diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -186,26 +186,34 @@
   if (!sliceOp)
     return failure(hasDynamicShape);
 
+  // Compute the dropped dimensions if `sliceOp` is ranke-reducing.
+  llvm::SmallDenseSet<unsigned> droppedDims = sliceOp.getDroppedDims();
+
   // Upper bound the `sliceOp` sizes to obtain a static bounding box.
   SmallVector<int64_t> staticSizes;
-  staticSizes.reserve(opToPad.getRank(opOperand));
+  staticSizes.reserve(shape.size());
   auto shapedOp = cast<OffsetSizeAndStrideOpInterface>(sliceOp.getOperation());
-  for (auto size : shapedOp.getMixedSizes()) {
+  for (auto en : enumerate(shapedOp.getMixedSizes())) {
+    // Skip dropped dimensions.
+    if (droppedDims.contains(en.index()))
+      continue;
     // If the size is an attribute add it directly to `staticSizes`.
-    if (size.is<Attribute>()) {
+    if (en.value().is<Attribute>()) {
       staticSizes.push_back(
-          size.get<Attribute>().dyn_cast<IntegerAttr>().getInt());
+          en.value().get<Attribute>().dyn_cast<IntegerAttr>().getInt());
       continue;
     }
     // Otherwise, try to compute a constant upper bound for the size value.
     FailureOr<int64_t> upperBound =
-        getConstantUpperBoundForIndex(size.get<Value>());
+        getConstantUpperBoundForIndex(en.value().get<Value>());
     if (failed(upperBound)) {
       LLVM_DEBUG(DBGS() << "No constant bounding box can be found for padding");
       return failure();
     }
     staticSizes.push_back(upperBound.getValue());
   }
+  assert(staticSizes.size() == shape.size() &&
+         "expect the dynamic and static ranks to match");
 
   // Pad the operand to the bounding box defined by `staticSizes`.
   auto staticTensorType = RankedTensorType::get(
diff --git a/mlir/test/Dialect/Linalg/pad.mlir b/mlir/test/Dialect/Linalg/pad.mlir
--- a/mlir/test/Dialect/Linalg/pad.mlir
+++ b/mlir/test/Dialect/Linalg/pad.mlir
@@ -426,3 +426,24 @@
   %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, %0] [1, 1] : tensor<4x?xf32> into tensor<24x25xf32>
   return %5 : tensor<24x25xf32>
 }
+
+// -----
+
+#map0 = affine_map<()[s0] -> (64, s0)>
+
+//      FILL:  rank_reducing
+// FILL-SAME:    %[[ARG0:[0-9a-zA-Z]*]]: tensor<1x64x1x64xf32>
+func @rank_reducing(%arg0: tensor<1x64x1x64xf32>,
+                    %iv0 : index) -> tensor<1x?x?xf32> {
+  %cst = arith.constant 0.0 : f32
+  %size = affine.min #map0()[%iv0]
+  %0 = tensor.extract_slice %arg0[0, 0, 0, 0] [1, %size, 1, %size] [1, 1, 1, 1] : tensor<1x64x1x64xf32> to tensor<1x?x?xf32>
+
+  // Check the fill is padded despite the rank-reducing slice operation.
+  //      FILL:  %[[T0:.*]] = linalg.pad_tensor
+  //      FILL:  %[[T1:.*]] = linalg.fill(%{{.*}}, %[[T0]])
+  // FILL-SAME:    tensor<1x64x64xf32>
+  //      FILL:  = tensor.extract_slice %[[T1]]
+  %1 = linalg.fill(%cst, %0) : f32, tensor<1x?x?xf32> -> tensor<1x?x?xf32>
+  return %1 : tensor<1x?x?xf32>
+}