
+      return failure();
+
+    // We can only fold if the padding value is the same as the original
+    // filling value.
+    Value padValue = srcPadOp.getConstantPaddingValue();
+    if (!padValue || dstFillOp.value() != padValue)
+      return failure();
+
+    SmallVector<OpFoldResult> lowPads = srcPadOp.getMixedLowPad();
+    SmallVector<OpFoldResult> oldOffsets = insertOp.getMixedOffsets();
+
+    Location loc = insertOp.getLoc();
+    MLIRContext *context = getContext();
+
+    AffineExpr sym0, sym1;
+    bindSymbols(context, sym0, sym1);
+    auto addMap = AffineMap::get(0, 2, {sym0 + sym1}, context);
+
+    // Calculate the new offsets for the insert. It should be the old offsets
+    // plus low padding sizes.
+    SmallVector<OpFoldResult, 4> newOffsets;
+    for (const auto &p : llvm::zip(lowPads, oldOffsets)) {
+      Value padValue = getValueOrCreateConstantIndexOp(
+          rewriter, srcPadOp.getLoc(), std::get<0>(p));
+      Value offsetValue = getValueOrCreateConstantIndexOp(
+          rewriter, insertOp.getLoc(), std::get<1>(p));
+      newOffsets.push_back(
+          applyMapToValues(rewriter, loc, addMap, {offsetValue, padValue})[0]);
+    }
+
+    SmallVector<OpFoldResult, 4> newSizes;
+    for (int i = 0, e = srcPadOp.getSourceType().getRank(); i < e; ++i) {
+      newSizes.push_back(
+          rewriter.create<tensor::DimOp>(loc, srcPadOp.source(), i).result());
+    }
+
+    rewriter.replaceOpWithNewOp<tensor::InsertSliceOp>(
+        insertOp, srcPadOp.source(), insertOp.dest(), newOffsets, newSizes,
+        insertOp.getMixedStrides());
+    return success();
+  }
+};
+
 } // namespace
 
 void FillOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                          MLIRContext *context) {
   results
       .add<FoldFillWithPad, FoldFillWithTensorReshape<tensor::CollapseShapeOp>,
-           FoldFillWithTensorReshape<tensor::ExpandShapeOp>>(context);
+           FoldFillWithTensorReshape<tensor::ExpandShapeOp>,
+           FoldInsertPadIntoFill>(context);
 }
 
 //===----------------------------------------------------------------------===//
 // GenericOps
 //===----------------------------------------------------------------------===//
 void GenericOp::build(
     OpBuilder &builder, OperationState &result, TypeRange resultTensorTypes,
     ValueRange inputs, ValueRange outputs, ArrayRef<AffineMap> indexingMaps,

mlir/test/Dialect/Linalg/canonicalize.mlir

^bb0(%arg2 : f32, %arg3 : f32, %arg4 : f32):
     linalg.yield %9 : f32
   } -> (tensor<?x?x?xf32>)
   %7 = tensor.cast %6 : tensor<?x?x?xf32> to tensor<2x3x4xf32>
   return %7: tensor<2x3x4xf32>
     //  CHECK:      %[[GENERIC_OP:.*]] = linalg.generic
     //  CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<2x3x4xf32>, tensor<2x3x4xf32>)
     //  CHECK-SAME: outs({{.*}} : tensor<2x3x4xf32>)
 }
+
+// -----
+
+//       CHECK: #[[MAP:.+]] = affine_map<()[s0] -> (s0 + 1)>
+//       CHECK: func @insert_pad_into_fill
+//  CHECK-SAME: (%[[INPUT:.+]]: tensor<?x?x?xf32>, %[[LOW0:.+]]: index, %[[LOW1:.+]]: index, %{{.+}}: index, %{{.+}}: index)
+//   CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
+//   CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
+//   CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
+//   CHECK-DAG: %[[F0:.+]] = arith.constant 0.000000e+00 : f32
+//       CHECK: %[[INIT:.+]] = linalg.init_tensor [8, 384, 384]
+//       CHECK: %[[FILL:.+]] = linalg.fill(%[[F0]], %[[INIT]])
+//       CHECK: %[[OFFSET1:.+]] = affine.apply #[[MAP]]()[%[[LOW1]]]
+//       CHECK: %[[D0:.+]] = tensor.dim %[[INPUT]], %[[C0]] : tensor<?x?x?xf32>
+//       CHECK: %[[D1:.+]] = tensor.dim %[[INPUT]], %[[C1]] : tensor<?x?x?xf32>
+//       CHECK: %[[D2:.+]] = tensor.dim %[[INPUT]], %[[C2]] : tensor<?x?x?xf32>
+//       CHECK: tensor.insert_slice %[[INPUT]] into %[[FILL]][%[[LOW0]], %[[OFFSET1]], 2] [%[[D0]], %[[D1]], %[[D2]]] [1, 1, 1]
+func @insert_pad_into_fill(%input: tensor<?x?x?xf32>, %low0: index, %low1: index, %high1: index, %high2: index) -> tensor<8x384x384xf32> {
+  %f0 = arith.constant 0.0 : f32
+  %c0 = arith.constant 0 : index
+  %pad = tensor.pad %input low[%low0, %low1, %c0] high[%c0, %high1, %high2] {
+  ^bb0(%arg3: index, %arg4: index, %arg5: index):
+    tensor.yield %f0 : f32
+  } : tensor<?x?x?xf32> to tensor<8x128x128xf32>
+  %init = linalg.init_tensor [8, 384, 384] : tensor<8x384x384xf32>
+  %fill = linalg.fill(%f0, %init) : f32, tensor<8x384x384xf32> -> tensor<8x384x384xf32>
+  %0 = tensor.insert_slice %pad into %fill[0, 1, 2] [8, 128, 128] [1, 1, 1] : tensor<8x128x128xf32> into tensor<8x384x384xf32>
+  return %0: tensor<8x384x384xf32>
+}