diff --git a/tfjs-backend-webgpu/src/backend_webgpu.ts b/tfjs-backend-webgpu/src/backend_webgpu.ts
index 1227f74890..ab78ef7777 100644
--- a/tfjs-backend-webgpu/src/backend_webgpu.ts
+++ b/tfjs-backend-webgpu/src/backend_webgpu.ts
@@ -17,7 +17,7 @@
 
 import './flags_webgpu';
 
-import {backend_util, buffer, DataStorage, DataType, engine, env, GPUData, KernelBackend, Rank, RecursiveArray, ShapeMap, Tensor, TensorBuffer, TensorInfo, TimingInfo, TypedArray, util, WebGPUData} from '@tensorflow/tfjs-core';
+import {backend_util, BackendValues, buffer, DataStorage, DataType, engine, env, GPUData, KernelBackend, Rank, RecursiveArray, ShapeMap, Tensor, TensorBuffer, TensorInfo, TimingInfo, TypedArray, util, WebGPUData} from '@tensorflow/tfjs-core';
 
 import {AdapterInfo} from './adapter_info';
 import {BufferManager} from './buffer_manager';
@@ -46,7 +46,7 @@ export type TextureInfo = {
 };
 
 type TensorData = {
-  values: backend_util.BackendValues,
+  values: BackendValues,
   dtype: DataType,
   shape: number[],
   refCount: number,
@@ -290,9 +290,8 @@ export class WebGPUBackend extends KernelBackend {
     }
   }
 
-  override write(
-      values: backend_util.BackendValues, shape: number[],
-      dtype: DataType): DataId {
+  override write(values: BackendValues, shape: number[], dtype: DataType):
+      DataId {
     if (dtype === 'complex64' && values != null) {
       throw new Error(
           `Cannot write to a complex64 dtype. ` +
@@ -304,8 +303,8 @@ export class WebGPUBackend extends KernelBackend {
   }
 
   override move(
-      dataId: DataId, values: backend_util.BackendValues, shape: number[],
-      dtype: DataType, refCount: number): void {
+      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,
+      refCount: number): void {
     if (dtype === 'complex64') {
       throw new Error(
           `Cannot write to a complex64 dtype. ` +
@@ -386,8 +385,8 @@ export class WebGPUBackend extends KernelBackend {
     return values;
   }
 
-  private convertAndCacheOnCPU(dataId: DataId, data: backend_util.TypedArray):
-      backend_util.TypedArray {
+  private convertAndCacheOnCPU(dataId: DataId, data: BackendValues):
+      BackendValues {
     const tensorData = this.tensorMap.get(dataId);
     this.releaseResource(dataId);
     tensorData.values = data;
@@ -396,7 +395,7 @@ export class WebGPUBackend extends KernelBackend {
 
   // TODO: Remove once this is fixed:
   // https://github.com/tensorflow/tfjs/issues/1595
-  override readSync(dataId: object): backend_util.BackendValues {
+  override readSync(dataId: object): BackendValues {
     const tensorData = this.tensorMap.get(dataId);
     const {values} = tensorData;
 
@@ -408,7 +407,7 @@ export class WebGPUBackend extends KernelBackend {
     return values;
   }
 
-  override async read(dataId: object): Promise<backend_util.BackendValues> {
+  override async read(dataId: object): Promise<BackendValues> {
     if (!this.tensorMap.has(dataId)) {
       throw new Error(`Tensor ${dataId} was not registered!`);
     }
@@ -417,15 +416,11 @@ export class WebGPUBackend extends KernelBackend {
     const {values} = tensorData;
 
     if (values != null) {
-      // TODO(xing.xu@intel.com): Merge backend_util.BackendValues and
-      // backend_util.TypedArray.
-      return this.convertAndCacheOnCPU(
-                 dataId, values as backend_util.TypedArray) as
-          backend_util.BackendValues;
+      return this.convertAndCacheOnCPU(dataId, values);
     }
 
     // Download the values from the GPU.
-    let vals: backend_util.BackendValues;
+    let vals: BackendValues;
     if (tensorData.dtype === 'complex64') {
       const ps = await Promise.all([
         this.read(tensorData.complexTensorInfos.real.dataId),
@@ -441,7 +436,7 @@ export class WebGPUBackend extends KernelBackend {
       const data = await this.getBufferData(bufferInfo.buffer, bufferInfo.size);
       vals = util.convertBackendValuesAndArrayBuffer(data, tensorData.dtype);
     }
-    this.convertAndCacheOnCPU(dataId, vals as backend_util.TypedArray);
+    this.convertAndCacheOnCPU(dataId, vals);
     return vals;
   }
 
@@ -604,13 +599,12 @@ export class WebGPUBackend extends KernelBackend {
 
   makeTensorInfo(
       shape: number[], dtype: DataType,
-      values?: backend_util.BackendValues|string[]): TensorInfo {
+      values?: BackendValues|string[]): TensorInfo {
     if (dtype === 'string' && values != null && values.length > 0 &&
         util.isString(values[0])) {
       values = (values as unknown as string[]).map(d => util.encodeString(d));
     }
-    const dataId =
-        this.write(values as backend_util.BackendValues, shape, dtype);
+    const dataId = this.write(values as BackendValues, shape, dtype);
     return {dataId, shape, dtype};
   }