[JS/Web] Add ConvTranspose implementation using MatMul (#17573)

### Description
Add ConvTranspose implementation using MatMul to increase perf.


### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

js/web/lib/wasm/jsep/webgpu/ops/3rd-party/conv_backprop_mm_webgpu.ts

@@ -0,0 +1,243 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+// sampled from [@tensorflow/tfjs] tfjs-backend-webgpu/src/conv_backprop_mm_webgpu.ts
+//
+// modified to fit the needs of the project
+
+import {LOG_DEBUG} from '../../../log';
+import {TensorView} from '../../../tensor-view';
+import {ShapeUtil} from '../../../util';
+import {GpuDataType, ProgramInfo, ProgramMetadata} from '../../types';
+import {ConvTransposeAttributes} from '../conv-transpose';
+
+import {Activation, activationFnSnippet, biasActivationSnippet, typeSnippet} from './activation_util';
+import {utilFunctions} from './conv_util';
+import {makeMatMulPackedSource, makeMatMulPackedVec4Source} from './matmul_packed_webgpu';
+
+const conv2dTransposeCommonSnippet =
+    (isChannelsLast: boolean, addBias = false, activation?: Activation, hasPreluActivationWeights = false,
+     innerElementSize = 4): string => {
+      const getWSnippet = (innerElementSize: number) => {
+        switch (innerElementSize) {
+          case 1:
+            return 'return W[getIndexFromCoords4D(coord, wShape)];';
+          case 4:
+            return `
+            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
+            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
+            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
+            let v0 = W[getIndexFromCoords4D(coord, wShape)];
+            let v1 = W[getIndexFromCoords4D(coord1, wShape)];
+            let v2 = W[getIndexFromCoords4D(coord2, wShape)];
+            let v3 = W[getIndexFromCoords4D(coord3, wShape)];
+            return vec4<f32>(v0, v1, v2, v3);
+            `;
+          default:
+            throw new Error(`innerElementSize ${innerElementSize} is not supported.`);
+        }
+      };
+      const coordASnippet = isChannelsLast ? `
+      let coord = vec4<i32>(batch, iXR, iXC, xCh);
+      ` :
+                                             `
+      let coord = vec4<i32>(batch, xCh, iXR, iXC);
+      `;
+
+      const coordResSnippet = isChannelsLast ? `
+    let coords = vec4<i32>(
+      batch,
+      row / outWidth,
+      row % outWidth,
+      col);
+    ` :
+                                               `
+    let coords = vec4<i32>(
+      batch,
+      row,
+      col / outWidth,
+      col % outWidth);
+    `;
+
+      const xHeight = isChannelsLast ? 'outBackprop[1]' : 'outBackprop[2]';
+      const xWidth = isChannelsLast ? 'outBackprop[2]' : 'outBackprop[3]';
+      const row = isChannelsLast ? 'row' : 'col';
+      const col = isChannelsLast ? 'col' : 'row';
+
+      const readASnippet = `
+      let inChannels = ${isChannelsLast ? 'outBackprop[3]' : 'outBackprop[1]'};
+      let outWidth = ${isChannelsLast ? 'outShape[2]' : 'outShape[3]'};
+      let outRow = ${row} / outWidth;
+      let outCol = ${row} % outWidth;
+
+      let WRow = ${col} / (filterDims[1] * inChannels);
+      let WCol = ${col} / inChannels % filterDims[1];
+      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);
+      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);
+      if (xR < 0.0 || xR >= f32(${xHeight}) || fract(xR) > 0.0) {
+        return ${typeSnippet(innerElementSize)}(0.0);
+      }
+      if (xC < 0.0 || xC >= f32(${xWidth}) || fract(xC) > 0.0) {
+        return ${typeSnippet(innerElementSize)}(0.0);
+      }
+      let iXR = i32(xR);
+      let iXC = i32(xC);
+      let xCh = ${col} % inChannels;
+      ${coordASnippet}
+      return x[getIndexFromCoords4D(coord, xShape)/${innerElementSize}];`;
+
+      const sampleA = isChannelsLast ? `
+      let col = colIn * ${innerElementSize};
+      if (row < dimAOuter && col < dimInner) {
+        ${readASnippet}
+      }
+      return ${typeSnippet(innerElementSize)}(0.0);` :
+                                       `
+      let col = colIn * ${innerElementSize};
+      if (row < dimInner && col < dimBOuter) {
+        ${readASnippet}
+      }
+      return ${typeSnippet(innerElementSize)}(0.0);`;
+
+      const sampleW = `
+      let col = colIn * ${innerElementSize};
+      let inChannels = ${isChannelsLast ? 'outBackprop[3]' : 'outBackprop[1]'};
+      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);
+      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];
+      if (${
+          isChannelsLast ? 'row < dimInner && col < dimBOuter' :
+                           'row < dimInner && col < dimAOuter'}  && coordX >= 0 && coordY >= 0) {
+        let rowInner = row % inChannels;
+        let coord = vec4<i32>(coordX, coordY, col, rowInner);
+        ${getWSnippet(innerElementSize)}
+      }
+      return ${typeSnippet(innerElementSize)}(0.0);
+      `;
+
+
+      const userCode = `
+  ${activationFnSnippet(activation, hasPreluActivationWeights, innerElementSize === 4, 4)}
+  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${typeSnippet(innerElementSize)} {
+    ${isChannelsLast ? sampleA : sampleW}
+  }
+
+  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${typeSnippet(innerElementSize)} {
+    ${isChannelsLast ? sampleW : sampleA}
+  }
+
+  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${typeSnippet(innerElementSize)}) {
+    let col = colIn * ${innerElementSize};
+    if (row < dimAOuter && col < dimBOuter) {
+      var value = valueInput;
+      let outWidth = ${isChannelsLast ? 'outShape[2]' : 'outShape[3]'};
+      ${coordResSnippet}
+      ${biasActivationSnippet(addBias, activation)}
+      result[getIndexFromCoords4D(coords, outShape)/${innerElementSize}] = value;
+    }
+  }`;
+      return userCode;
+    };
+
+export const createConv2DTransposeMatMulProgramInfo =
+    (inputs: readonly TensorView[], metadata: ProgramMetadata, attributes: ConvTransposeAttributes,
+     outputShape: readonly number[], dimAOuter: number, dimBOuter: number, dimInner: number, hasBias: boolean,
+     sequentialAccessByThreads: boolean): ProgramInfo => {
+      const isChannelsLast = attributes.format === 'NHWC';
+      const inChannels = isChannelsLast ? inputs[0].dims[3] : inputs[0].dims[1];
+      const batchSize = outputShape[0];
+      const outWidth = isChannelsLast ? outputShape[2] : outputShape[3];
+      const outHeight = isChannelsLast ? outputShape[1] : outputShape[2];
+      const outChannels = isChannelsLast ? outputShape[3] : outputShape[1];
+      const isVec4 =
+          isChannelsLast ? inChannels % 4 === 0 && outChannels % 4 === 0 : outWidth % 4 === 0 && outChannels % 4 === 0;
+
+      // TODO: fine tune size
+      const dispatchX = isChannelsLast ? outChannels : outWidth * outHeight;
+      const dispatchY = isChannelsLast ? outWidth * outHeight : outChannels;
+      const workGroupSize: [number, number, number] = isVec4 ?
+          [8, 8, 1] :
+          [(dispatchX <= 4 || dispatchY <= 4) ? 4 : 16, dispatchX > 4 && dispatchY <= 4 ? 4 : 16, 1];
+      const elementsPerThread =
+          isVec4 ? [4, 4, 1] : [dispatchX <= 4 ? 1 : 4, dispatchX > 4 && dispatchY <= 4 ? 1 : 4, 1];
+      const dispatch = [
+        Math.ceil(dispatchX / workGroupSize[0] / elementsPerThread[0]),
+        Math.ceil(dispatchY / workGroupSize[1] / elementsPerThread[1]),
+        Math.ceil(batchSize / workGroupSize[2] / elementsPerThread[2])
+      ];
+
+      LOG_DEBUG('verbose', () => `[conv_backprop_mm_webgpu] dispatch = ${dispatch}`);
+
+      const innerElementSize = isVec4 ? 4 : 1;
+      const tileInner = Math.max(workGroupSize[0] * innerElementSize, workGroupSize[1]);
+
+
+      const declareInputs = [
+        `@group(0) @binding(0) var<storage, read> x: array<${isVec4 ? 'vec4<f32>' : 'f32'}>;`,
+        '@group(0) @binding(1) var<storage, read> W: array<f32>;'
+      ];
+      let declareFunctions = '';
+      if (hasBias) {
+        declareInputs.push(`@group(0) @binding(2) var<storage, read> bias: array<${isVec4 ? 'vec4<f32>' : 'f32'}>;`);
+        declareFunctions += `
+        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${isVec4 ? 'vec4<f32>' : 'f32'} {
+          return bias[coords.${isChannelsLast ? 'w' : 'y'}${isVec4 ? '/ 4' : ''}];
+        }`;
+      }
+      return {
+        ...metadata,
+        outputs: [{dims: outputShape, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default}],
+        dispatchGroup: () => ({x: dispatch[0], y: dispatch[1], z: dispatch[2]}),
+        getShaderSource: () => `
+        ${utilFunctions}
+        ${declareInputs.join('\n')}
+        @group(0) @binding(${declareInputs.length}) var<storage, read_write> result: array<${
+            isVec4 ? 'vec4<f32>' : 'f32'}>;
+        const outBackprop : vec4<i32> = vec4<i32>(${inputs[0].dims.join(',')});
+        const xShape : vec4<i32> = vec4<i32>(${inputs[0].dims.join(',')});
+        const wShape : vec4<i32> = vec4<i32>(${inputs[1].dims.join(',')});
+        const outShape : vec4<i32> = vec4<i32>(${outputShape.join(',')});
+        const outShapeStrides : vec3<i32> = vec3<i32>(${ShapeUtil.computeStrides(outputShape).slice(0, 3).join(',')});
+        const filterDims : vec2<i32> = vec2<i32>(${attributes.kernelShape[isChannelsLast ? 1 : 2]}, ${
+            attributes.kernelShape[isChannelsLast ? 2 : 3]});
+        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(
+              ${
+            attributes.dilations[0] <= 1 ?
+                0 :
+                (attributes.kernelShape[isChannelsLast ? 1 : 2] - 1) * (attributes.dilations[0] - 1)},
+              ${
+            attributes.dilations[1] <= 1 ?
+                0 :
+                (attributes.kernelShape[isChannelsLast ? 2 : 3] - 1) * (attributes.dilations[1] - 1)});
+        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${
+            attributes.pads[0] + attributes.pads[2]})/2,
+                                         i32(effectiveFilterDims[1]) - 1 - (${
+            attributes.pads[1] + attributes.pads[3]})/2);
+        const strides : vec2<i32> = vec2<i32>(${attributes.strides[0]}, ${attributes.strides[1]});
+        const dilation : vec2<i32> = vec2<i32>(${attributes.dilations[0]}, ${attributes.dilations[1]});
+        const dimAOuter : i32 = ${dimAOuter};
+        const dimBOuter : i32 = ${dimBOuter};
+        const dimInner : i32 = ${dimInner};
+        ${declareFunctions}
+        ${conv2dTransposeCommonSnippet(isChannelsLast, hasBias, undefined, false, innerElementSize)}
+        ${
+            isVec4 ?
+                makeMatMulPackedVec4Source(elementsPerThread, workGroupSize, undefined, !isChannelsLast, tileInner) :
+                makeMatMulPackedSource(
+                    elementsPerThread, workGroupSize, undefined, !isChannelsLast, tileInner, false, undefined,
+                    sequentialAccessByThreads)}`
+      };
+    };

js/web/lib/wasm/jsep/webgpu/ops/3rd-party/conv_backprop_webgpu.ts

@@ -197,14 +197,14 @@ const createConvTranspose2DOpProgramShaderSource =
                 continue;
               }
               let idyC: u32 = u32(dyC);
-
+              var inputChannel = groupId * ${inputChannelsPerGroup};
               for (var d2: u32 = 0; d2 < ${inputChannelsPerGroup}; d2 = d2 + 1) {
-                let inputChannel = groupId * ${inputChannelsPerGroup} + d2;
                 let xValue = ${
           isChannelsLast ? dy.get('batch', 'idyR', 'idyC', 'inputChannel') :
                            dy.get('batch', 'inputChannel', 'idyR', 'idyC')};
                 let wValue = ${w.get('inputChannel', 'wOutChannel', 'u32(wRPerm)', 'u32(wCPerm)')};
                 dotProd = dotProd + xValue * wValue;
+                inputChannel = inputChannel + 1;
               }
             }
           }

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

@@ -8,7 +8,9 @@ import {ComputeContext, GpuDataType, ProgramInfoLoader, ProgramMetadata} from '.
 
 import {createConvTranspose2DProgramInfo} from './3rd-party/conv_backprop_webgpu';
 import {ConvAttributes} from './conv';
+import {createConv2DTransposeMatMulProgramInfoLoader} from './conv2dtranspose-mm';
 import {parseInternalActivationAttributes} from './fuse-utils';
+import {createTransposeProgramInfo, TransposeAttributes, transposeProgramMetadata} from './transpose';
 
 const computeTotalPad =
     (inDim: number, stride: number, adj: number, kernel: number, dilation: number, outSize: number) =>
@@ -63,7 +65,7 @@ const getAdjustedConvTransposeAttributes =
     <T extends ConvTransposeAttributes>(attributes: T, inputs: readonly TensorView[]): T => {
       const kernelShape = attributes.kernelShape.slice();
       // if kernelShape is not specified in the attributes of this op, infer it from the weight tensor dims
-      if (attributes.kernelShape.length === 0 || attributes.kernelShape.reduce((a, b) => a * b, 0) === 0) {
+      if (attributes.kernelShape.length === 0 || attributes.kernelShape.reduce((a, b) => a * b, 1) === 0) {
         kernelShape.length = 0;
         for (let i = 2; i < inputs[1].dims.length; ++i) {
           kernelShape.push(inputs[1].dims[i]);
@@ -95,9 +97,11 @@ const getAdjustedConvTransposeAttributes =
 
       // always return a new object so does not modify the original attributes
       const newAttributes: T = Object.assign({}, attributes);
-      Object.assign(
-          newAttributes,
-          {kernelShape, pads, outputPadding, outputShape, dilations, strides, cacheKey: attributes.cacheKey});
+      const cacheKey = attributes.cacheKey + [
+        kernelShape.join('n,'), pads.join(','), strides.join(','), outputPadding.join(','), outputShape.join(','),
+        dilations.join(',')
+      ].join('_');
+      Object.assign(newAttributes, {kernelShape, pads, outputPadding, outputShape, dilations, strides, cacheKey});
       return newAttributes;
     };
 
@@ -226,12 +230,64 @@ const createConvTranspose2DProgramInfoLoader =
       };
     };
 
+// for transposing weight tensor from [C, M/group, KH, KW] to [KH, KW, M/group, C]
+const weightTransposeAttribute: TransposeAttributes = createAttributeWithCacheKey({perm: [2, 3, 1, 0]});
+
 const convTranspose2d =
     (context: ComputeContext, inputs: readonly TensorView[], attributes: ConvTransposeAttributes): void => {
       const adjustedAttributes = getAdjustedConvTransposeAttributes(attributes, inputs);
+      const isChannelsLast = attributes.format === 'NHWC';
+      const hasBias = inputs.length === 3;
+      if (adjustedAttributes.group !== 1) {
+        context.compute(createConvTranspose2DProgramInfoLoader(inputs, adjustedAttributes));
+        return;
+      }
+      const outputShape = adjustedAttributes.outputShape;
+      const outHeight = outputShape[isChannelsLast ? 1 : 2];
+      const outWidth = outputShape[isChannelsLast ? 2 : 3];
+      const outChannels = outputShape[isChannelsLast ? 3 : 1];
+      const weightHeight = inputs[1].dims[2];
+      const weightWidth = inputs[1].dims[3];
+      const inputChannels = inputs[0].dims[isChannelsLast ? 3 : 1];
 
-      context.compute(createConvTranspose2DProgramInfoLoader(inputs, adjustedAttributes));
+      const dimAOuter = isChannelsLast ? outHeight * outWidth : outChannels;
+      const dimBOuter = isChannelsLast ? outChannels : outHeight * outWidth;
+      const dimInner = weightHeight * weightWidth * inputChannels;
+
+      const sequentialAccessByThreads = /* backend.adapterInfo.isIntel() */ true;
+
+
+      // STEP.1: transpose weight
+      const transposedWeight = (context.kernelCustomData.wT as TensorView | undefined) ??
+          context.compute(
+              {
+                ...transposeProgramMetadata,
+                cacheHint: weightTransposeAttribute.cacheKey,
+                get: () => createTransposeProgramInfo(inputs[1], weightTransposeAttribute.perm)
+              },
+              {inputs: [1], outputs: [attributes.wIsConst ? -2 : -1]})[0];
+      if (attributes.wIsConst && !context.kernelCustomData.wT) {
+        context.kernelCustomData.wT = transposedWeight;
+      }
+
+      // STEP.2: prepare reshaped inputs
+      const convTransposeInputs = [inputs[0], transposedWeight];
+      if (hasBias) {
+        if (!isChannelsLast && inputs[2].dims.length === 1) {
+          convTransposeInputs.push(inputs[2].reshape([inputs[2].dims[0], 1, 1]));
+        } else {
+          convTransposeInputs.push(inputs[2]);
+        }
+      }
+
+      // STEP.3: compute matmul
+      context.compute(
+          createConv2DTransposeMatMulProgramInfoLoader(
+              convTransposeInputs, adjustedAttributes, outputShape, dimAOuter, dimBOuter, dimInner, hasBias,
+              sequentialAccessByThreads),
+          {inputs: convTransposeInputs});
     };
+
 const convTranspose1d = (context: ComputeContext, attributes: ConvTransposeAttributes): void => {
   // extend the input to 2D by adding H dimension
   const isChannelLast = attributes.format === 'NHWC';

js/web/lib/wasm/jsep/webgpu/ops/conv2dtranspose-mm.ts

@@ -0,0 +1,29 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+import {TensorView} from '../../tensor-view';
+import {GpuDataType, ProgramInfoLoader, ProgramMetadata} from '../types';
+
+import {createConv2DTransposeMatMulProgramInfo} from './3rd-party/conv_backprop_mm_webgpu';
+import {ConvTransposeAttributes} from './conv-transpose';
+
+
+const createConv2DTransposeMatMulProgramMetadata = (hasBias: boolean, cacheHint: string): ProgramMetadata => ({
+  name: 'Conv2DTransposeMatMul',
+  inputTypes: hasBias ? [GpuDataType.default, GpuDataType.default, GpuDataType.default] :
+                        [GpuDataType.default, GpuDataType.default],
+  cacheHint
+});
+
+export const createConv2DTransposeMatMulProgramInfoLoader =
+    (inputs: readonly TensorView[], attributes: ConvTransposeAttributes, outputShape: readonly number[],
+     dimAOuter: number, dimBOuter: number, dimInner: number, hasBias: boolean,
+     sequentialAccessByThreads: boolean): ProgramInfoLoader => {
+      const metadata = createConv2DTransposeMatMulProgramMetadata(hasBias, attributes.cacheKey);
+      return {
+        ...metadata,
+        get: () => createConv2DTransposeMatMulProgramInfo(
+            inputs, metadata, attributes, outputShape, dimAOuter, dimBOuter, dimInner, hasBias,
+            sequentialAccessByThreads)
+      };
+    };

js/web/test/data/ops/conv-transpose.jsonc

@@ -28,6 +28,37 @@
       }
     ]
   },
+  {
+    "name": "ConvTranspose without bias addition A - NHWC",
+    "inputShapeDefinitions": "rankOnly",
+    "opset": { "domain": "", "version": 17 },
+    "operator": "ConvTranspose",
+    "attributes": [{ "name": "kernel_shape", "data": [2, 2], "type": "ints" }],
+    "cases": [
+      {
+        "name": "T[0]",
+        "inputs": [
+          {
+            "data": [10, 20, 30, 40],
+            "dims": [1, 1, 2, 2],
+            "type": "float32"
+          },
+          {
+            "data": [1, 2, 3, 4],
+            "dims": [1, 1, 2, 2],
+            "type": "float32"
+          }
+        ],
+        "outputs": [
+          {
+            "data": [10, 40, 40, 60, 200, 160, 90, 240, 160],
+            "dims": [1, 1, 3, 3],
+            "type": "float32"
+          }
+        ]
+      }
+    ]
+  },
   {
     "name": "ConvTranspose without bias addition B",
     "operator": "ConvTranspose",
@@ -74,26 +105,22 @@
           },
           {
             "data": [
-              1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-              1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
+              1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+              30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+              56, 57, 58, 59, 60, 61, 62, 63, 64
             ],
             "dims": [4, 4, 2, 2],
             "type": "float32"
           },
           {
-            "data": [0.1, 0.2, 0.3, 0.4],
+            "data": [65, 66, 67, 68],
             "dims": [4],
             "type": "float32"
           }
         ],
         "outputs": [
           {
-            "data": [
-              100.0999984741211, 100.0999984741211, 100.0999984741211, 100.0999984741211, 100.19999694824219,
-              100.19999694824219, 100.19999694824219, 100.19999694824219, 100.30000305175781, 100.30000305175781,
-              100.30000305175781, 100.30000305175781, 100.4000015258789, 100.4000015258789, 100.4000015258789,
-              100.4000015258789
-            ],
+            "data": [3365, 3465, 3565, 3665, 3766, 3866, 3966, 4066, 4167, 4267, 4367, 4467, 4568, 4668, 4768, 4868],
             "dims": [1, 4, 2, 2],
             "type": "float32"
           }
@@ -115,7 +142,7 @@
             "type": "float32"
           },
           {
-            "data": [1, 1, 1, 1],
+            "data": [1, 2, 3, 4],
             "dims": [1, 1, 2, 2],
             "type": "float32"
           },
@@ -127,7 +154,43 @@
         ],
         "outputs": [
           {
-            "data": [11, 19, 20, 12, 20, 43, 46, 23, 22, 49, 52, 25, 13, 25, 26, 14],
+            "data": [11, 25, 28, 19, 32, 86, 99, 55, 40, 114, 131, 67, 29, 73, 80, 41],
+            "dims": [1, 1, 4, 4],
+            "type": "float32"
+          }
+        ]
+      }
+    ]
+  },
+  {
+    "name": "ConvTranspose with bias addition B - NHWC",
+    "operator": "ConvTranspose",
+    "inputShapeDefinitions": "rankOnly",
+    "opset": { "domain": "", "version": 17 },
+    "attributes": [{ "name": "kernel_shape", "data": [2, 2], "type": "ints" }],
+    "cases": [
+      {
+        "name": "T[0]",
+        "inputs": [
+          {
+            "data": [6, 8, 7, 9, 15, 11, 8, 12, 9],
+            "dims": [1, 1, 3, 3],
+            "type": "float32"
+          },
+          {
+            "data": [1, 2, 3, 4],
+            "dims": [1, 1, 2, 2],
+            "type": "float32"
+          },
+          {
+            "data": [5],
+            "dims": [1],
+            "type": "float32"
+          }
+        ],
+        "outputs": [
+          {
+            "data": [11, 25, 28, 19, 32, 86, 99, 55, 40, 114, 131, 67, 29, 73, 80, 41],
             "dims": [1, 1, 4, 4],
             "type": "float32"
           }
@@ -251,7 +314,6 @@
       }
     ]
   },
-
   {
     "name": "ConvTranspose- pointwise",
     "operator": "ConvTranspose",
@@ -285,5 +347,50 @@
         ]
       }
     ]
+  },
+  {
+    "name": "ConvTranspose with bias addition C",
+    "operator": "ConvTranspose",
+    "inputShapeDefinitions": "rankOnly",
+    "opset": { "domain": "", "version": 17 },
+    "attributes": [{ "name": "kernel_shape", "data": [1, 1], "type": "ints" }],
+    "cases": [
+      {
+        "name": "T[0]",
+        "inputs": [
+          {
+            "data": [
+              1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+              30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+              56, 57, 58, 59, 60, 61, 62, 63, 64
+            ],
+            "dims": [1, 4, 4, 4],
+            "type": "float32"
+          },
+          {
+            "data": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+            "dims": [4, 4, 1, 1],
+            "type": "float32"
+          },
+          {
+            "data": [1, 2, 3, 4],
+            "dims": [4],
+            "type": "float32"
+          }
+        ],
+        "outputs": [
+          {
+            "data": [
+              1021, 1049, 1077, 1105, 1133, 1161, 1189, 1217, 1245, 1273, 1301, 1329, 1357, 1385, 1413, 1441, 1122,
+              1154, 1186, 1218, 1250, 1282, 1314, 1346, 1378, 1410, 1442, 1474, 1506, 1538, 1570, 1602, 1223, 1259,
+              1295, 1331, 1367, 1403, 1439, 1475, 1511, 1547, 1583, 1619, 1655, 1691, 1727, 1763, 1324, 1364, 1404,
+              1444, 1484, 1524, 1564, 1604, 1644, 1684, 1724, 1764, 1804, 1844, 1884, 1924
+            ],
+            "dims": [1, 4, 4, 4],
+            "type": "float32"
+          }
+        ]
+      }
+    ]
   }
 ]

onnxruntime/core/providers/js/operators/conv_transpose.h

@@ -108,6 +108,23 @@ class ConvTranspose : public JsKernel {
     }
   }
 
+  Status PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
+                 /*out*/ bool& is_packed,
+                 /*out*/ PrePackedWeights* /* prepacked_weights */) override {
+    is_packed = false;
+
+    if (input_idx == 1) {
+      // Only handle the common case of conv2D
+      if (tensor.Shape().NumDimensions() != 4 || tensor.SizeInBytes() == 0) {
+        return Status::OK();
+      }
+
+      w_is_const_ = true;
+    }
+
+    return Status::OK();
+  }
+
  protected:
   ConvTransposeAttributes conv_transpose_attrs_;
   bool w_is_const_;