diff --git a/compiler/src/iree/compiler/DispatchCreation/CollapseDimensions.cpp b/compiler/src/iree/compiler/DispatchCreation/CollapseDimensions.cpp
index 61f9266bf6b8..ca807eecb6ac 100644
--- a/compiler/src/iree/compiler/DispatchCreation/CollapseDimensions.cpp
+++ b/compiler/src/iree/compiler/DispatchCreation/CollapseDimensions.cpp
@@ -24,19 +24,17 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tensor/Transforms/Transforms.h"
 #include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
+#include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/Dialect/Utils/StructuredOpsUtils.h"
 #include "mlir/IR/AffineExpr.h"
 #include "mlir/IR/AffineMap.h"
 #include "mlir/IR/Block.h"
 #include "mlir/IR/Builders.h"
-#include "mlir/IR/Iterators.h"
 #include "mlir/IR/MLIRContext.h"
 #include "mlir/IR/Operation.h"
 #include "mlir/IR/PatternMatch.h"
-#include "mlir/IR/Visitors.h"
 #include "mlir/Interfaces/DestinationStyleOpInterface.h"
 #include "mlir/Interfaces/FunctionInterfaces.h"
-#include "mlir/Interfaces/SideEffectInterfaces.h"
 #include "mlir/Interfaces/TilingInterface.h"
 #include "mlir/Support/LogicalResult.h"
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
@@ -777,16 +775,9 @@ hoistTensorReshapesOutOfDispatchRegion(
     auto shapedType = dyn_cast<ShapedType>(origResult.getType());
     assert(shapedType && "result should be shaped type");
 
-    SmallVector<OpFoldResult> outputShape;
     ValueRange dynamicDims = dispatchOp.getResultDynamicDims(index);
-    for (int64_t dim : shapedType.getShape()) {
-      if (ShapedType::isDynamic(dim)) {
-        outputShape.push_back(dynamicDims.front());
-        dynamicDims.drop_front();
-        continue;
-      }
-      outputShape.push_back(rewriter.getIndexAttr(dim));
-    }
+    SmallVector<OpFoldResult> outputShape =
+        mlir::getMixedValues(shapedType.getShape(), dynamicDims, rewriter);
 
     auto newExpandShapeOp = rewriter.create<tensor::ExpandShapeOp>(
         loc, origResult.getType(), returnValue,
@@ -1062,11 +1053,7 @@ void CollapseDimensionsPass::runOnOperation() {
     memref::populateResolveRankedShapedTypeResultDimsPatterns(moveReshapeOps);
     tensor::populateFoldTensorEmptyPatterns(moveReshapeOps);
     SmallVector<Operation *> candidateOps;
-    block.walk([&](Operation *op) {
-      if (isa<tensor::CollapseShapeOp>(op)) {
-        candidateOps.push_back(op);
-      }
-    });
+    block.walk([&](Operation *op) { candidateOps.push_back(op); });
     if (failed(
             applyOpPatternsGreedily(candidateOps, std::move(moveReshapeOps)))) {
       funcOp.emitOpError(
diff --git a/compiler/src/iree/compiler/DispatchCreation/test/collapse_dimensions.mlir b/compiler/src/iree/compiler/DispatchCreation/test/collapse_dimensions.mlir
index 5ae2cb71df1b..377c91b6a054 100644
--- a/compiler/src/iree/compiler/DispatchCreation/test/collapse_dimensions.mlir
+++ b/compiler/src/iree/compiler/DispatchCreation/test/collapse_dimensions.mlir
@@ -753,3 +753,37 @@ util.func public @uncollapsable_consumer_partial(%arg0: tensor<10x20x30x2304xf32
 // CHECK-SAME:         iterator_types = ["parallel", "parallel", "reduction"]
 //     CHECK:        %[[BARRIER:.+]] = util.optimization_barrier %[[RES]]
 //     CHECK:        flow.return %[[RES]]
+
+// -----
+
+util.func @elementwise_dynamic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32>{
+  %cst_0 = arith.constant 0 : index
+  %cst_1 = arith.constant 1 : index
+  %0 = tensor.dim %arg0, %cst_0 : tensor<?x?xf32>
+  %1 = tensor.dim %arg0, %cst_1 : tensor<?x?xf32>
+  %3 = flow.dispatch.region -> (tensor<?x?xf32>{%0, %1}) {
+    %5 = tensor.empty(%0, %1) : tensor<?x?xf32>
+    %cst = arith.constant 1.000000e+02 : f32
+    %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<?x?xf32>) outs(%5 : tensor<?x?xf32>) {
+    ^bb0(%in: f32, %out: f32):
+      %7 = arith.addf %in, %cst : f32
+      linalg.yield %7 : f32
+    } -> tensor<?x?xf32>
+    flow.return %6 : tensor<?x?xf32>
+  }
+  util.return %3 : tensor<?x?xf32>
+}
+// CHECK-LABEL: util.func public @elementwise_dynamic
+//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]+]]
+//  CHECK-SAME:   %[[ARG1:[0-9a-zA-Z]+]]
+//   CHECK-DAG:   %[[CST0:.+]] = arith.constant 0 : index
+//   CHECK-DAG:   %[[CST1:.+]] = arith.constant 1 : index
+//   CHECK-DAG:   %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[CST0]]
+//   CHECK-DAG:   %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[CST1]]
+//       CHECK:   %[[DISPATCH:.+]] = flow.dispatch.region
+//       CHECK:     %[[VAL:.+]] = linalg.generic
+//  CHECK-SAME:      iterator_types = ["parallel"]
+//       CHECK:     flow.return %[[VAL]] : tensor<?xf32>
+//       CHECK:   %[[EXPAND:.+]] = tensor.expand_shape %[[DISPATCH]]
+//  CHECK-SAME:     {{.+}} output_shape [%[[DIM0]], %[[DIM1]]]
+//       CHECK:   util.return %[[EXPAND]] : tensor<?x?xf32>