diff --git a/js/web/lib/wasm/jsep/webgpu/ops/attention.ts b/js/web/lib/wasm/jsep/webgpu/ops/attention.ts
index 79b24e9c4d67a..94ad67d3c3b0a 100644
--- a/js/web/lib/wasm/jsep/webgpu/ops/attention.ts
+++ b/js/web/lib/wasm/jsep/webgpu/ops/attention.ts
@@ -5,7 +5,7 @@ import {DataType} from '../../../wasm-common';
 import {TensorView} from '../../tensor-view';
 import {ComputeContext, GpuDataType, ProgramInputTensorInfoDependency, ProgramUniform} from '../types';
 
-import {createTensorShapeVariables, getMaxComponents, inputVariable, outputVariable, ShaderHelper, tensorTypeToWsglStorageType, tensorTypeToWsglValueType, UniformDataElementType, UniformsArrayType} from './common';
+import {getMaxComponents, inputVariable, outputVariable, ShaderHelper, tensorTypeToWsglStorageType, tensorTypeToWsglValueType, UniformDataElementType, UniformsArrayType} from './common';
 import {createConcatProgramInfo} from './concat';
 
 export const enum AttentionQkvFormat {
@@ -351,31 +351,27 @@ const createAttentionProbsProgramInfo =
       const programUniforms: ProgramUniform[] = [
         {type: DataType.uint32, data: parameters.sequenceLength}, {type: DataType.uint32, data: vectorizedHeadSize},
         {type: DataType.uint32, data: totalSequenceLength}, {type: DataType.uint32, data: parameters.numHeads},
-        {type: q.dataType, data: alpha}
+        {type: DataType.float, data: alpha}
       ];
 
-      const inputDependencies: ProgramInputTensorInfoDependency[] = ['type', 'type'];
-      if (relativePositionBias) {
-        inputDependencies.push('rank');
-        programUniforms.push(...createTensorShapeVariables(relativePositionBias.dims));
-      }
+      const inputDependencies: ProgramInputTensorInfoDependency[] =
+          relativePositionBias ? ['type', 'type', 'type'] : ['type', 'type'];
 
       const getShaderSource = (shaderHelper: ShaderHelper) => {
         const qInput = inputVariable('q', q.dataType, q.dims, components);
         const kInput = inputVariable('key', key.dataType, key.dims, components);
         const inputVars = [qInput, kInput];
-        const relativePositionBiasInput = relativePositionBias ?
-            inputVariable('relative_position_bias', relativePositionBias.dataType, relativePositionBias.dims.length) :
-            undefined;
-        if (relativePositionBiasInput) {
-          inputVars.push(relativePositionBiasInput);
+        if (relativePositionBias) {
+          inputVars.push(
+              inputVariable('relative_position_bias', relativePositionBias.dataType, relativePositionBias.dims));
         }
         const output = outputVariable('output', q.dataType, probsShape);
-        const dataType = tensorTypeToWsglStorageType(q.dataType);
+        // const dataType = tensorTypeToWsglStorageType(q.dataType);
+        const f32Type = tensorTypeToWsglValueType(DataType.float, components);
 
         const uniforms: UniformsArrayType = [
           {name: 'M', type: 'u32'}, {name: 'K', type: 'u32'}, {name: 'N', type: 'u32'},
-          {name: 'num_heads', type: 'u32'}, {name: 'alpha', type: dataType as UniformDataElementType}
+          {name: 'num_heads', type: 'u32'}, {name: 'alpha', type: 'f32' as UniformDataElementType}
         ];
         return `
   const TILE_SIZE = ${TILE_SIZE}u;
@@ -393,7 +389,7 @@ const createAttentionProbsProgramInfo =
     let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
     let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;
 
-    var value = ${qInput.type.value}(0);
+    var value = ${f32Type}(0);
     for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
       if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
         tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
@@ -404,7 +400,7 @@ const createAttentionProbsProgramInfo =
       workgroupBarrier();
 
       for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
-        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k];
+        value += ${f32Type}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
       }
 
       workgroupBarrier();
@@ -413,7 +409,7 @@ const createAttentionProbsProgramInfo =
     let headOffset = headIdx * uniforms.M * uniforms.N;
     if (global_id.y < uniforms.M && global_id.x < uniforms.N) {
       let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
-      var sum = ${(() => {
+      var sum: f32 = ${(() => {
           switch (components) {
             case 1:
               return 'value';
@@ -425,17 +421,8 @@ const createAttentionProbsProgramInfo =
               throw new Error(`Unsupported components: ${components}`);
           }
         })()};
-
-  ${(() => {
-          if (relativePositionBiasInput) {
-            return `
-      let batch = workgroup_id.z / uniforms.num_heads;
-      let head = workgroup_id.z % uniforms.num_heads;
-      var indices = ${relativePositionBiasInput.type.indices}(batch, head, global_id.y, global_id.x);
-      output[outputIdx] = sum * uniforms.alpha + ${relativePositionBiasInput.getByIndices('indices')};`;
-          }
-          return 'output[outputIdx] = sum * uniforms.alpha;';
-        })()}
+        output[outputIdx] = ${output.type.value} (sum * uniforms.alpha) + ${
+            relativePositionBias ? 'relative_position_bias[outputIdx]' : '0.0'};
     }
   }`;
       };
@@ -502,7 +489,7 @@ const createVxAttentionScoreProgramInfo =
        tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];
      }
      workgroupBarrier();
-     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
+     for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];
      }
      workgroupBarrier();
@@ -512,7 +499,7 @@ const createVxAttentionScoreProgramInfo =
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;
    if (m < uniforms.M && n < uniforms.N) {
-     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size
+     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
        + currentBatchHeadNumber * uniforms.N + n;
      output[outputIdx] = value;
    }