[js/webgpu] Use DataType as uniform cpu type

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

@@ -3,7 +3,7 @@
 
 import {Env, Tensor, TRACE, TRACE_FUNC_BEGIN, TRACE_FUNC_END} from 'onnxruntime-common';
 
-import {tensorDataTypeEnumToString} from '../wasm-common';
+import {DataType, tensorDataTypeEnumToString} from '../wasm-common';
 
 import {configureLogger, LOG_DEBUG} from './log';
 import {createView, TensorView} from './tensor-view';
@@ -428,10 +428,10 @@ export class WebGpuBackend {
           return;
         }
         // https://www.w3.org/TR/WGSL/#alignof
-        const sizeOfElement = v.type === 'float16' ? 2 : 4;
+        const sizeOfElement = v.type === DataType.float16 ? 2 : 4;
         let sizeOfVecOrMat;
         let baseAlignment;
-        if (v.type === 'float16') {
+        if (v.type === DataType.float16) {
           baseAlignment = data.length > 4 ? 16 : (data.length > 2 ? 8 : data.length * sizeOfElement);
           sizeOfVecOrMat = data.length > 4 ? 16 : sizeOfElement * data.length;
         } else {
@@ -445,7 +445,7 @@ export class WebGpuBackend {
         // SizeOf(vec4<i32|u32|f32>). For float16 type, when data.length > 4, the uniform variable is of type
         // array<mat2x4<f16>,N>, where N = Math.ceil(data.length / 8) and SizeOf(mat2x4<f16>) = 16. The total byte
         // length is N * SizeOf(mat2x4<f16>).
-        const elementPerVecOrMat = v.type === 'float16' ? 8 : 4;
+        const elementPerVecOrMat = v.type === DataType.float16 ? 8 : 4;
         currentOffset += data.length > 4 ? Math.ceil(data.length / elementPerVecOrMat) * sizeOfVecOrMat :
                                            data.length * sizeOfElement;
       });
@@ -458,15 +458,17 @@ export class WebGpuBackend {
       programUniforms.forEach((v, i) => {
         const offset = offsets[i];
         const data = typeof v.data === 'number' ? [v.data] : v.data;
-        if (v.type === 'int32') {
+        if (v.type === DataType.int32) {
           new Int32Array(arrayBuffer, offset, data.length).set(data);
-        } else if (v.type === 'uint32') {
+        } else if (v.type === DataType.uint32) {
           new Uint32Array(arrayBuffer, offset, data.length).set(data);
-        } else if (v.type === 'float16') {
+        } else if (v.type === DataType.float16) {
           // TODO: use Float16Array.
           new Uint16Array(arrayBuffer, offset, data.length).set(data);
-        } else {
+        } else if (v.type === DataType.float) {
           new Float32Array(arrayBuffer, offset, data.length).set(data);
+        } else {
+          throw new Error(`Unsupported uniform type: ${tensorDataTypeEnumToString(v.type)}`);
         }
       });
 

js/web/lib/wasm/jsep/webgpu/ops/3rd-party/conv2d_mm_webgpu.ts

@@ -19,6 +19,7 @@
 //
 // modified to fit the needs of the project
 
+import {DataType} from '../../../../wasm-common';
 import {LOG_DEBUG} from '../../../log';
 import {TensorView} from '../../../tensor-view';
 import {ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform} from '../../types';
@@ -189,13 +190,13 @@ export const createConv2DMatMulProgramInfo =
       const elementsSize = isVec4 ? [innerElementSize, 4, 4] : [1, 1, 1];
 
       const programUniforms: ProgramUniform[] = [
-        {type: 'int32', data: dimAOuter}, {type: 'int32', data: dimBOuter}, {type: 'int32', data: dimInner},
-        {type: 'int32', data: [attributes.pads[0], attributes.pads[1]]}, {type: 'int32', data: attributes.strides},
-        {type: 'int32', data: attributes.dilations}
+        {type: DataType.int32, data: dimAOuter}, {type: DataType.int32, data: dimBOuter},
+        {type: DataType.int32, data: dimInner}, {type: DataType.int32, data: [attributes.pads[0], attributes.pads[1]]},
+        {type: DataType.int32, data: attributes.strides}, {type: DataType.int32, data: attributes.dilations}
       ];
       if (attributes.activation === 'Clip') {
         programUniforms.push(
-            {type: 'float32', data: attributes.clipMax!}, {type: 'float32', data: attributes.clipMin!});
+            {type: DataType.float, data: attributes.clipMax!}, {type: DataType.float, data: attributes.clipMin!});
       }
       programUniforms.push(
           ...createTensorShapeVariables(inputs[0].dims), ...createTensorShapeVariables(inputs[1].dims));

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

@@ -19,6 +19,7 @@
 //
 // modified to fit the needs of the project
 
+import {DataType} from '../../../../wasm-common';
 import {LOG_DEBUG} from '../../../log';
 import {TensorView} from '../../../tensor-view';
 import {ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform} from '../../types';
@@ -197,13 +198,14 @@ export const createConv2DTransposeMatMulProgramInfo =
       ];
 
       const programUniforms: ProgramUniform[] = [
-        {type: 'int32', data: dimAOuter}, {type: 'int32', data: dimBOuter}, {type: 'int32', data: dimInner},
-        {type: 'int32', data: attributes.strides}, {type: 'int32', data: attributes.dilations},
-        {type: 'int32', data: filterDims}, {type: 'int32', data: pads}
+        {type: DataType.int32, data: dimAOuter}, {type: DataType.int32, data: dimBOuter},
+        {type: DataType.int32, data: dimInner}, {type: DataType.int32, data: attributes.strides},
+        {type: DataType.int32, data: attributes.dilations}, {type: DataType.int32, data: filterDims},
+        {type: DataType.int32, data: pads}
       ];
       if (attributes.activation === 'Clip') {
         programUniforms.push(
-            {type: 'float32', data: attributes.clipMax!}, {type: 'float32', data: attributes.clipMin!});
+            {type: DataType.float, data: attributes.clipMax!}, {type: DataType.float, data: attributes.clipMin!});
       }
       programUniforms.push(
           ...createTensorShapeVariables(inputs[0].dims), ...createTensorShapeVariables(inputs[1].dims));

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

@@ -17,6 +17,7 @@
 
 // sampled from [@tensorflow/tfjs] tfjs-backend-webgpu/src/conv_backprop_webgpu.ts
 
+import {DataType} from '../../../../wasm-common';
 import {LOG_DEBUG} from '../../../log';
 import {TensorView} from '../../../tensor-view';
 import {ShapeUtil} from '../../../util';
@@ -264,9 +265,10 @@ export const createConvTranspose2DProgramInfo =
       const outputChannelsPerGroup = wShape[1];
 
       const programUniforms: ProgramUniform[] = [
-        {type: 'int32', data: outputSize}, {type: 'uint32', data: strides}, {type: 'uint32', data: filterDims},
-        {type: 'uint32', data: dilations}, {type: 'uint32', data: effectiveFilterDims}, {type: 'int32', data: pads},
-        {type: 'uint32', data: inputChannelsPerGroup}, {type: 'uint32', data: outputChannelsPerGroup},
+        {type: DataType.int32, data: outputSize}, {type: DataType.uint32, data: strides},
+        {type: DataType.uint32, data: filterDims}, {type: DataType.uint32, data: dilations},
+        {type: DataType.uint32, data: effectiveFilterDims}, {type: DataType.int32, data: pads},
+        {type: DataType.uint32, data: inputChannelsPerGroup}, {type: DataType.uint32, data: outputChannelsPerGroup},
         ...createTensorShapeVariables(inputs[0].dims), ...createTensorShapeVariables(inputs[1].dims)
       ];
       if (hasBias) {

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

@@ -19,6 +19,7 @@
 //
 // modified to fit the needs of the project
 
+import {DataType} from '../../../../wasm-common';
 import {TensorView} from '../../../tensor-view';
 import {ShapeUtil} from '../../../util';
 import {ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform} from '../../types';
@@ -408,7 +409,7 @@ const matMulReadWriteFnSource =
         ${
           hasBias ?
               `value = value + ${isChannelsLast ? 'bias[colIn]' : `${typeSnippet(component, dataType)}(bias[row])`};` :
-                                                  ''                                    }
+                                                  ''}
         ${applyActivation}
         ${outputVariable.setByIndices('vec3<u32>(coords)', 'value')}
       }
@@ -447,12 +448,14 @@ export const createMatmulProgramInfo =
       const bShapeTemp = [...outerDimsB, dimInner, dimBOuter / components];
       const bRank = bShapeTemp.length;
       const outputShapeTemp = [batchSize, dimAOuter, dimBOuter / components];
-      const programUniforms: ProgramUniform[] =
-          [{type: 'int32', data: dimAOuter}, {type: 'int32', data: dimBOuter}, {type: 'int32', data: dimInner}];
+      const programUniforms: ProgramUniform[] = [
+        {type: DataType.int32, data: dimAOuter}, {type: DataType.int32, data: dimBOuter},
+        {type: DataType.int32, data: dimInner}
+      ];
       if (activationAttributes.activation === 'Clip') {
         programUniforms.push(
-            {type: 'float32', data: activationAttributes.clipMax!},
-            {type: 'float32', data: activationAttributes.clipMin!});
+            {type: DataType.float, data: activationAttributes.clipMax!},
+            {type: DataType.float, data: activationAttributes.clipMin!});
       }
       programUniforms.push(
           ...createTensorShapeVariables(outerDims), ...createTensorShapeVariables(aShapeTemp),

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

@@ -1,7 +1,7 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
-import {tensorDataTypeEnumToString} from '../../../wasm-common';
+import {DataType} from '../../../wasm-common';
 import {TensorView} from '../../tensor-view';
 import {ComputeContext, GpuDataType, ProgramUniform} from '../types';
 
@@ -241,9 +241,10 @@ export const computeInPlaceSoftmax = (context: ComputeContext, input: TensorView
     WG = Math.ceil(dComp / 8);
   }
   const elementsPerWG = Math.ceil(d / components / WG);
-  const tensorDataType = tensorDataTypeEnumToString(input.dataType) as ProgramUniform['type'];
-  const programUniforms: ProgramUniform[] =
-      [{type: tensorDataType, data: 1 / d}, {type: 'uint32', data: dComp}, {type: 'uint32', data: elementsPerWG}];
+  const programUniforms: ProgramUniform[] = [
+    {type: input.dataType, data: 1 / d}, {type: DataType.uint32, data: dComp},
+    {type: DataType.uint32, data: elementsPerWG}
+  ];
   const dataType = tensorTypeToWsglStorageType(input.dataType, components);
 
   const getShaderSource = (shaderHelper: ShaderHelper) => {
@@ -336,11 +337,10 @@ const computeAttentionProbs =
         y: Math.ceil(parameters.sequenceLength / TILE_SIZE),
         z: parameters.batchSize * parameters.numHeads
       };
-      const tensorDataType = tensorDataTypeEnumToString(q.dataType) as ProgramUniform['type'];
       const programUniforms: ProgramUniform[] = [
-        {type: 'uint32', data: parameters.sequenceLength}, {type: 'uint32', data: vectorizedHeadSize},
-        {type: 'uint32', data: parameters.totalSequenceLength}, {type: 'uint32', data: parameters.kvSequenceLength},
-        {type: tensorDataType, data: alpha}
+        {type: DataType.uint32, data: parameters.sequenceLength}, {type: DataType.uint32, data: vectorizedHeadSize},
+        {type: DataType.uint32, data: parameters.totalSequenceLength},
+        {type: DataType.uint32, data: parameters.kvSequenceLength}, {type: q.dataType, data: alpha}
       ];
 
       const inputs = [q, key];
@@ -430,9 +430,9 @@ const computeVxAttentionScore =
         z: params.batchSize * params.numHeads
       };
       const programUniforms: ProgramUniform[] = [
-        {type: 'uint32', data: params.sequenceLength}, {type: 'uint32', data: params.totalSequenceLength},
-        {type: 'uint32', data: params.vHeadSize}, {type: 'uint32', data: params.numHeads},
-        {type: 'uint32', data: params.vHiddenSize}
+        {type: DataType.uint32, data: params.sequenceLength}, {type: DataType.uint32, data: params.totalSequenceLength},
+        {type: DataType.uint32, data: params.vHeadSize}, {type: DataType.uint32, data: params.numHeads},
+        {type: DataType.uint32, data: params.vHiddenSize}
       ];
 
       const getShaderSource = (shaderHelper: ShaderHelper) => {
@@ -526,10 +526,10 @@ const prepare = (context: ComputeContext, parameters: AttentionParameters) => {
   };
   const inputs = [context.inputs[0], context.inputs[1], context.inputs[2]];
   const programUniforms: ProgramUniform[] = [
-    {type: 'uint32', data: M}, {type: 'uint32', data: K}, {type: 'uint32', data: N},
-    {type: 'uint32', data: parameters.numHeads}, {type: 'uint32', data: parameters.headSize},
-    {type: 'uint32', data: parameters.hiddenSize},
-    {type: 'uint32', data: parameters.hiddenSize + parameters.hiddenSize + parameters.vHiddenSize}
+    {type: DataType.uint32, data: M}, {type: DataType.uint32, data: K}, {type: DataType.uint32, data: N},
+    {type: DataType.uint32, data: parameters.numHeads}, {type: DataType.uint32, data: parameters.headSize},
+    {type: DataType.uint32, data: parameters.hiddenSize},
+    {type: DataType.uint32, data: parameters.hiddenSize + parameters.hiddenSize + parameters.vHiddenSize}
   ];
 
   const getShaderSource = (shaderHelper: ShaderHelper) => {

js/web/lib/wasm/jsep/webgpu/ops/batch-norm.ts

@@ -3,6 +3,7 @@
 
 import {env} from 'onnxruntime-common';
 
+import {DataType} from '../../../wasm-common';
 import {TensorView} from '../../tensor-view';
 import {ShapeUtil} from '../../util';
 import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
@@ -123,11 +124,11 @@ const createBatchNormInferenceProgramInfo =
           dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)},
           programUniforms: useShapesUniforms ?
               [
-                {type: 'uint32', data: outputSize},
+                {type: DataType.uint32, data: outputSize},
                 ...createTensorShapeVariables(yShape),
               ] :
               [
-                {type: 'uint32', data: outputSize},
+                {type: DataType.uint32, data: outputSize},
               ],
         }),
       };

js/web/lib/wasm/jsep/webgpu/ops/binary-op.ts

@@ -179,7 +179,7 @@ const createBinaryOpProgramInfo =
           outputs: [{dims: outputShape, dataType: outputDataType}],
           dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */ / 4 /* component size */)},
           programUniforms: [
-            {type: 'uint32', data: Math.ceil(ShapeUtil.size(outputShape) / 4)},
+            {type: DataType.uint32, data: Math.ceil(ShapeUtil.size(outputShape) / 4)},
             ...createTensorShapeVariables(a.dims),
             ...createTensorShapeVariables(b.dims),
             ...createTensorShapeVariables(outputShape),

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

@@ -259,8 +259,9 @@ export const tensorTypeToWsglValueType = (type: DataType, components: 1|2|3|4 =
   return typeof mappedType === 'string' ? mappedType : mappedType[1];
 };
 
-export const createTensorShapeVariables = (dims: readonly number[]): ProgramUniform[] =>
-    dims.length === 0 ? [] : [{type: 'uint32', data: dims}, {type: 'uint32', data: ShapeUtil.computeStrides(dims)}];
+export const createTensorShapeVariables = (dims: readonly number[]): ProgramUniform[] => dims.length === 0 ?
+    [] :
+    [{type: DataType.uint32, data: dims}, {type: DataType.uint32, data: ShapeUtil.computeStrides(dims)}];
 
 /**
  * A helper function to get maximum vector size for specified data length

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

@@ -1,6 +1,7 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
+import {DataType} from '../../../wasm-common';
 import {TensorView} from '../../tensor-view';
 import {ShapeUtil} from '../../util';
 import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
@@ -95,14 +96,14 @@ const createConcatProgramInfo = (inputs: readonly TensorView[], axis: number): P
   let previousSum = 0;
   const inputDependencies: ProgramInputTensorInfoDependency[] = [];
   const inputRanks = [];
-  const programUniforms: ProgramUniform[] = [{type: 'uint32', data: outputSize}];
+  const programUniforms: ProgramUniform[] = [{type: DataType.uint32, data: outputSize}];
   for (let i = 0; i < inputs.length; ++i) {
     previousSum += inputs[i].dims[adjustedAxis];
     sizeInConcatAxis[i] = previousSum;
     inputRanks.push(inputs[i].dims.length);
     inputVars[i] = inputVariable(`input${i}`, dataType, inputRanks[i]);
     inputDependencies.push('rank');
-    programUniforms.push({type: 'uint32', data: sizeInConcatAxis[i]});
+    programUniforms.push({type: DataType.uint32, data: sizeInConcatAxis[i]});
   }
   for (let i = 0; i < inputs.length; ++i) {
     programUniforms.push(...createTensorShapeVariables(inputs[i].dims));

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

@@ -1,6 +1,7 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
+import {DataType} from '../../../wasm-common';
 import {TensorView} from '../../tensor-view';
 import {ShapeUtil} from '../../util';
 import {ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform} from '../types';
@@ -28,13 +29,14 @@ export const createGroupedConvProgramInfo =
       const outputSize = ShapeUtil.size(outputShape);
 
       const programUniforms: ProgramUniform[] = [
-        {type: 'uint32', data: outputSize}, {type: 'uint32', data: attributes.dilations},
-        {type: 'uint32', data: [attributes.strides[0], attributes.strides[1]]},
-        {type: 'uint32', data: [attributes.pads[0], attributes.pads[1]]}, {type: 'uint32', data: outputChannelsPerGroup}
+        {type: DataType.uint32, data: outputSize}, {type: DataType.uint32, data: attributes.dilations},
+        {type: DataType.uint32, data: [attributes.strides[0], attributes.strides[1]]},
+        {type: DataType.uint32, data: [attributes.pads[0], attributes.pads[1]]},
+        {type: DataType.uint32, data: outputChannelsPerGroup}
       ];
       if (attributes.activation === 'Clip') {
         programUniforms.push(
-            {type: 'float32', data: attributes.clipMax!}, {type: 'float32', data: attributes.clipMin!});
+            {type: DataType.float, data: attributes.clipMax!}, {type: DataType.float, data: attributes.clipMin!});
       }
       programUniforms.push(
           ...createTensorShapeVariables(xShape), ...createTensorShapeVariables(wShape),
@@ -132,8 +134,8 @@ export const createGroupedConvVectorizeProgramInfo =
       const outputShapeInShader = [outputShape[0], outputShape[1], outputShape[2], outputShape[3] / components];
 
       const programUniforms: ProgramUniform[] = [
-        {type: 'uint32', data: outputSize}, {type: 'int32', data: attributes.strides},
-        {type: 'int32', data: attributes.pads}, ...createTensorShapeVariables(xShape),
+        {type: DataType.uint32, data: outputSize}, {type: DataType.int32, data: attributes.strides},
+        {type: DataType.int32, data: attributes.pads}, ...createTensorShapeVariables(xShape),
         ...createTensorShapeVariables(wShape), ...createTensorShapeVariables(outputShapeInShader)
       ];
       const xNumber = (outputNumber - 1) * attributes.strides[1] + wShape[1];

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

@@ -54,7 +54,7 @@ const createCumsumProgramInfo =
               outputs: [{dims: inputShape, dataType: inputType}],
               dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)},
               programUniforms: [
-                {type: 'uint32', data: outputSize}, {type: 'int32', data: axis},
+                {type: DataType.uint32, data: outputSize}, {type: DataType.int32, data: axis},
                 ...createTensorShapeVariables(inputShape), ...createTensorShapeVariables(inputShape)
               ]
 

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

@@ -1,6 +1,7 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Licensed under the MIT License.
 
+import {DataType} from '../../../wasm-common';
 import {TensorView} from '../../tensor-view';
 import {ShapeUtil} from '../../util';
 import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
@@ -272,8 +273,10 @@ const createEinsumProgramInfo =
           // filter is added to make sure that dimValue is never 0.
           const programUniformsInit: ProgramUniform[] =
               uniformsSymbols.filter((symbol) => einsumEquation.symbolToInfo.has(symbol))
-                  .map((symbol) => ({type: 'uint32', data: einsumEquation.symbolToInfo.get(symbol)?.dimValue || 0}));
-          programUniformsInit.push({type: 'uint32', data: outputSize});
+                  .map(
+                      (symbol) =>
+                          ({type: DataType.uint32, data: einsumEquation.symbolToInfo.get(symbol)?.dimValue || 0}));
+          programUniformsInit.push({type: DataType.uint32, data: outputSize});
           const programUniforms: ProgramUniform[] =
               inputShapes.map((dims, _) => [...createTensorShapeVariables(dims)])
                   .reduce((acc, inputProgramUniforms) => acc.concat(inputProgramUniforms), programUniformsInit);