[mlir] Disallow tensor types in VectorToSCF

[springerm]

Summary

Support for tensor types can be added if needed.

mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp

 
 template <typename OpTy>
 LogicalResult checkPrepareXferOp(OpTy xferOp,
                                  VectorTransferToSCFOptions options) {
   if (xferOp->hasAttr(kPassLabel))
     return failure();
   if (xferOp.getVectorType().getRank() <= options.targetRank)
     return failure();
+  if (xferOp.getShapedType().template isa<RankedTensorType>())
+    return failure();
   return success();
 }
 
 /// Prepare a TransferReadOp for progressive lowering.
 ///
 /// 1. Allocate a temporary buffer.
 /// 2. Label the TransferReadOp, marking it eligible for progressive lowering.
 /// 3. Store the result of the TransferReadOp into the temporary buffer.
struct UnrollTransferReadConversion
   }
 
   /// Rewrite the op: Unpack one dimension. Can handle masks, out-of-bounds
   /// accesses, and broadcasts and transposes in permutation maps.
   LogicalResult matchAndRewrite(TransferReadOp xferOp,
                                 PatternRewriter &rewriter) const override {
     if (xferOp.getVectorType().getRank() <= options.targetRank)
       return failure();
+    if (xferOp.getShapedType().template isa<RankedTensorType>())
+      return failure();
 
     auto insertOp = getInsertOp(xferOp);
     auto vec = getResultVector(xferOp, rewriter);
     auto vecType = vec.getType().dyn_cast<VectorType>();
     auto xferVecType = xferOp.getVectorType();
     auto newXferVecType = VectorType::get(xferVecType.getShape().drop_front(),
                                           xferVecType.getElementType());
     int64_t dimSize = xferVecType.getShape()[0];
struct UnrollTransferWriteConversion
   }
 
   /// Rewrite the op: Unpack one dimension. Can handle masks, out-of-bounds
   /// accesses, and broadcasts and transposes in permutation maps.
   LogicalResult matchAndRewrite(TransferWriteOp xferOp,
                                 PatternRewriter &rewriter) const override {
     if (xferOp.getVectorType().getRank() <= options.targetRank)
       return failure();
+    if (xferOp.getShapedType().template isa<RankedTensorType>())
+      return failure();
 
     auto vec = getDataVector(xferOp);
     auto xferVecType = xferOp.getVectorType();
     int64_t dimSize = xferVecType.getShape()[0];
 
     // Generate fully unrolled loop of transfer ops.
     Location loc = xferOp.getLoc();
     for (int64_t i = 0; i < dimSize; ++i) {