diff --git a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
@@ -9,6 +9,7 @@
 // This file implements the linalg dialect Fusion on tensors operations pass.
 //
 //===----------------------------------------------------------------------===//
+#include <numeric>
 #include <utility>
 
 #include "PassDetail.h"
@@ -650,6 +651,20 @@
   return RankedTensorType::get(expandedShape, originalType.getElementType());
 }
 
+static RankedTensorType getCollapsedType(RankedTensorType originalType,
+                                         AffineMap indexingMap,
+                                         const ExpansionInfo &expansionInfo) {
+  SmallVector<int64_t> expandedShape;
+  for (AffineExpr expr : indexingMap.getResults()) {
+    unsigned dim = expr.cast<AffineDimExpr>().getPosition();
+    auto dimExpansion = expansionInfo.getExpandedShapeOfDim(dim);
+    expandedShape.push_back(std::accumulate(
+        dimExpansion.begin(), dimExpansion.end(), 1,
+        [](int64_t acc, int64_t i) { return std::max<int64_t>(acc * i, -1); }));
+  }
+  return RankedTensorType::get(expandedShape, originalType.getElementType());
+}
+
 /// Returns the reassociation maps to use in the `tensor.expand_shape`
 /// operation to convert the operands of the original operation to operands of
 /// the expanded operation. The same method is used to compute the
@@ -748,6 +763,13 @@
         return getIndexingMapInExpandedOp(rewriter, m, expansionInfo);
       }));
 
+  // The input type to the generic can be more dynamic than the input of the
+  // reshape. Insert a cast if necessary to make them match.
+  auto maybeCast = [&](Value op, Type t) {
+    return op.getType() != t
+               ? rewriter.create<tensor::CastOp>(genericOp.getLoc(), t, op)
+               : op;
+  };
   SmallVector<Value> expandedOpOperands;
   expandedOpOperands.reserve(genericOp.getNumInputs());
   for (OpOperand *opOperand : genericOp.getInputOperands()) {
@@ -761,13 +783,16 @@
       RankedTensorType expandedOperandType =
           getExpandedType(opOperand->get().getType().cast<RankedTensorType>(),
                           indexingMap, expansionInfo);
+      RankedTensorType collapsedOperandType =
+          getCollapsedType(opOperand->get().getType().cast<RankedTensorType>(),
+                           indexingMap, expansionInfo);
       if (expandedOperandType != opOperand->get().getType()) {
         // Reshape the operand to get the right type.
         SmallVector<ReassociationIndices> reassociation =
             getReassociationForExpansion(indexingMap, expansionInfo);
         expandedOpOperands.push_back(rewriter.create<tensor::ExpandShapeOp>(
-            genericOp.getLoc(), expandedOperandType, opOperand->get(),
-            reassociation));
+            genericOp.getLoc(), expandedOperandType,
+            maybeCast(opOperand->get(), collapsedOperandType), reassociation));
         continue;
       }
     }
@@ -781,12 +806,15 @@
     RankedTensorType expandedOutputType =
         getExpandedType(opOperand->get().getType().cast<RankedTensorType>(),
                         indexingMap, expansionInfo);
+    RankedTensorType collapsedOperandType =
+        getCollapsedType(opOperand->get().getType().cast<RankedTensorType>(),
+                         indexingMap, expansionInfo);
     if (expandedOutputType != opOperand->get().getType()) {
       SmallVector<ReassociationIndices> reassociation =
           getReassociationForExpansion(indexingMap, expansionInfo);
       outputs.push_back(rewriter.create<tensor::ExpandShapeOp>(
-          genericOp.getLoc(), expandedOutputType, opOperand->get(),
-          reassociation));
+          genericOp.getLoc(), expandedOutputType,
+          maybeCast(opOperand->get(), collapsedOperandType), reassociation));
     }
   }
 
diff --git a/mlir/test/Dialect/Linalg/reshape_fusion.mlir b/mlir/test/Dialect/Linalg/reshape_fusion.mlir
--- a/mlir/test/Dialect/Linalg/reshape_fusion.mlir
+++ b/mlir/test/Dialect/Linalg/reshape_fusion.mlir
@@ -507,3 +507,26 @@
 //    FOLDUNITDIM-SAME:     ins(%[[ARG0]], %[[RESHAPE]] : tensor<1x?x1x2x1x4xf32>, tensor<1x?x1x2x1x4xf32>)
 //    FOLDUNITDIM-SAME:     outs(%{{.+}} : tensor<1x?x1x2x1x4xf32>)
 
+// -----
+
+func @result_less_dynamic(%arg0: tensor<1x1xi64>, %arg1: tensor<?xi64>) -> tensor<1xi64> {
+  %0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<1x1xi64> into tensor<1xi64>
+  %1 = linalg.init_tensor [1] : tensor<1xi64>
+  %2 = linalg.generic
+    {indexing_maps = [affine_map<(d0) -> (d0)>,
+                      affine_map<(d0) -> (d0)>,
+                      affine_map<(d0) -> (d0)>],
+     iterator_types = ["parallel"]}
+    ins(%0, %arg1 : tensor<1xi64>, tensor<?xi64>)
+    outs(%1 : tensor<1xi64>) {
+  ^bb0(%arg4: i64, %arg5: i64, %arg6: i64):  // no predecessors
+    %3 = arith.addi %arg4, %arg5 : i64
+    linalg.yield %3 : i64
+  } -> tensor<1xi64>
+  return %2 : tensor<1xi64>
+}
+
+//      CHECK: func @result_less_dynamic
+// CHECK-SAME:     %[[ARG1:[^ ]+]]: tensor<?xi64>
+//      CHECK:   %[[CAST:.+]] = tensor.cast %[[ARG1]] : tensor<?xi64> to tensor<1xi64>
+//      CHECK:   tensor.expand_shape %[[CAST]] {{\[}}[0, 1]] : tensor<1xi64> into tensor<1x1xi64>