Address Yulong's comments

js/web/lib/wasm/jsep/backend-webgpu.ts

@@ -95,8 +95,8 @@ const getProgramInfoUniqueKey =
     };
 
 class AdapterInfoImpl implements AdapterInfo {
-  readonly architecture: string;
-  readonly vendor: string;
+  readonly architecture?: string;
+  readonly vendor?: string;
 
   constructor(adapterInfo: GPUAdapterInfo) {
     if (adapterInfo) {

js/web/lib/wasm/jsep/webgpu/ops/conv.ts

@@ -149,11 +149,10 @@ const conv2d = (context: ComputeContext, inputs: readonly TensorView[], attribut
   const isChannelsLast = attributes.format === 'NHWC';
   if (attributes.group !== 1) {
     // One CI with NVIDIA GPU (ampere architecutre) fails with below 2 cases, but we couldn't repro them with any other
-    // GPUs, including NVIDIA ones.
+    // GPUs, including NVIDIA ones. So just disable vectorize on NVIDIA ampere to ensure always correct outputs.
     // [webgpu]Conv - conv - vectorize group - B
     // [webgpu]Conv - conv - vectorize group - D
-    // Disable vectorize on NVIDIA ampere to make bots happy.
-    const enableGroupedConvVectorize = context.adapterInfo.isArchitecture('ampere') ? false : true;
+    const enableGroupedConvVectorize = !context.adapterInfo.isArchitecture('ampere');
     if (enableGroupedConvVectorize && isChannelsLast && inputs[1].dims[0] === attributes.group &&
         inputs[1].dims[1] === 1 && attributes.dilations[0] === 1 && attributes.dilations[1] === 1) {
       const outputShape = calculateOutputShape(

js/web/lib/wasm/jsep/webgpu/types.ts

@@ -18,9 +18,6 @@ export type GpuDataId = number;
 export type GpuArchitecture = 'ampere';
 export type GpuVendor = 'amd'|'intel'|'nvidia';
 export interface AdapterInfo {
-  readonly architecture: string;
-  readonly vendor: string;
-
   isArchitecture: (architecture: GpuArchitecture) => boolean;
   isVendor: (vendor: GpuVendor) => boolean;
 }