[mlir] Convert expand_shape to more static form (#112265)

Add pattern that converts a `tensor.expand_shape` op to a more static
form.

This matches the pattern: `tensor.cast` -> `tensor.expand_shape` if it
has a foldable `tensor.cast` and some constant foldable `output_shape`
operands for the `tensor.expand_shape`. This makes the
`tensor.expand_shape` more static, as well as allowing the static
information to be propagated further down in the program.

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

@@ -24,6 +24,7 @@
 #include "mlir/IR/TypeUtilities.h"
 #include "mlir/Interfaces/DestinationStyleOpInterface.h"
 #include "mlir/Interfaces/LoopLikeInterface.h"
+#include "mlir/Support/LLVM.h"
 #include "llvm/ADT/DenseSet.h"
 #include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/SmallBitVector.h"
@@ -1982,6 +1983,86 @@ struct FoldDimOfCollapseShape : public OpRewritePattern<DimOp> {
     return success();
   }
 };
+
+/// Fold/sink a producer `tensor.cast` with a consumer `tensor.expand_shape` by
+/// matching constant output_shape operands of the expand. This makes the
+/// `tensor.expand_shape` more static and creates a consumer cast that can be
+/// propagated further.
+struct ConvertToStaticExpandShape : public OpRewritePattern<ExpandShapeOp> {
+  using OpRewritePattern<ExpandShapeOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(ExpandShapeOp expandOp,
+                                PatternRewriter &rewriter) const override {
+    auto castOp = expandOp.getSrc().getDefiningOp<CastOp>();
+    if (!canFoldIntoConsumerOp(castOp))
+      return failure();
+
+    ArrayRef<int64_t> castSrcShape = castOp.getSource().getType().getShape();
+    SmallVector<ReassociationIndices, 4> reassoc =
+        expandOp.getReassociationIndices();
+
+    SmallVector<int64_t> newOutputShape(expandOp.getResultType().getShape());
+    SmallVector<Value> dynamicOutputShape;
+    auto outputIt = expandOp.getOutputShape().begin();
+
+    for (const auto &[inputDim, innerReassoc] : llvm::enumerate(reassoc)) {
+      for (uint64_t outDim : innerReassoc) {
+        if (!ShapedType::isDynamic(newOutputShape[outDim]))
+          continue;
+
+        // If the cast's src type is dynamic, don't infer any of the
+        // corresponding expanded dimensions. `tensor.expand_shape` requires at
+        // least one of the expanded dimensions to be dynamic if the input is
+        // dynamic.
+        Value val = *outputIt;
+        ++outputIt;
+        if (ShapedType::isDynamic(castSrcShape[inputDim])) {
+          dynamicOutputShape.push_back(val);
+          continue;
+        }
+
+        APInt cst;
+        if (matchPattern(val, m_ConstantInt(&cst))) {
+          newOutputShape[outDim] = cst.getSExtValue();
+        } else {
+          dynamicOutputShape.push_back(val);
+        }
+      }
+    }
+
+    // Couldn't match any values, nothing to change
+    if (expandOp.getOutputShape().size() == dynamicOutputShape.size())
+      return failure();
+
+    // Calculate the input shape from the output
+    SmallVector<int64_t> newInputShape(expandOp.getSrcType().getRank(), 1l);
+    for (auto inDim : llvm::seq<int>(0, newInputShape.size())) {
+      for (auto outDim : reassoc[inDim]) {
+        auto ofr = newOutputShape[outDim];
+        if (ShapedType::isDynamic(ofr)) {
+          newInputShape[inDim] = ShapedType::kDynamic;
+          break;
+        }
+        newInputShape[inDim] *= ofr;
+      }
+    }
+
+    SmallVector<OpFoldResult> outputOfr =
+        getMixedValues(newOutputShape, dynamicOutputShape, rewriter);
+    auto inputType = RankedTensorType::get(
+        newInputShape, expandOp.getSrcType().getElementType());
+    auto outputType = RankedTensorType::get(
+        newOutputShape, expandOp.getSrcType().getElementType());
+    auto inputCast = rewriter.create<CastOp>(expandOp.getLoc(), inputType,
+                                             expandOp.getSrc());
+    auto newExpand = rewriter.create<ExpandShapeOp>(
+        expandOp.getLoc(), outputType, inputCast.getResult(),
+        expandOp.getReassociationIndices(), outputOfr);
+    rewriter.replaceOpWithNewOp<CastOp>(expandOp, expandOp.getType(),
+                                        newExpand.getResult());
+    return success();
+  }
+};
 } // namespace
 
 void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
@@ -1989,7 +2070,7 @@ void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
   results.add<
       ComposeReassociativeReshapeOps<ExpandShapeOp, ReshapeOpKind::kExpand>,
       ComposeExpandOfCollapseOp<ExpandShapeOp, CollapseShapeOp>,
-      FoldReshapeWithConstant<ExpandShapeOp>,
+      ConvertToStaticExpandShape, FoldReshapeWithConstant<ExpandShapeOp>,
       FoldReshapeWithSplat<ExpandShapeOp>,
       FoldReshapeWithFromElements<ExpandShapeOp>, FoldDimOfExpandShape,
       FoldDimOfCollapseShape>(context);

mlir/test/Dialect/Tensor/canonicalize.mlir

@@ -2741,3 +2741,57 @@ func.func @pack_dont_drop_attributes(%arg0: tensor<?x?x?xf16>, %arg1: tensor<128
   %pack = tensor.pack %arg0 padding_value(%cst : f16) outer_dims_perm = [0, 1, 2] inner_dims_pos = [1, 2] inner_tiles = [16, 1] into %arg1 {test_attr} : tensor<?x?x?xf16> -> tensor<128x?x100x16x1xf16>
   return %pack : tensor<128x?x100x16x1xf16>
 }
+
+// -----
+
+func.func @fold_expand_of_cast(%arg0 : tensor<10x10xf32>)
+    -> tensor<10x1x10xf32> {
+  %c1 = arith.constant 1 : index 
+  %c10 = arith.constant 10 : index 
+  %0 = tensor.cast %arg0 : tensor<10x10xf32> to tensor<?x?xf32>
+  %1 = tensor.expand_shape %0 [[0, 1], [2]] output_shape [%c10, %c1, %c10]
+      : tensor<?x?xf32> into tensor<?x?x?xf32>
+  %2 = tensor.cast %1 : tensor<?x?x?xf32> to tensor<10x1x10xf32>
+  return %2 : tensor<10x1x10xf32>
+}
+// CHECK-LABEL:  func.func @fold_expand_of_cast
+//       CHECK:   %[[RES:.+]] = tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2]] output_shape [10, 1, 10]
+//       CHECK:   return %[[RES]]
+
+// -----
+
+func.func @sink_expand_of_cast(%arg0 : tensor<?x10xf32>)
+    -> tensor<?x?x?xf32> {
+  %c1 = arith.constant 1 : index
+  %c10 = arith.constant 10 : index
+  %0 = tensor.cast %arg0 : tensor<?x10xf32> to tensor<?x?xf32>
+  %1 = tensor.expand_shape %0 [[0, 1], [2]] output_shape [%c10, %c1, %c10]
+      : tensor<?x?xf32> into tensor<?x?x?xf32>
+  return %1 : tensor<?x?x?xf32>
+}
+// CHECK-LABEL:  func.func @sink_expand_of_cast
+//   CHECK-DAG:   %[[C10:.*]] = arith.constant 10
+//   CHECK-DAG:   %[[C1:.*]] = arith.constant 1
+//       CHECK:   %[[EXPAND:.+]] = tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2]] 
+//  CHECK-SAME:     output_shape [%[[C10]], %[[C1]], 10]
+//       CHECK:   %[[RES:.+]] = tensor.cast %[[EXPAND]]
+//       CHECK:   return %[[RES]]
+
+// -----
+
+func.func @partial_sink_expand_of_cast(%arg0 : tensor<10x10xf32>, %arg1 : index, %arg2 : index)
+    -> tensor<?x?x?xf32> {
+  %c10 = arith.constant 10 : index
+  %0 = tensor.cast %arg0 : tensor<10x10xf32> to tensor<?x?xf32>
+  %1 = tensor.expand_shape %0 [[0, 1], [2]] output_shape [%arg1, %arg2, %c10]
+      : tensor<?x?xf32> into tensor<?x?x?xf32>
+  return %1 : tensor<?x?x?xf32>
+}
+// CHECK-LABEL:  func.func @partial_sink_expand_of_cast
+//       CHECK:   %[[CAST:.+]] = tensor.cast
+//  CHECK-SAME:     tensor<10x10xf32> to tensor<?x10xf32>
+//       CHECK:   %[[EXPAND:.+]] = tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2]] 
+//  CHECK-SAME:     output_shape [%{{.*}}, %{{.*}}, 10]
+//       CHECK:   %[[RES:.+]] = tensor.cast %[[EXPAND]]
+//  CHECK-SAME:     tensor<?x?x10xf32> to tensor<?x?x?xf32>
+//       CHECK:   return %[[RES]]