diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -9,6 +9,7 @@
 // This file implements the linalg dialect Vectorization transformations.
 //
 //===----------------------------------------------------------------------===//
+#include "mlir/Dialect/Affine/Utils.h"
 
 #include "mlir/Analysis/SliceAnalysis.h"
 #include "mlir/Dialect/Affine/IR/AffineOps.h"
@@ -1084,6 +1085,27 @@
                                              vectorizeNDExtract)))
       return failure();
     LDBG("Vectorize generic by broadcasting to the canonical vector shape\n");
+
+    // Convert affine.apply to arithmetic operatiions before trying to
+    // vectorize
+    SmallVector<Value> affineApplyOpsToDelete;
+    auto oldIP = rewriter.saveInsertionPoint();
+    auto &newIP = linalgOp.getBlock()->front();
+    rewriter.setInsertionPointAfter(&newIP);
+    auto toReplace = linalgOp.getBlock()->getOps<AffineApplyOp>();
+
+    for (auto op : toReplace) {
+      auto expanded = expandAffineExpr(rewriter, op->getLoc(),
+                                       op.getAffineMap().getResult(0),
+                                       op.getOperands(), ValueRange{});
+      op.replaceAllUsesWith(expanded);
+      affineApplyOpsToDelete.push_back(op);
+    }
+    for (auto op : affineApplyOpsToDelete) {
+        rewriter.eraseOp(op.getDefiningOp());
+    }
+    rewriter.restoreInsertionPoint(oldIP);
+
     // TODO: 'vectorize' takes in a 'RewriterBase' which is up-casted to
     // 'OpBuilder' when it is passed over to some methods like
     // 'vectorizeAsLinalgGeneric'. This is highly problematic: if we erase an op
diff --git a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
--- a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
@@ -164,7 +164,7 @@
     return false;
   for (Operation &op : r.front()) {
     if (!(isa<arith::ConstantOp, func::ConstantOp, tensor::ExtractOp,
-              linalg::YieldOp, linalg::IndexOp>(op) ||
+              linalg::YieldOp, linalg::IndexOp, AffineApplyOp>(op) ||
           OpTrait::hasElementwiseMappableTraits(&op)) ||
         llvm::any_of(op.getResultTypes(),
                      [](Type type) { return !type.isIntOrIndexOrFloat(); }))
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -2004,3 +2004,39 @@
 // CHECK-LABEL: @wrong_reduction_detection
 // CHECK:         vector.broadcast
 // CHECK:         vector.transfer_write
+
+// -----
+
+// Regression test: %12 was considered as not vectorizable despite there being
+// a simple arithmetic representation that can be used instead
+
+#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+
+func.func @affine_apply(%arg0: tensor<128x12x32xf32>, %arg3: index) -> tensor<128x12x32xf32> {
+  %0 = tensor.empty() : tensor<128x12x32xf32>
+  %1 = linalg.generic {indexing_maps = [#map0, #map0],
+                       iterator_types = ["parallel", "parallel", "parallel"]}
+    ins(%arg0 : tensor<128x12x32xf32>)
+    outs(%0 : tensor<128x12x32xf32>) {
+  ^bb0(%arg1: f32, %arg2: f32):
+    %2 = linalg.index 2 : index
+    %12 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg3)
+    %3 = arith.index_cast %12 : index to i32
+    %4 = arith.uitofp %3 : i32 to f32
+    %5 = arith.mulf %4, %arg1 : f32
+    linalg.yield %5 : f32
+  } -> tensor<128x12x32xf32>
+  return %1 : tensor<128x12x32xf32>
+}
+
+transform.sequence failures(propagate) {
+ ^bb1(%arg1: !pdl.operation):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1
+   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ }
+
+// CHECK-LABEL: @affine_apply
+// CHECK:         vector.transfer_read
+// CHECK:         vector.broadcast
+// CHECK:         vector.transfer_write