diff --git a/iree/compiler/Dialect/Flow/Transforms/DispatchLinalgOnTensors.cpp b/iree/compiler/Dialect/Flow/Transforms/DispatchLinalgOnTensors.cpp
index a5e6f48..30c7932 100644
--- a/iree/compiler/Dialect/Flow/Transforms/DispatchLinalgOnTensors.cpp
+++ b/iree/compiler/Dialect/Flow/Transforms/DispatchLinalgOnTensors.cpp
@@ -717,6 +717,9 @@
     auto linalgOp = dyn_cast<linalg::LinalgOp>(op);
     if (!linalgOp || !linalgOp.hasTensorSemantics()) return failure();
     if (!hasRootOpAttribute(op)) return failure();
+    if (op->getParentOfType<IREE::Flow::DispatchWorkgroupsOp>()) {
+      return failure();
+    }
 
     // TODO(ravishankarm): It is getting strange to track when to apply this
     // pattern and when not to. Need to revisit this, with dynamic shape cases
@@ -798,6 +801,9 @@
                                 PatternRewriter &rewriter) const override {
     if (!hasRootOpAttribute(tilableOp)) return failure();
     if (hasOnlyDimUses(tilableOp)) return failure();
+    if (tilableOp->getParentOfType<IREE::Flow::DispatchWorkgroupsOp>()) {
+      return failure();
+    }
 
     SmallVector<StringRef> iteratorTypes = tilableOp.getLoopIteratorTypes();
     SmallVector<Range> loopRanges = tilableOp.getLoopBounds(rewriter);
@@ -1079,7 +1085,7 @@
     // order here.
     for (Operation &op : llvm::reverse(block)) {
       // Start with a root operation and fuse its producers.
-      if (!isRootOp(&op)) continue;
+      if (hasFusionGroupsAttribute(&op) || !isRootOp(&op)) continue;
       unsigned newGroup = numRootOps++;
       setRootAttribute(context, &op, newGroup);
 
diff --git a/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir b/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir
index 0dc7904..2d35b8a 100644
--- a/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir
+++ b/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir
@@ -1073,3 +1073,26 @@
 //       CHECK:         scf.for %[[X:.+]] =
 //       CHECK:           %[[POOL:.+]] = linalg.pooling_nhwc_sum
 //       CHECK:           flow.dispatch.tensor.store %[[POOL]], %[[OUTPUT]], offsets = [0, %[[Z]], %[[Y]], %[[X]]], sizes = [1, %{{.+}}, %{{.+}}, %{{.+}}]
+
+// -----
+
+func @named_op_outs_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %c0 = constant 0 : index
+  %c1 = constant 1 : index
+  %cst1 = constant -1.0 : f64
+  %cstm1 = constant 1.0 : f64
+  %c12345 = constant 12345 : i32
+  %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
+  %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
+  %init = linalg.init_tensor [%d0, %d1] : tensor<?x?xf32>
+  %fill = linalg.fill_rng_2d ins(%cst1, %cstm1, %c12345 : f64, f64, i32)
+      outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32>
+  %matmul = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
+      outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %matmul : tensor<?x?xf32>
+}
+// CHECK-LABEL: func @named_op_outs_fusion
+//       CHECK:   flow.dispatch.workgroups
+//       CHECK:     %[[FILL:.+]] = linalg.fill_rng_2d
+//       CHECK:     linalg.matmul
+//  CHECK-SAME:       outs(%[[FILL]] : tensor<?x?xf32>)