[mlir][linalg][nfc] Clean-up leftover code post llvm#149156 (llvm#150602)

In llvm#149156, I ensured that we
no longer generate spurious `tensor.empty` ops when vectorizing
`linalg.unpack`.

This follow-up removes leftover code that is now redundant but was
missed in the original PR.

Author: banach-space

mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp

@@ -1913,14 +1913,6 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
   readVectorSizes.append(sourceShape.begin() + vectorSizes.size(),
                          sourceShape.end());
 
-  ReifiedRankedShapedTypeDims reifiedRetShapes;
-  LogicalResult status =
-      cast<ReifyRankedShapedTypeOpInterface>(unpackOp.getOperation())
-          .reifyResultShapes(rewriter, reifiedRetShapes);
-  if (status.failed()) {
-    LDBG() << "Unable to reify result shapes of " << unpackOp;
-    return failure();
-  }
   Location loc = unpackOp->getLoc();
 
   auto padValue = arith::ConstantOp::create(

mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir

@@ -941,20 +941,17 @@ module attributes {transform.with_named_sequence} {
 
 // CHECK-LABEL: func @test_vectorize_dynamic_shapes_unpack
 // CHECK-SAME:      %[[ARG_0:.*]]: tensor<?x?xf32>,
+// CHECK-SAME:      %[[ARG_1:.*]]: tensor<?x?x16x2xf32>
 func.func @test_vectorize_dynamic_shapes_unpack(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
 // CHECK: %[[C0:.*]] = arith.constant 0
-// CHECK: %[[DIM:.*]] = tensor.dim %arg0, %[[C0]] : tensor<?x?xf32>
-// CHECK: %[[C1:.*]] = arith.constant 1 : index
-// CHECK: %[[DIM0:.*]] = tensor.dim %arg0, %[[C1]] : tensor<?x?xf32>
-// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
 // CHECK: %[[C01:.*]] = arith.constant 0
 // CHECK: %[[C02:.*]] = arith.constant 0
-// CHECK: %[[DIM4:.*]] = tensor.dim %arg1, %[[C02]] : tensor<?x?x16x2xf32>
-// CHECK: %[[CNST14:.*]] = arith.constant 1
-// CHECK: %[[DIM6:.*]] = tensor.dim %arg1, %[[CNST14]] : tensor<?x?x16x2xf32>
+// CHECK: %[[DIM_0:.*]] = tensor.dim %[[ARG_1]], %[[C02]] : tensor<?x?x16x2xf32>
+// CHECK: %[[C1:.*]] = arith.constant 1
+// CHECK: %[[DIM6:.*]] = tensor.dim %[[ARG_1]], %[[C1]] : tensor<?x?x16x2xf32>
 // CHECK: %[[CNST16:.*]] = arith.constant 16 : index
 // CHECK: %[[CNST2:.*]] = arith.constant 2 : index
-// CHECK: %[[readMsk0:.*]] = vector.create_mask %[[DIM4]], %[[DIM6]], %[[CNST16]], %[[CNST2]] : vector<2x1x16x2xi1>
+// CHECK: %[[readMsk0:.*]] = vector.create_mask %[[DIM_0]], %[[DIM6]], %[[CNST16]], %[[CNST2]] : vector<2x1x16x2xi1>
 // CHECK: %[[read0:.*]] = vector.mask %[[readMsk0]] {{.*}} vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x16x2xf32> } : vector<2x1x16x2xi1> -> vector<2x1x16x2xf32>
 // CHECK: %[[trans0:.*]] = vector.transpose %[[read0]], [0, 3, 1, 2] : vector<2x1x16x2xf32> to vector<2x2x1x16xf32>
 // CHECK: %[[sc0:.*]] = vector.shape_cast %[[trans0]] : vector<2x2x1x16xf32> to vector<4x16xf32>