Address reviewer comments

Author: christopherbate

mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td

@@ -402,7 +402,7 @@ def Bufferization_ToTensorOp : Bufferization_Op<"to_tensor", [
 
     ```mlir
     // Produces a value of tensor<4x?xf32> type.
-    %t = bufferization.to_tensor %m : memref<4x?xf32, #layout, 0>
+    %t = bufferization.to_tensor %m : memref<4x?xf32, #layout, 0> -> tensor<4x?xf32>
     ```
 
     If the `writable` unit attribute is set, the produced tensor is considered
@@ -425,7 +425,7 @@ def Bufferization_ToTensorOp : Bufferization_Op<"to_tensor", [
     Example:
 
     ```
-    %t = bufferization.to_tensor %m restrict writable : memref<4xf32>
+    %t = bufferization.to_tensor %m restrict writable : memref<4xf32> -> tensor<4xf32>
 
     // %t is writable, so the tensor.insert may bufferize in-place in the
     // absence of other conflicts.
@@ -498,18 +498,16 @@ def Bufferization_ToMemrefOp : Bufferization_Op<"to_memref", [
     SameOperandsAndResultShape,
     SameOperandsAndResultElementType,
     Pure,
-    TypesMatchWith<"type of 'tensor' is the tensor equivalent of 'memref'",
-                   "memref", "tensor",
-                   "memref::getTensorTypeFromMemRefType($_self)",
-                   "bufferization::detail::tensorTypesMatchUpToEncoding">
+    AllShapesMatch<["memref", "tensor"]>,
+    AllElementTypesMatch<["memref", "tensor"]>
   ]> {
   let summary = "cast a tensor to memref";
   let description = [{
     An operation that returns the future buffer of a `tensor`.
 
     ```mlir
     // Result type is memref<4x?xf32, #layout, 0>
-    %m = bufferization.to_memref %t : memref<4x?xf32, #layout, 0>
+    %m = bufferization.to_memref %t : tensor<4x?xf32> -> memref<4x?xf32, #layout, 0>
     ```
 
     This operation is a specialized variant of the built-in

mlir/lib/Dialect/Bufferization/Transforms/Bufferize.cpp

@@ -67,10 +67,6 @@ BufferizeTypeConverter::BufferizeTypeConverter() {
                               ValueRange inputs, Location loc) -> Value {
     assert(inputs.size() == 1 && "expected exactly one input");
 
-    // Unranked to ranked casts must be explicit.
-    if (auto inputType = dyn_cast<UnrankedMemRefType>(inputs[0].getType()))
-      return nullptr;
-
     if (auto inputType = dyn_cast<MemRefType>(inputs[0].getType())) {
       // MemRef to MemRef cast.
       assert(inputType != type && "expected different types");

mlir/test/Dialect/Bufferization/Transforms/finalizing-bufferize.mlir

@@ -83,13 +83,3 @@ func.func @no_layout_to_dyn_layout_cast(%m: memref<?xf32>) -> memref<?xf32, stri
   // expected-note @below{{see existing live user here}}
   return %1 : memref<?xf32, strided<[1], offset: ?>>
 }
-
-// -----
-
-func.func @illegal_unranked_to_rank(%m: memref<*xf32>) -> memref<?xf32> {
-
-  %0 = bufferization.to_tensor %m : memref<*xf32> -> tensor<*xf32>
-  // expected-error @+1 {{failed to legalize unresolved materialization from ('memref<*xf32>') to 'memref<?xf32>' that remained live after conversion}}
-  %1 = bufferization.to_memref %0 : tensor<*xf32> -> memref<?xf32>
-  return %1 : memref<?xf32>
-}