[mlir][linalg] Convert tensor.pad to destination style

[springerm]

Summary

This can be a pre-processing for bufferization and allows for more efficient lowerings without an alloc.

Depends On: D142206

[mravishankar]

mlir/lib/Dialect/Tensor/Transforms/ConvertToDestinationStyle.cpp

164 ↗

(On Diff #490803)

This logic is implemented on the pad op using the reifyResultShapes interface...Could we just reuse that?

187 ↗

(On Diff #490803)

I am not sure I want to go down this path. This linalg.generic isnt vectorizable AFAIK. We could use a slice and insert, etc. but tahts too cumbersome. For now maybe just handle the zero-padded case....

[springerm]

mlir/lib/Dialect/Tensor/Transforms/ConvertToDestinationStyle.cpp

187 ↗

(On Diff #490803)

OK then I'm going to add a default linalg.fill lowering here, so that it can vectorize. In the unlikely case that it is not a constant, it will still lower to linalg,generic.

[springerm]

address comments

[springerm]

rebase

[mravishankar]

Unblocking, but have one comment remaining.

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

114

First, I am not sure we need to make this distinction.... constant yielded value is already outside the op.

Even if we need to, could this be made simpler by just having

if (matchPattern(yieldedValue, m_Constant(...)) {
  yieldedValue = ...
}
auto fillOp = rewriter.create<linalg::FillOp>(..., yieldeValue, ...);

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

 // without an allocation (assuming there are no further conflicts).
 //
 //===----------------------------------------------------------------------===//
 //
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/Matchers.h"
 #include "mlir/IR/PatternMatch.h"
 #include "llvm/Support/Debug.h"
 
 using namespace mlir;
 using namespace mlir::tensor;
 
 namespace {
 
LogicalResult matchAndRewrite(GenerateOp generateOp,
     rewriter.replaceOpWithNewOp<linalg::YieldOp>(yieldOp, yieldOp.getValue());
 
     // Replace tensor.generate.
     rewriter.replaceOp(generateOp, genericOp->getResult(0));
     return success();
   }
 };
 
+/// Lower tensor.pad to linalg.generic + tensor.insert_slice.
+struct PadOpConverter : public OpRewritePattern<PadOp> {
+  using OpRewritePattern<PadOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(PadOp padOp,
+                                PatternRewriter &rewriter) const override {
+    // Only ops with exactly one block are supported.
+    if (!padOp.getBodyRegion().hasOneBlock())
+      return failure();
+
+    // Create tensor.empty.
+    Location loc = padOp.getLoc();
+    RankedTensorType resultType = padOp.getResultType();
+    ReifiedRankedShapedTypeDims reifiedShape;
+    if (failed(cast<ReifyRankedShapedTypeOpInterface>(padOp.getOperation())
+                   .reifyResultShapes(rewriter, reifiedShape)))
+      return rewriter.notifyMatchFailure(
+          padOp, "failed to reify tensor.pad op result shape");
+    SmallVector<Value> dynamicSizes;
+    for (int64_t i = 0; i < resultType.getRank(); ++i)
+      if (resultType.isDynamicDim(i))
+        dynamicSizes.push_back(reifiedShape[0][i]);
+    auto emptyOp = rewriter.create<EmptyOp>(loc, resultType, dynamicSizes);
+
+    // Examine the yielded value to decide if a linalg.generic is neede or a
+    // linalg.fill is sufficient.
+    Value filled;
+    Value yieldedValue =
+        cast<tensor::YieldOp>(padOp.getBody()->getTerminator()).getValue();
+    Attribute constYieldedValue;
+    // Is the yielded value a bbArg defined outside of the PadOp?
+    bool outsideBbArg =
+        yieldedValue.isa<BlockArgument>() &&
+        yieldedValue.cast<BlockArgument>().getOwner()->getParentOp() !=
+            padOp.getOperation();
+    // Is the yielded value an OpResult defined outside of the PadOp?
+    bool outsideOpResult =
+        yieldedValue.isa<OpResult>() &&
+        yieldedValue.getDefiningOp()->getParentOp() != padOp.getOperation();
+    bool invariantYieldedValue = outsideBbArg || outsideOpResult;
+    if (matchPattern(yieldedValue, m_Constant(&constYieldedValue))) {
+      // Padding with a constant: Create linalg.fill.
+      Dialect *arithDialect =
+          rewriter.getContext()->getLoadedDialect<arith::ArithDialect>();
+      Value fillValue = arithDialect
+                            ->materializeConstant(rewriter, constYieldedValue,
+                                                  yieldedValue.getType(),
+                                                  yieldedValue.getLoc())
+                            ->getResult(0);
+      auto fillOp = rewriter.create<linalg::FillOp>(
+          loc, ValueRange(fillValue), ValueRange(emptyOp.getResult()));
+      rewriter.setInsertionPointAfter(fillOp);
+      filled = fillOp.getResult(0);
+    } else if (invariantYieldedValue) {
+      // Padding with an invariant value.
+      auto fillOp = rewriter.create<linalg::FillOp>(
+          loc, ValueRange(yieldedValue), ValueRange(emptyOp.getResult()));
+      rewriter.setInsertionPointAfter(fillOp);
+      filled = fillOp.getResult(0);
+    } else {
+      // Create linalg.generic.
+      SmallVector<utils::IteratorType> iteratorTypes(
+          resultType.getRank(), utils::IteratorType::parallel);
+      SmallVector<AffineMap> indexingMaps(
+          1, rewriter.getMultiDimIdentityMap(resultType.getRank()));
+      auto genericOp = rewriter.create<linalg::GenericOp>(
+          loc, resultType, /*inputs=*/ValueRange(),
+          /*outputs=*/ValueRange{emptyOp.getResult()}, /*indexingMaps=*/
+          indexingMaps, iteratorTypes);
+      Block *body = rewriter.createBlock(&genericOp->getRegion(0), {},
+                                         resultType.getElementType(), loc);
+      rewriter.setInsertionPointToStart(body);
+      SmallVector<Value> bbArgReplacements;
+      for (int64_t i = 0; i < resultType.getRank(); ++i)
+        bbArgReplacements.push_back(rewriter.create<linalg::IndexOp>(loc, i));
+      rewriter.mergeBlocks(padOp.getBody(), body, bbArgReplacements);
+
+      // Update terminator.
+      auto yieldOp = cast<tensor::YieldOp>(body->getTerminator());
+      rewriter.replaceOpWithNewOp<linalg::YieldOp>(yieldOp, yieldOp.getValue());
+      rewriter.setInsertionPointAfter(genericOp);
+      filled = genericOp->getResult(0);
+    }
+
+    // Create tensor::InsertSliceOp.
+    SmallVector<OpFoldResult> sliceSizes =
+        getMixedSizes(rewriter, loc, padOp.getSource());
+    SmallVector<OpFoldResult> sliceStrides(resultType.getRank(),
+                                           rewriter.getIndexAttr(1));
+    rewriter.replaceOpWithNewOp<tensor::InsertSliceOp>(
+        padOp, padOp.getSource(), filled,
+        /*offsets=*/padOp.getMixedLowPad(), sliceSizes, sliceStrides);
+
+    return success();
+  }
+};
+
 } // namespace
 
 void linalg::populateConvertToDestinationStylePatterns(
     RewritePatternSet &patterns) {
-  patterns.insert<GenerateOpConverter>(patterns.getContext());
+  patterns.insert<GenerateOpConverter, PadOpConverter>(patterns.getContext());
 }

mlir/test/Dialect/Linalg/convert-to-destination-style.mlir

-// RUN: mlir-opt -split-input-file -test-linalg-transform-patterns=test-convert-to-destination-style-patterns %s | FileCheck %s
+// RUN: mlir-opt -split-input-file -test-linalg-transform-patterns=test-convert-to-destination-style-patterns -canonicalize %s | FileCheck %s
 
 // CHECK: #[[$map:.*]] = affine_map<(d0, d1) -> (d0, d1)>
 // CHECK-LABEL: func @tensor_generate(
 //  CHECK-SAME:     %[[s1:.*]]: index, %[[s2:.*]]: index
 //       CHECK:   %[[empty:.*]] = tensor.empty(%[[s1]], %[[s2]]) : tensor<?x?xindex>
 //       CHECK:   %[[generic:.*]] = linalg.generic
 //  CHECK-SAME:       {indexing_maps = [#[[$map]]], iterator_types = ["parallel", "parallel"]}
 //  CHECK-SAME:       outs(%[[empty]] : tensor<?x?xindex>) {
 //       CHECK:     %[[i0:.*]] = linalg.index 0
 //       CHECK:     %[[i1:.*]] = linalg.index 1
 //       CHECK:     %[[added:.*]] = arith.addi %[[i0]], %[[i1]]
 //       CHECK:     linalg.yield %[[added]]
 //       CHECK:   }
 //       CHECK:   return %[[generic]]
 func.func @tensor_generate(%s1: index, %s2: index) -> tensor<?x?xindex> {
   %0 = tensor.generate %s1, %s2 {
     ^bb0(%arg0: index, %arg1: index):
     %1 = arith.addi %arg0, %arg1 : index
     tensor.yield %1 : index
   } : tensor<?x?xindex>
   return %0 : tensor<?x?xindex>
 }
+
+// -----
+
+// CHECK:       #[[$map:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 5)>
+// CHECK:       #[[$map1:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 10)>
+// CHECK:       #[[$map2:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-LABEL: func @tensor_pad(
+//  CHECK-SAME:   %[[t1:.*]]: tensor<?x10xindex>, %[[l2:.*]]: index, %[[h1:.*]]: index, %[[h2:.*]]: index
+//   CHECK-DAG:   %[[c0:.*]] = arith.constant 0 : index
+//   CHECK-DAG:   %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]]
+//   CHECK-DAG:   %[[size0:.*]] = affine.apply #[[$map]]()[%[[h1]], %[[dim0]]]
+//   CHECK-DAG:   %[[size1:.*]] = affine.apply #[[$map1]]()[%[[l2]], %[[h2]]]
+//       CHECK:   %[[empty:.*]] = tensor.empty(%[[size0]], %[[size1]]) : tensor<?x?xindex>
+//       CHECK:   %[[generic:.*]] = linalg.generic
+//  CHECK-SAME:       {indexing_maps = [#[[$map2]]], iterator_types = ["parallel", "parallel"]}
+//  CHECK-SAME:       outs(%[[empty]] : tensor<?x?xindex>) {
+//       CHECK:     %[[i0:.*]] = linalg.index 0
+//       CHECK:     %[[i1:.*]] = linalg.index 1
+//       CHECK:     %[[mul:.*]] = arith.muli %[[i0]], %[[i1]]
+//       CHECK:     linalg.yield %[[mul]]
+//       CHECK:   }
+//   CHECK-DAG:   %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]]
+//       CHECK:   %[[inserted:.*]] = tensor.insert_slice %[[t1]] into %[[generic]][5, %[[l2]]] [%[[dim0]], 10] [1, 1] : tensor<?x10xindex> into tensor<?x?xindex>
+//       CHECK:   return %[[inserted]]
+func.func @tensor_pad(%t1: tensor<?x10xindex>, %l2: index, %h1: index,
+                      %h2: index) -> tensor<?x?xindex> {
+  %0 = tensor.pad %t1 low[5, %l2] high[%h1, %h2] {
+  ^bb0(%arg0: index, %arg1: index):
+    %m = arith.muli %arg0, %arg1 : index
+    tensor.yield %m : index
+  } : tensor<?x10xindex> to tensor<?x?xindex>
+  return %0 : tensor<?x?xindex>
+}
+
+// -----
+
+// CHECK:       #[[$map:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 5)>
+// CHECK:       #[[$map1:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 10)>
+// CHECK-LABEL: func @tensor_pad_constant(
+//  CHECK-SAME:   %[[t1:.*]]: tensor<?x10xindex>, %[[l2:.*]]: index, %[[h1:.*]]: index, %[[h2:.*]]: index
+//   CHECK-DAG:   %[[c0:.*]] = arith.constant 0 : index
+//   CHECK-DAG:   %[[c50:.*]] = arith.constant 50 : index
+//   CHECK-DAG:   %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]]
+//   CHECK-DAG:   %[[size0:.*]] = affine.apply #[[$map]]()[%[[h1]], %[[dim0]]]
+//   CHECK-DAG:   %[[size1:.*]] = affine.apply #[[$map1]]()[%[[l2]], %[[h2]]]
+//       CHECK:   %[[empty:.*]] = tensor.empty(%[[size0]], %[[size1]]) : tensor<?x?xindex>
+//       CHECK:   %[[filled:.*]] = linalg.fill ins(%[[c50]] : index) outs(%[[empty]] : tensor<?x?xindex>)
+//   CHECK-DAG:   %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]]
+//       CHECK:   %[[inserted:.*]] = tensor.insert_slice %[[t1]] into %[[filled]][5, %[[l2]]] [%[[dim0]], 10] [1, 1] : tensor<?x10xindex> into tensor<?x?xindex>
+//       CHECK:   return %[[inserted]]
+func.func @tensor_pad_constant(%t1: tensor<?x10xindex>, %l2: index, %h1: index,
+                               %h2: index) -> tensor<?x?xindex> {
+  %0 = tensor.pad %t1 low[5, %l2] high[%h1, %h2] {
+  ^bb0(%arg0: index, %arg1: index):
+    %c = arith.constant 50 : index
+    tensor.yield %c : index
+  } : tensor<?x10xindex> to tensor<?x?xindex>
+  return %0 : tensor<?x?xindex>
+}
+
+// -----
+
+// CHECK:       #[[$map:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 5)>
+// CHECK:       #[[$map1:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 10)>
+// CHECK-LABEL: func @tensor_pad_invariant(
+//  CHECK-SAME:   %[[t1:.*]]: tensor<?x10xindex>, %[[l2:.*]]: index, %[[h1:.*]]: index, %[[h2:.*]]: index, %[[padding:.*]]: index
+//   CHECK-DAG:   %[[c0:.*]] = arith.constant 0 : index
+//   CHECK-DAG:   %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]]
+//   CHECK-DAG:   %[[size0:.*]] = affine.apply #[[$map]]()[%[[h1]], %[[dim0]]]
+//   CHECK-DAG:   %[[size1:.*]] = affine.apply #[[$map1]]()[%[[l2]], %[[h2]]]
+//       CHECK:   %[[empty:.*]] = tensor.empty(%[[size0]], %[[size1]]) : tensor<?x?xindex>
+//       CHECK:   %[[filled:.*]] = linalg.fill ins(%[[padding]] : index) outs(%[[empty]] : tensor<?x?xindex>)
+//   CHECK-DAG:   %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]]
+//       CHECK:   %[[inserted:.*]] = tensor.insert_slice %[[t1]] into %[[filled]][5, %[[l2]]] [%[[dim0]], 10] [1, 1] : tensor<?x10xindex> into tensor<?x?xindex>
+//       CHECK:   return %[[inserted]]
+func.func @tensor_pad_invariant(%t1: tensor<?x10xindex>, %l2: index, %h1: index,
+                                %h2: index, %padding: index) -> tensor<?x?xindex> {
+  %0 = tensor.pad %t1 low[5, %l2] high[%h1, %h2] {
+  ^bb0(%arg0: index, %arg1: index):
+    tensor.yield %padding : index
+  } : tensor<?x10xindex> to tensor<?x?xindex>
+  return %0 : tensor<?x?xindex>
+}