[mlir] Add canonicalization for tensor_cast + tensor_to_memref

[ThomasRaoux]

Summary

This helps bufferization passes by removing tensor_cast operations.

mlir/include/mlir/Dialect/StandardOps/IR/Ops.td

def TensorToMemrefOp : Std_Op<"tensor_to_memref",
   let arguments = (ins AnyTensor:$tensor);
   let results = (outs AnyRankedOrUnrankedMemRef:$memref);
   // This op is fully verified by traits.
   let verifier = ?;
 
   let assemblyFormat = "$tensor attr-dict `:` type($memref)";
 
   let hasFolder = 1;
+  let hasCanonicalizer = 1;
 }
 
 //===----------------------------------------------------------------------===//
 // TransposeOp
 //===----------------------------------------------------------------------===//
 
 def TransposeOp : Std_Op<"transpose", [NoSideEffect]>,
     Arguments<(ins AnyStridedMemRef:$in, AffineMapAttr:$permutation)>,

mlir/lib/Dialect/StandardOps/IR/Ops.cpp

 
 OpFoldResult TensorToMemrefOp::fold(ArrayRef<Attribute>) {
   if (auto tensorLoad = tensor().getDefiningOp<TensorLoadOp>())
     if (tensorLoad.memref().getType() == getType())
       return tensorLoad.memref();
   return {};
 }
 
+namespace {
+/// Replace tensor_cast + tensor_to_memref by tensor_to_memref + memref_cast.
+struct TensorCastToMemref : public OpRewritePattern<TensorToMemrefOp> {
+  using OpRewritePattern<TensorToMemrefOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(TensorToMemrefOp tensorToMemRef,
+                                PatternRewriter &rewriter) const final {
+    auto tensorCastOperand =
+        tensorToMemRef.getOperand().getDefiningOp<tensor::CastOp>();
+    if (!tensorCastOperand)
+      return failure();
+    auto srcTensorType =
+        tensorCastOperand.getOperand().getType().dyn_cast<RankedTensorType>();
+    if (!srcTensorType)
+      return failure();
+    auto memrefType = MemRefType::get(srcTensorType.getShape(),
+                                      srcTensorType.getElementType());
+    Value memref = rewriter.create<TensorToMemrefOp>(
+        tensorToMemRef.getLoc(), memrefType, tensorCastOperand.getOperand());
+    rewriter.replaceOpWithNewOp<MemRefCastOp>(tensorToMemRef,
+                                              tensorToMemRef.getType(), memref);
+    return success();
+  }
+};
+} // namespace
+
+void TensorToMemrefOp::getCanonicalizationPatterns(
+    OwningRewritePatternList &results, MLIRContext *context) {
+  results.insert<TensorCastToMemref>(context);
+}
+
 //===----------------------------------------------------------------------===//
 // TransposeOp
 //===----------------------------------------------------------------------===//
 
 /// Build a strided memref type by applying `permutationMap` tp `memRefType`.
 static MemRefType inferTransposeResultType(MemRefType memRefType,
                                            AffineMap permutationMap) {
   auto rank = memRefType.getRank();

mlir/test/Dialect/Standard/canonicalize.mlir

 func @fold_dim_of_tensor.cast(%arg0 : tensor<4x?xf32>) -> (index, index) {
   %c0 = constant 0 : index
   %c1 = constant 1 : index
   %0 = tensor.cast %arg0 : tensor<4x?xf32> to tensor<?x?xf32>
   %1 = dim %0, %c0 : tensor<?x?xf32>
   %2 = dim %0, %c1 : tensor<?x?xf32>
   return %1, %2: index, index
 }
+
+// CHECK-LABEL: func @tensor_cast_to_memref
+//  CHECK-SAME:   %[[ARG0:.+]]: tensor<4x6x16x32xi8>
+//       CHECK:   %[[M:.+]] = tensor_to_memref %[[ARG0]] : memref<4x6x16x32xi8>
+//       CHECK:   %[[M1:.+]] = memref_cast %[[M]] : memref<4x6x16x32xi8> to memref<?x?x16x32xi8>
+//       CHECK:   return %[[M1]] : memref<?x?x16x32xi8>
+func @tensor_cast_to_memref(%arg0 : tensor<4x6x16x32xi8>) ->
+  memref<?x?x16x32xi8> {
+  %0 = tensor.cast %arg0 : tensor<4x6x16x32xi8> to tensor<?x?x16x32xi8>
+  %1 = tensor_to_memref %0 : memref<?x?x16x32xi8>
+  return %1 : memref<?x?x16x32xi8>
+}