diff --git a/js/web/lib/onnxjs/backends/webgpu/op-resolve-rules.ts b/js/web/lib/onnxjs/backends/webgpu/op-resolve-rules.ts
index 1a04f780be643..6fce8e887ee1e 100644
--- a/js/web/lib/onnxjs/backends/webgpu/op-resolve-rules.ts
+++ b/js/web/lib/onnxjs/backends/webgpu/op-resolve-rules.ts
@@ -11,6 +11,7 @@ import {gemm, parseGemmAttributesV11, parseGemmAttributesV7} from './ops/gemm';
 import {matMul, parseMatMulAttributes} from './ops/matmul';
 import {averagePool, globalAveragePool, globalMaxPool, maxPool, parseAveragePoolAttributes, parseGlobalAveragePoolAttributes, parseMaxPoolAttributes} from './ops/pool';
 import {reshape} from './ops/reshape';
+import {shape} from './ops/shape';
 import {parseSliceAttributes, slice, sliceV10} from './ops/slice';
 import * as unaryOps from './ops/unary-op';
 import {parseUnsqueezeAttributes, unsqueeze, unsqueezeV13} from './ops/unsqueeze';
@@ -63,8 +64,7 @@ export const WEBGPU_OP_RESOLVE_RULES: readonly OpSet.ResolveRule[] = [
   ['Relu', '', '6+', unaryOps.relu], ['Reshape', '', '5+', reshape],
   // ['Resize', '', '10', resize, parseResizeAttributesV10],
   // ['Resize', '', '11+', resize, parseResizeAttributesV11],
-  // ['Shape', '', '1+', shape],
-  ['Sigmoid', '', '6+', unaryOps.sigmoid], ['Sin', '', '7+', unaryOps.sin],
+  ['Shape', '', '1+', shape], ['Sigmoid', '', '6+', unaryOps.sigmoid], ['Sin', '', '7+', unaryOps.sin],
   ['Slice', '', '10+', sliceV10],  // TODO: support 'steps' for Slice-10
   ['Slice', '', '1-9', slice, parseSliceAttributes],
   // // The "semantic" meaning of axis has changed in opset-13.
diff --git a/js/web/lib/onnxjs/backends/webgpu/ops/reshape.ts b/js/web/lib/onnxjs/backends/webgpu/ops/reshape.ts
index 0efb46960c2c9..323e80bdb596a 100644
--- a/js/web/lib/onnxjs/backends/webgpu/ops/reshape.ts
+++ b/js/web/lib/onnxjs/backends/webgpu/ops/reshape.ts
@@ -5,7 +5,18 @@ import {Tensor} from '../../../tensor';
 import {ShapeUtil} from '../../../util';
 import {WebGpuInferenceHandler} from '../inference-handler';
 
-export const reshape = (handler: WebGpuInferenceHandler, inputs: Tensor[]): Tensor[] => {
-  const reshapedDims = ShapeUtil.calculateReshapedDims(inputs[0].dims, inputs[1].integerData);
+export const reshape = async(handler: WebGpuInferenceHandler, inputs: Tensor[]): Promise<Tensor[]> => {
+  validateInputs(inputs);
+  const shape = await inputs[1].getData();
+  const reshapedDims = ShapeUtil.calculateReshapedDims(inputs[0].dims, shape as Int32Array);
   return [handler.reshape(inputs[0], reshapedDims)];
 };
+
+const validateInputs = (inputs: Tensor[]): void => {
+  if (!inputs || inputs.length !== 2) {
+    throw new Error('Reshape requires 2 inputs.');
+  }
+  if (inputs[1].type !== 'int32') {
+    throw new Error('Invalid input type.');
+  }
+};
diff --git a/js/web/lib/onnxjs/backends/webgpu/ops/shape.ts b/js/web/lib/onnxjs/backends/webgpu/ops/shape.ts
new file mode 100644
index 0000000000000..94ba9293c457a
--- /dev/null
+++ b/js/web/lib/onnxjs/backends/webgpu/ops/shape.ts
@@ -0,0 +1,16 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+import {Tensor} from '../../../tensor';
+import {WebGpuInferenceHandler} from '../inference-handler';
+
+export const shape = async(inferenceHandler: WebGpuInferenceHandler, inputs: Tensor[]): Promise<Tensor[]> => {
+  validateInputs(inputs);
+  return [new Tensor([inputs[0].dims.length], 'int32', undefined, undefined, new Int32Array(inputs[0].dims))];
+};
+
+const validateInputs = (inputs: Tensor[]): void => {
+  if (!inputs || inputs.length !== 1) {
+    throw new Error('Shape requires 1 input.');
+  }
+};
diff --git a/js/web/lib/onnxjs/tensor.ts b/js/web/lib/onnxjs/tensor.ts
index bd68a44806a26..db5e599fd68dc 100644
--- a/js/web/lib/onnxjs/tensor.ts
+++ b/js/web/lib/onnxjs/tensor.ts
@@ -131,7 +131,15 @@ export class Tensor {
    */
   async getData(): Promise<TensorData> {
     if (this.cache === undefined) {
-      this.cache = await this.asyncDataProvider!(this.dataId);
+      if (this.asyncDataProvider) {
+        const data = await this.asyncDataProvider(this.dataId);
+        if (data.length !== this.size) {
+          throw new Error('Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.');
+        }
+        this.cache = data;
+      } else {
+        return this.data;
+      }
     }
     return this.cache;
   }