[mlir][Tensor] Rework ReifyRankedShapedTypeInterface implementation…

… for `tensor.expand_shape` op. The op carries the output-shape directly. This can be used directly. Also adds a method to get the shape as a `SmallVector<OpFoldResult>`. Signed-off-by: MaheshRavishankar <[email protected]>
llvm · Jan 25, 2025 · e10d18d · e10d18d
1 parent 3b35b4c
commit e10d18d
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Showing 8 changed files with 43 additions and 116 deletions.
diff --git a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
@@ -1165,6 +1165,9 @@ def Tensor_ExpandShapeOp : Tensor_ReassociativeReshapeOp<"expand_shape"> {
   let extraClassDeclaration = commonExtraClassDeclaration # [{
     int64_t getCorrespondingSourceDim(int64_t resultDim);
 
+    // Return output shape as mixes static/dynamic shapes.
+    SmallVector<OpFoldResult> getMixedOutputShape();
+
     // Infer the output shape for a tensor.expand_shape when it is possible
     // to do so.
     static FailureOr<SmallVector<OpFoldResult>> inferOutputShape(

diff --git a/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h b/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
@@ -144,6 +144,9 @@ bool isEqualConstantIntOrValueArray(ArrayRef<OpFoldResult> ofrs1,
 /// Return a vector of OpFoldResults with the same size a staticValues, but
 /// all elements for which ShapedType::isDynamic is true, will be replaced by
 /// dynamicValues.
+SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
+                                         ValueRange dynamicValues,
+                                         MLIRContext *context);
 SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
                                          ValueRange dynamicValues, Builder &b);
 

diff --git a/mlir/lib/Dialect/Tensor/IR/TensorInferTypeOpInterfaceImpl.cpp b/mlir/lib/Dialect/Tensor/IR/TensorInferTypeOpInterfaceImpl.cpp
@@ -16,24 +16,6 @@
 using namespace mlir;
 using namespace mlir::tensor;
 
-/// Compute a map that for a given dimension of the expanded type gives the
-/// dimension in the collapsed type it maps to. Essentially its the inverse of
-/// the `reassocation` maps.
-static llvm::DenseMap<int64_t, int64_t>
-getExpandedDimToCollapsedDimMap(ArrayRef<AffineMap> reassociation) {
-  llvm::DenseMap<int64_t, int64_t> expandedDimToCollapsedDim;
-  for (const auto &map : enumerate(reassociation)) {
-    unsigned startPos =
-        cast<AffineDimExpr>(map.value().getResults().front()).getPosition();
-    unsigned endPos =
-        cast<AffineDimExpr>(map.value().getResults().back()).getPosition();
-    for (auto dim : llvm::seq_inclusive(startPos, endPos)) {
-      expandedDimToCollapsedDim[dim] = map.index();
-    }
-  }
-  return expandedDimToCollapsedDim;
-}
-
 /// For reshape op compute the shape at dimension `dimIndex` of the output in
 /// terms of shape of the `src`, when the reshape op is a collapsing
 /// operation. It is the product of the shape of the collapsed dimensions of the
@@ -76,84 +58,15 @@ static SmallVector<OpFoldResult, 4> getCollapsedOutputShapeFromInputShape(
       }));
 }
 
-/// For an expanding reshape op, compute the value for a dimension of the output
-/// from the shape of the input.
-static OpFoldResult getExpandedOutputDimFromInputShape(
-    OpBuilder &builder, Location loc, int64_t dimIndex, Value src,
-    ArrayRef<int64_t> dstStaticShape, ArrayRef<AffineMap> reassociation,
-    llvm::DenseMap<int64_t, int64_t> &expandedDimToCollapsedDim) {
-  if (!ShapedType::isDynamic(dstStaticShape[dimIndex])) {
-    // Static dimension: return Attribute.
-    return builder.getIndexAttr(dstStaticShape[dimIndex]);
-  }
-  unsigned sourceDimPos = expandedDimToCollapsedDim[dimIndex];
-  unsigned startPos =
-      cast<AffineDimExpr>(reassociation[sourceDimPos].getResults().front())
-          .getPosition();
-  unsigned endPos =
-      cast<AffineDimExpr>(reassociation[sourceDimPos].getResults().back())
-          .getPosition();
-  int64_t linearizedStaticDim = 1;
-  for (auto d :
-       llvm::enumerate(dstStaticShape.slice(startPos, endPos - startPos + 1))) {
-    if (d.index() + startPos == static_cast<unsigned>(dimIndex))
-      continue;
-    assert(!ShapedType::isDynamic(d.value()) &&
-           "single dimension cannot be expanded into multiple dynamic "
-           "dimensions");
-    linearizedStaticDim *= d.value();
-  }
-  OpFoldResult sourceDim =
-      builder.create<tensor::DimOp>(loc, src, sourceDimPos).getResult();
-
-  // Dynamic dimension: return Value.
-  return affine::makeComposedAffineApply(
-             builder, loc,
-             AffineMap::get(
-                 0, 1,
-                 builder.getAffineSymbolExpr(0).floorDiv(linearizedStaticDim)),
-             sourceDim)
-      ->getResult(0);
-}
-
-/// Given the `src` of an expanding reshape op, the reassociation maps and the
-/// result type, compute the shape of the result of the reshape.
-static SmallVector<OpFoldResult, 4> getExpandedOutputShapeFromInputShape(
-    OpBuilder &builder, Location loc, Value src,
-    ArrayRef<int64_t> dstStaticShape, ArrayRef<AffineMap> reassociation) {
-  llvm::DenseMap<int64_t, int64_t> expandedDimToCollapsedDim =
-      getExpandedDimToCollapsedDimMap(reassociation);
-  return llvm::to_vector<4>(llvm::map_range(
-      llvm::seq<int64_t>(0, dstStaticShape.size()), [&](int64_t dim) {
-        return getExpandedOutputDimFromInputShape(builder, loc, dim, src,
-                                                  dstStaticShape, reassociation,
-                                                  expandedDimToCollapsedDim);
-      }));
-}
-
-static SmallVector<OpFoldResult, 4>
-getReshapeOutputShapeFromInputShape(OpBuilder &builder, Location loc, Value src,
-                                    ArrayRef<int64_t> dstStaticShape,
-                                    ArrayRef<AffineMap> reassocation) {
-  return dstStaticShape.size() >
-                 static_cast<size_t>(
-                     llvm::cast<ShapedType>(src.getType()).getRank())
-             ? getExpandedOutputShapeFromInputShape(
-                   builder, loc, src, dstStaticShape, reassocation)
-             : getCollapsedOutputShapeFromInputShape(
-                   builder, loc, src, dstStaticShape, reassocation);
-}
-
-template <typename OpTy>
-struct ReifyExpandOrCollapseShapeOp
+struct ReifyCollapseShapeOp
     : public ReifyRankedShapedTypeOpInterface::ExternalModel<
-          ReifyExpandOrCollapseShapeOp<OpTy>, OpTy> {
+          ReifyCollapseShapeOp, CollapseShapeOp> {
   LogicalResult
   reifyResultShapes(Operation *op, OpBuilder &b,
                     ReifiedRankedShapedTypeDims &reifiedReturnShapes) const {
     auto loc = op->getLoc();
-    auto reshapeOp = cast<OpTy>(op);
-    reifiedReturnShapes.push_back(getReshapeOutputShapeFromInputShape(
+    auto reshapeOp = cast<tensor::CollapseShapeOp>(op);
+    reifiedReturnShapes.push_back(getCollapsedOutputShapeFromInputShape(
         b, loc, reshapeOp.getSrc(), reshapeOp.getResultType().getShape(),
         reshapeOp.getReassociationMaps()));
     return success();
@@ -162,6 +75,20 @@ struct ReifyExpandOrCollapseShapeOp
 
 namespace {
 
+struct ReifyExpandShapeOp
+    : public ReifyRankedShapedTypeOpInterface::ExternalModel<ReifyExpandShapeOp,
+                                                             ExpandShapeOp> {
+  LogicalResult
+  reifyResultShapes(Operation *op, OpBuilder &b,
+                    ReifiedRankedShapedTypeDims &reifyResultShapes) const {
+    auto expandShapeOp = cast<tensor::ExpandShapeOp>(op);
+    SmallVector<OpFoldResult> resultShapes =
+        expandShapeOp.getMixedOutputShape();
+    reifyResultShapes.emplace_back(std::move(resultShapes));
+    return success();
+  }
+};
+
 struct ReifyPadOp
     : public ReifyRankedShapedTypeOpInterface::ExternalModel<ReifyPadOp,
                                                              PadOp> {
@@ -202,10 +129,8 @@ struct ReifyPadOp
 void mlir::tensor::registerInferTypeOpInterfaceExternalModels(
     DialectRegistry &registry) {
   registry.addExtension(+[](MLIRContext *ctx, TensorDialect *dialect) {
-    ExpandShapeOp::attachInterface<
-        ReifyExpandOrCollapseShapeOp<tensor::ExpandShapeOp>>(*ctx);
-    CollapseShapeOp::attachInterface<
-        ReifyExpandOrCollapseShapeOp<tensor::CollapseShapeOp>>(*ctx);
+    ExpandShapeOp::attachInterface<ReifyExpandShapeOp>(*ctx);
+    CollapseShapeOp::attachInterface<ReifyCollapseShapeOp>(*ctx);
     PadOp::attachInterface<ReifyPadOp>(*ctx);
   });
 }
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -1732,6 +1732,10 @@ ExpandShapeOp::inferOutputShape(OpBuilder &b, Location loc,
   return *outputShape;
 }
 
+SmallVector<OpFoldResult> ExpandShapeOp::getMixedOutputShape() {
+  return getMixedValues(getStaticOutputShape(), getOutputShape(), getContext());
+}
+
 void ExpandShapeOp::build(OpBuilder &builder, OperationState &result,
                           Type resultType, Value src,
                           ArrayRef<ReassociationIndices> reassociation,

diff --git a/mlir/lib/Dialect/Utils/StaticValueUtils.cpp b/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
@@ -191,7 +191,8 @@ bool isEqualConstantIntOrValueArray(ArrayRef<OpFoldResult> ofrs1,
 /// elements for which ShapedType::isDynamic is true, will be replaced by
 /// dynamicValues.
 SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
-                                         ValueRange dynamicValues, Builder &b) {
+                                         ValueRange dynamicValues,
+                                         MLIRContext *context) {
   SmallVector<OpFoldResult> res;
   res.reserve(staticValues.size());
   unsigned numDynamic = 0;
@@ -200,10 +201,15 @@ SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
     int64_t value = staticValues[idx];
     res.push_back(ShapedType::isDynamic(value)
                       ? OpFoldResult{dynamicValues[numDynamic++]}
-                      : OpFoldResult{b.getI64IntegerAttr(staticValues[idx])});
+                      : OpFoldResult{IntegerAttr::get(
+                            IntegerType::get(context, 64), staticValues[idx])});
   }
   return res;
 }
+SmallVector<OpFoldResult> getMixedValues(ArrayRef<int64_t> staticValues,
+                                         ValueRange dynamicValues, Builder &b) {
+  return getMixedValues(staticValues, dynamicValues, b.getContext());
+}
 
 /// Decompose a vector of mixed static or dynamic values into the corresponding
 /// pair of arrays. This is the inverse function of `getMixedValues`.

diff --git a/mlir/lib/Interfaces/InferTypeOpInterface.cpp b/mlir/lib/Interfaces/InferTypeOpInterface.cpp
@@ -48,14 +48,6 @@ mlir::reifyResultShapes(OpBuilder &b, Operation *op,
     assert(shapedType.getRank() ==
                static_cast<int64_t>(reifiedReturnShapes[resultIdx].size()) &&
            "incorrect implementation of ReifyRankedShapedTypeOpInterface");
-    for (int64_t dim = 0; dim < shapedType.getRank(); ++dim) {
-      // reifyResultShapes must return:
-      // * Attribute for static dimensions
-      // * Value for dynamic dimensions
-      assert(shapedType.isDynamicDim(dim) ==
-                 isa<Value>(reifiedReturnShapes[resultIdx][dim]) &&
-             "incorrect implementation of ReifyRankedShapedTypeOpInterface");
-    }
     ++resultIdx;
   }
   // Assert that every shaped value result was reified.

diff --git a/mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir b/mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir
@@ -210,15 +210,12 @@ func.func @dim_reshape_expansion(%arg0 : tensor<6x5x?xf32>, %sz0: index) -> (ind
   %3 = tensor.dim %0, %c4 : tensor<2x3x5x4x?x7xf32>
   return %1, %2, %3 : index, index, index
 }
-//      CHECK: #[[MAP:.+]] = affine_map<()[s0] -> (s0 floordiv 28)>
 //      CHECK: func @dim_reshape_expansion
 // CHECK-SAME:   %[[ARG0:[a-zA-Z0-9_]+]]: tensor<6x5x?xf32>
-//  CHECK-DAG:   %[[C2:.+]] = arith.constant 2 : index
+// CHECK-SAME:   %[[ARG1:.+]]: index
 //  CHECK-DAG:   %[[C3:.+]] = arith.constant 3 : index
 //  CHECK-DAG:   %[[C4:.+]] = arith.constant 4 : index
-//      CHECK:   %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C2]]
-//      CHECK:   %[[D1:.+]] = affine.apply #[[MAP]]()[%[[D0]]]
-//      CHECK:   return %[[C3]], %[[C4]], %[[D1]]
+//      CHECK:   return %[[C3]], %[[C4]], %[[ARG1]]
 
 // -----
 

diff --git a/mlir/test/Dialect/Tensor/fold-empty-op.mlir b/mlir/test/Dialect/Tensor/fold-empty-op.mlir
@@ -10,7 +10,6 @@ module attributes {transform.with_named_sequence} {
   }
 }
 
-// CHECK: #[[$MAP:.+]] = affine_map<()[s0] -> (s0 floordiv 28)>
 // CHECK: #[[$MAP2:.+]] = affine_map<()[s0] -> (s0 * 28)>
 
 func.func @empty_reshape_expansion(%arg0 : index, %sz0: index) -> tensor<2x3x5x4x?x7xf32> {
@@ -19,11 +18,9 @@ func.func @empty_reshape_expansion(%arg0 : index, %sz0: index) -> tensor<2x3x5x4
   return %1 : tensor<2x3x5x4x?x7xf32>
 }
 // CHECK-LABEL: func @empty_reshape_expansion
-// CHECK-SAME:     %[[ARG0:.+]]: index
-// CHECK:        %[[OLD_INIT:.+]] = tensor.empty(%{{.*}}) : tensor<6x5x?xf32>
-// CHECK-NEXT:   %[[DIM:.*]] = tensor.dim %[[OLD_INIT]]
-// CHECK-NEXT:   %[[D:.+]] = affine.apply #[[$MAP]]()[%[[DIM]]]
-// CHECK-NEXT:   %[[INIT:.+]] = tensor.empty(%[[D]])
+// CHECK-SAME:     %[[ARG0:[a-zA-Z0-9]+]]: index,
+// CHECK-SAME:     %[[ARG1:[a-zA-Z0-9]+]]: index
+// CHECK-NEXT:   %[[INIT:.+]] = tensor.empty(%[[ARG1]])
 // CHECK-NEXT:   return %[[INIT]]
 
 func.func @empty_reshape_collapse(%arg0 : index) -> tensor<6x5x?xf32> {