diff --git a/mlir/lib/Dialect/StandardOps/IR/Ops.cpp b/mlir/lib/Dialect/StandardOps/IR/Ops.cpp
--- a/mlir/lib/Dialect/StandardOps/IR/Ops.cpp
+++ b/mlir/lib/Dialect/StandardOps/IR/Ops.cpp
@@ -1484,6 +1484,25 @@
     return getResult();
   }
 
+  // Fold dim to the operand of dynamic_tensor_from_elements.
+  if (auto fromElements =
+          dyn_cast_or_null<DynamicTensorFromElementsOp>(definingOp)) {
+    auto resultType =
+        fromElements.getResult().getType().cast<RankedTensorType>();
+    // The case where the type encodes the size of the dimension is handled
+    // above.
+    assert(resultType.getShape()[index.getInt()] ==
+           RankedTensorType::kDynamicSize);
+
+    // Find the operand of the fromElements that corresponds to this index.
+    auto dynExtents = fromElements.dynamicExtents().begin();
+    for (auto dim : resultType.getShape().take_front(index.getInt()))
+      if (dim == RankedTensorType::kDynamicSize)
+        dynExtents++;
+
+    return Value{*dynExtents};
+  }
+
   // Fold dim to the size argument for an `AllocOp`, `ViewOp`, or `SubViewOp`.
   auto memrefType = argTy.dyn_cast<MemRefType>();
   if (!memrefType)
diff --git a/mlir/test/Dialect/Standard/canonicalize.mlir b/mlir/test/Dialect/Standard/canonicalize.mlir
--- a/mlir/test/Dialect/Standard/canonicalize.mlir
+++ b/mlir/test/Dialect/Standard/canonicalize.mlir
@@ -5,9 +5,9 @@
 // CHECK-SAME:                                          %[[TENSOR:.*]]: tensor<?xf32>) -> tensor<?xf32> {
 // CHECK:           return %[[TENSOR]]
 func @tensor_load_of_tensor_to_memref(%arg0: tensor<?xf32>) -> tensor<?xf32> {
-    %0 = tensor_to_memref %arg0 : memref<?xf32>
-    %1 = tensor_load %0 : memref<?xf32>
-    return %1 : tensor<?xf32>
+  %0 = tensor_to_memref %arg0 : memref<?xf32>
+  %1 = tensor_load %0 : memref<?xf32>
+  return %1 : tensor<?xf32>
 }
 
 // Test case: Basic folding of tensor_to_memref(tensor_load(m)) -> m
@@ -15,9 +15,9 @@
 // CHECK-SAME:                                          %[[MEMREF:.*]]: memref<?xf32>) -> memref<?xf32> {
 // CHECK:           return %[[MEMREF]]
 func @tensor_to_memref_of_tensor_load(%arg0: memref<?xf32>) -> memref<?xf32> {
-    %0 = tensor_load %arg0 : memref<?xf32>
-    %1 = tensor_to_memref %0 : memref<?xf32>
-    return %1 : memref<?xf32>
+  %0 = tensor_load %arg0 : memref<?xf32>
+  %1 = tensor_to_memref %0 : memref<?xf32>
+  return %1 : memref<?xf32>
 }
 
 // Test case: If the memrefs are not the same type, don't fold them.
@@ -27,9 +27,9 @@
 // CHECK:           %[[MEMREF_ADDRSPACE7:.*]] = tensor_to_memref %[[TENSOR]] : memref<?xf32, 7>
 // CHECK:           return %[[MEMREF_ADDRSPACE7]]
 func @no_fold_tensor_to_memref_of_tensor_load(%arg0: memref<?xf32, 2>) -> memref<?xf32, 7> {
-    %0 = tensor_load %arg0 : memref<?xf32, 2>
-    %1 = tensor_to_memref %0 : memref<?xf32, 7>
-    return %1 : memref<?xf32, 7>
+  %0 = tensor_load %arg0 : memref<?xf32, 2>
+  %1 = tensor_to_memref %0 : memref<?xf32, 7>
+  return %1 : memref<?xf32, 7>
 }
 
 // Test case: Basic folding of dim(tensor_load(m)) -> dim(m).
@@ -39,8 +39,23 @@
 //       CHECK:   %[[D:.*]] = dim %[[MEMREF]], %[[C0]]
 //       CHECK:   return %[[D]] : index
 func @dim_of_tensor_load(%arg0: memref<?xf32>) -> index {
-    %c0 = constant 0 : index
-    %0 = tensor_load %arg0 : memref<?xf32>
-    %1 = dim %0, %c0 : tensor<?xf32>
-    return %1 : index
+  %c0 = constant 0 : index
+  %0 = tensor_load %arg0 : memref<?xf32>
+  %1 = dim %0, %c0 : tensor<?xf32>
+  return %1 : index
+}
+
+// Test case: Folding of dim(dynamic_tensor_from_elements %idx) -> %idx
+// CHECK-LABEL: func @dim_of_dynamic_tensor_from_elements(
+//  CHECK-SAME:     %[[IDX0:[0-9a-z]+]]: index, %[[IDX1:[0-9a-z]+]]: index
+//   CHECK-NOT:   dim
+//       CHECK:   return %[[IDX1]] : index
+func @dim_of_dynamic_tensor_from_elements(%arg0: index, %arg1: index) -> index {
+  %c3 = constant 3 : index
+  %0 = dynamic_tensor_from_elements %arg0, %arg1 {
+  ^bb0(%arg2: index, %arg3: index, %arg4: index, %arg5: index, %arg6: index):
+    yield %c3 : index
+  } : tensor<2x?x4x?x5xindex>
+  %1 = dim %0, %c3 : tensor<2x?x4x?x5xindex>
+  return %1 : index
 }