Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

145 files changed, 64589 insertions, 0 deletions

diff --git a/testing/web-platform/tests/webnn/META.yml b/testing/web-platform/tests/webnn/META.yml
new file mode 100644
index 0000000000..3f87fc8042
--- /dev/null
+++ b/testing/web-platform/tests/webnn/META.yml
@@ -0,0 +1,4 @@
+spec: https://webmachinelearning.github.io/webnn/
+suggested_reviewers:
+  - dontcallmedom
+  - Honry
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/arg_min_max.https.any.js b/testing/web-platform/tests/webnn/arg_min_max.https.any.js
new file mode 100644
index 0000000000..cff1d6a955
--- /dev/null
+++ b/testing/web-platform/tests/webnn/arg_min_max.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API argMin/Max operations
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-argminmax
+
+testWebNNOperation(['argMin', 'argMax'], buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/batch_normalization.https.any.js b/testing/web-platform/tests/webnn/batch_normalization.https.any.js
new file mode 100644
index 0000000000..15e66a8bc0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/batch_normalization.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API batchNormalization operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-batchnorm
+
+testWebNNOperation('batchNormalization', buildBatchNorm);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/cast.https.any.js b/testing/web-platform/tests/webnn/cast.https.any.js
new file mode 100644
index 0000000000..c9be45a770
--- /dev/null
+++ b/testing/web-platform/tests/webnn/cast.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API cast operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-cast
+
+testWebNNOperation('cast', buildCast);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/clamp.https.any.js b/testing/web-platform/tests/webnn/clamp.https.any.js
new file mode 100644
index 0000000000..9818aed7c1
--- /dev/null
+++ b/testing/web-platform/tests/webnn/clamp.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API clamp operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-clamp
+
+testWebNNOperation('clamp', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/concat.https.any.js b/testing/web-platform/tests/webnn/concat.https.any.js
new file mode 100644
index 0000000000..cce43e492f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/concat.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API concat operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-concat
+
+testWebNNOperation('concat', buildConcat);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/conv2d.https.any.js b/testing/web-platform/tests/webnn/conv2d.https.any.js
new file mode 100644
index 0000000000..b26b35ec67
--- /dev/null
+++ b/testing/web-platform/tests/webnn/conv2d.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API conv2d operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-conv2d
+
+testWebNNOperation('conv2d', buildConv2d);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/conv_transpose2d.https.any.js b/testing/web-platform/tests/webnn/conv_transpose2d.https.any.js
new file mode 100644
index 0000000000..99e76b825e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/conv_transpose2d.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API convTranspose2d operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-convtranspose2d
+
+testWebNNOperation('convTranspose2d', buildConvTranspose2d);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/elementwise_binary.https.any.js b/testing/web-platform/tests/webnn/elementwise_binary.https.any.js
new file mode 100644
index 0000000000..06c2404f95
--- /dev/null
+++ b/testing/web-platform/tests/webnn/elementwise_binary.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API element-wise binary operations
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-binary
+
+testWebNNOperation(['add', 'sub', 'mul', 'div', 'max', 'min', 'pow'], buildOperationWithTwoInputs);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/elementwise_logical.https.any.js b/testing/web-platform/tests/webnn/elementwise_logical.https.any.js
new file mode 100644
index 0000000000..e614b94df5
--- /dev/null
+++ b/testing/web-platform/tests/webnn/elementwise_logical.https.any.js
@@ -0,0 +1,20 @@
+// META: title=test WebNN API element-wise logical operations
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-logical
+
+testWebNNOperation(
+  [
+    'equal',
+    'greater',
+    'greaterOrEqual',
+    'lesser',
+    'lesserOrEqual',
+  ],
+  buildOperationWithTwoInputs
+);
+testWebNNOperation('logicalNot', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/elementwise_unary.https.any.js b/testing/web-platform/tests/webnn/elementwise_unary.https.any.js
new file mode 100644
index 0000000000..4cdfee5bcb
--- /dev/null
+++ b/testing/web-platform/tests/webnn/elementwise_unary.https.any.js
@@ -0,0 +1,13 @@
+// META: title=test WebNN API element-wise unary operations
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-unary
+
+testWebNNOperation(
+  ['abs', 'ceil', 'cos', 'erf', 'exp', 'floor', 'identity', 'log', 'neg', 'reciprocal', 'sin', 'sqrt', 'tan'],
+  buildOperationWithSingleInput
+);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/elu.https.any.js b/testing/web-platform/tests/webnn/elu.https.any.js
new file mode 100644
index 0000000000..57b624b5da
--- /dev/null
+++ b/testing/web-platform/tests/webnn/elu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API elu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-elu
+
+testWebNNOperation('elu', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/expand.https.any.js b/testing/web-platform/tests/webnn/expand.https.any.js
new file mode 100644
index 0000000000..11abb9baa8
--- /dev/null
+++ b/testing/web-platform/tests/webnn/expand.https.any.js
@@ -0,0 +1,11 @@
+// META: title=test WebNN API expand operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-expand
+
+// reuse buildReshape method
+testWebNNOperation('expand', buildReshape);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gather.https.any.js b/testing/web-platform/tests/webnn/gather.https.any.js
new file mode 100644
index 0000000000..52bcece804
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gather.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API gather operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-gather
+
+testWebNNOperation('gather', buildOperationWithTwoInputs);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gemm.https.any.js b/testing/web-platform/tests/webnn/gemm.https.any.js
new file mode 100644
index 0000000000..e5de9521fb
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gemm.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API gemm operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-gemm
+
+testWebNNOperation('gemm', buildGemm);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/arg_min_max.https.any.js b/testing/web-platform/tests/webnn/gpu/arg_min_max.https.any.js
new file mode 100644
index 0000000000..76092ea92e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/arg_min_max.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API argMin/Max operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-argminmax
+
+testWebNNOperation(['argMin', 'argMax'], buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/batch_normalization.https.any.js b/testing/web-platform/tests/webnn/gpu/batch_normalization.https.any.js
new file mode 100644
index 0000000000..90b6def636
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/batch_normalization.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API batchNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-batchnorm
+
+testWebNNOperation('batchNormalization', buildBatchNorm, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/cast.https.any.js b/testing/web-platform/tests/webnn/gpu/cast.https.any.js
new file mode 100644
index 0000000000..1830364eb5
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/cast.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API cast operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-cast
+
+testWebNNOperation('cast', buildCast, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/clamp.https.any.js b/testing/web-platform/tests/webnn/gpu/clamp.https.any.js
new file mode 100644
index 0000000000..98313868b6
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/clamp.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API clamp operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-clamp
+
+testWebNNOperation('clamp', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/concat.https.any.js b/testing/web-platform/tests/webnn/gpu/concat.https.any.js
new file mode 100644
index 0000000000..07073724fe
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/concat.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API concat operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-concat
+
+testWebNNOperation('concat', buildConcat, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/conv2d.https.any.js b/testing/web-platform/tests/webnn/gpu/conv2d.https.any.js
new file mode 100644
index 0000000000..b3986b6555
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/conv2d.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API conv2d operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-conv2d
+
+testWebNNOperation('conv2d', buildConv2d, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/conv_transpose2d.https.any.js b/testing/web-platform/tests/webnn/gpu/conv_transpose2d.https.any.js
new file mode 100644
index 0000000000..020bfa9c97
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/conv_transpose2d.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API convTranspose2d operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-convtranspose2d
+
+testWebNNOperation('convTranspose2d', buildConvTranspose2d, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/elementwise_binary.https.any.js b/testing/web-platform/tests/webnn/gpu/elementwise_binary.https.any.js
new file mode 100644
index 0000000000..035d0c77c0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/elementwise_binary.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API element-wise binary operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-binary
+
+testWebNNOperation(['add', 'sub', 'mul', 'div', 'max', 'min', 'pow'], buildOperationWithTwoInputs, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/elementwise_logical.https.any.js b/testing/web-platform/tests/webnn/gpu/elementwise_logical.https.any.js
new file mode 100644
index 0000000000..f597ce8e0b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/elementwise_logical.https.any.js
@@ -0,0 +1,20 @@
+// META: title=test WebNN API element-wise logical operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-logical
+
+testWebNNOperation(
+  [
+    'equal',
+    'greater',
+    'greaterOrEqual',
+    'lesser',
+    'lesserOrEqual',
+  ],
+  buildOperationWithTwoInputs, 'gpu'
+);
+testWebNNOperation('logicalNot', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/elementwise_unary.https.any.js b/testing/web-platform/tests/webnn/gpu/elementwise_unary.https.any.js
new file mode 100644
index 0000000000..45978f91ec
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/elementwise_unary.https.any.js
@@ -0,0 +1,13 @@
+// META: title=test WebNN API element-wise unary operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-unary
+
+testWebNNOperation(
+  ['abs', 'ceil', 'cos', 'erf', 'exp', 'floor', 'identity', 'log', 'neg', 'reciprocal', 'sin', 'sqrt', 'tan'],
+  buildOperationWithSingleInput, 'gpu'
+);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/elu.https.any.js b/testing/web-platform/tests/webnn/gpu/elu.https.any.js
new file mode 100644
index 0000000000..965bb4d35f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/elu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API elu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-elu
+
+testWebNNOperation('elu', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/expand.https.any.js b/testing/web-platform/tests/webnn/gpu/expand.https.any.js
new file mode 100644
index 0000000000..82fa891a39
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/expand.https.any.js
@@ -0,0 +1,11 @@
+// META: title=test WebNN API expand operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-expand
+
+// reuse buildReshape method
+testWebNNOperation('expand', buildReshape, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/gather.https.any.js b/testing/web-platform/tests/webnn/gpu/gather.https.any.js
new file mode 100644
index 0000000000..7c8a685c5c
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/gather.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API gather operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-gather
+
+testWebNNOperation('gather', buildOperationWithTwoInputs, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/gemm.https.any.js b/testing/web-platform/tests/webnn/gpu/gemm.https.any.js
new file mode 100644
index 0000000000..a19dc39bbc
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/gemm.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API gemm operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-gemm
+
+testWebNNOperation('gemm', buildGemm, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/hard_sigmoid.https.any.js b/testing/web-platform/tests/webnn/gpu/hard_sigmoid.https.any.js
new file mode 100644
index 0000000000..b6f2f53b32
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/hard_sigmoid.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API hardSigmoid operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-hard-sigmoid
+
+testWebNNOperation('hardSigmoid', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/hard_swish.https.any.js b/testing/web-platform/tests/webnn/gpu/hard_swish.https.any.js
new file mode 100644
index 0000000000..a1731490bd
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/hard_swish.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API tanh operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-hard-swish
+
+testWebNNOperation('hardSwish', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/instance_normalization.https.any.js b/testing/web-platform/tests/webnn/gpu/instance_normalization.https.any.js
new file mode 100644
index 0000000000..e1d73de387
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/instance_normalization.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API instanceNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-instancenorm
+
+testWebNNOperation('instanceNormalization', buildLayerNorm, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/layer_normalization.https.any.js b/testing/web-platform/tests/webnn/gpu/layer_normalization.https.any.js
new file mode 100644
index 0000000000..1deb43bee5
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/layer_normalization.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API layerNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-layernorm
+
+testWebNNOperation('layerNormalization', buildLayerNorm, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/leaky_relu.https.any.js b/testing/web-platform/tests/webnn/gpu/leaky_relu.https.any.js
new file mode 100644
index 0000000000..f3a7bd8ba5
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/leaky_relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API leakyRelu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-leakyrelu
+
+testWebNNOperation('leakyRelu', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/linear.https.any.js b/testing/web-platform/tests/webnn/gpu/linear.https.any.js
new file mode 100644
index 0000000000..48b8d82c1e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/linear.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API linear operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-linear
+
+testWebNNOperation('linear', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/matmul.https.any.js b/testing/web-platform/tests/webnn/gpu/matmul.https.any.js
new file mode 100644
index 0000000000..01fed04e3d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/matmul.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API matmul operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-matmul
+
+testWebNNOperation('matmul', buildOperationWithTwoInputs, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/pad.https.any.js b/testing/web-platform/tests/webnn/gpu/pad.https.any.js
new file mode 100644
index 0000000000..26d1bf0f38
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/pad.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API pad operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-pad
+
+testWebNNOperation('pad', buildPad, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/pooling.https.any.js b/testing/web-platform/tests/webnn/gpu/pooling.https.any.js
new file mode 100644
index 0000000000..ab12881881
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/pooling.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API pooling operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-pool2d
+
+testWebNNOperation(['averagePool2d', 'maxPool2d'], buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/prelu.https.any.js b/testing/web-platform/tests/webnn/gpu/prelu.https.any.js
new file mode 100644
index 0000000000..5a1580e662
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/prelu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API prelu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-prelu
+
+testWebNNOperation('prelu', buildOperationWithTwoInputs, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/reduction.https.any.js b/testing/web-platform/tests/webnn/gpu/reduction.https.any.js
new file mode 100644
index 0000000000..69f9b64df4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/reduction.https.any.js
@@ -0,0 +1,24 @@
+// META: title=test WebNN API reduction  operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reduce
+
+testWebNNOperation(
+  [
+    'reduceL1',
+    'reduceL2',
+    'reduceLogSum',
+    'reduceLogSumExp',
+    'reduceMax',
+    'reduceMean',
+    'reduceMin',
+    'reduceProduct',
+    'reduceSum',
+    'reduceSumSquare',
+  ],
+  buildOperationWithSingleInput, 'gpu'
+);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/relu.https.any.js b/testing/web-platform/tests/webnn/gpu/relu.https.any.js
new file mode 100644
index 0000000000..dc09846e3b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API relu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-relu
+
+testWebNNOperation('relu', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/reshape.https.any.js b/testing/web-platform/tests/webnn/gpu/reshape.https.any.js
new file mode 100644
index 0000000000..e5145e2403
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/reshape.https.any.js
@@ -0,0 +1,11 @@
+// META: title=test WebNN API reshape operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reshape
+
+testWebNNOperation('reshape', buildReshape, 'gpu');
+
diff --git a/testing/web-platform/tests/webnn/gpu/sigmoid.https.any.js b/testing/web-platform/tests/webnn/gpu/sigmoid.https.any.js
new file mode 100644
index 0000000000..6c85f5b303
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/sigmoid.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API sigmoid operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-sigmoid
+
+testWebNNOperation('sigmoid', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/slice.https.any.js b/testing/web-platform/tests/webnn/gpu/slice.https.any.js
new file mode 100644
index 0000000000..98e5f422da
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/slice.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API slice operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-slice
+
+testWebNNOperation('slice', buildSlice, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/softmax.https.any.js b/testing/web-platform/tests/webnn/gpu/softmax.https.any.js
new file mode 100644
index 0000000000..9170dd0e14
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/softmax.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softmax operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softmax
+
+testWebNNOperation('softmax', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/softplus.https.any.js b/testing/web-platform/tests/webnn/gpu/softplus.https.any.js
new file mode 100644
index 0000000000..a61e5beaef
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/softplus.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softplus operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softplus
+
+testWebNNOperation('softplus', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/softsign.https.any.js b/testing/web-platform/tests/webnn/gpu/softsign.https.any.js
new file mode 100644
index 0000000000..f598cbfcef
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/softsign.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softsign operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softsign
+
+testWebNNOperation('softsign', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/split.https.any.js b/testing/web-platform/tests/webnn/gpu/split.https.any.js
new file mode 100644
index 0000000000..8eecd76fa1
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/split.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API split operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-split
+
+testWebNNOperation('split', buildSplit, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/tanh.https.any.js b/testing/web-platform/tests/webnn/gpu/tanh.https.any.js
new file mode 100644
index 0000000000..15a9eeb013
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/tanh.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API tanh operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-tanh
+
+testWebNNOperation('tanh', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/transpose.https.any.js b/testing/web-platform/tests/webnn/gpu/transpose.https.any.js
new file mode 100644
index 0000000000..074e18a488
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/transpose.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API transpose operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-transpose
+
+testWebNNOperation('transpose', buildOperationWithSingleInput, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/gpu/where.https.any.js b/testing/web-platform/tests/webnn/gpu/where.https.any.js
new file mode 100644
index 0000000000..ec0c5530a0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/gpu/where.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API where operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-where
+
+testWebNNOperation('where', buildWhere, 'gpu');
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/hard_sigmoid.https.any.js b/testing/web-platform/tests/webnn/hard_sigmoid.https.any.js
new file mode 100644
index 0000000000..81bd5124ce
--- /dev/null
+++ b/testing/web-platform/tests/webnn/hard_sigmoid.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API hardSigmoid operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-hard-sigmoid
+
+testWebNNOperation('hardSigmoid', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/hard_swish.https.any.js b/testing/web-platform/tests/webnn/hard_swish.https.any.js
new file mode 100644
index 0000000000..052c7f2a20
--- /dev/null
+++ b/testing/web-platform/tests/webnn/hard_swish.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API tanh operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-hard-swish
+
+testWebNNOperation('hardSwish', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/idlharness.https.any.js b/testing/web-platform/tests/webnn/idlharness.https.any.js
new file mode 100644
index 0000000000..65e21d5a1d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/idlharness.https.any.js
@@ -0,0 +1,56 @@
+// META: global=window,dedicatedworker
+// META: script=/resources/WebIDLParser.js
+// META: script=/resources/idlharness.js
+// META: script=./resources/utils.js
+// META: timeout=long
+
+// https://webmachinelearning.github.io/webnn/
+
+'use strict';
+
+idl_test(
+  ['webnn'],
+  ['html', 'webidl', 'webgpu'],
+  async (idl_array) => {
+    if (self.GLOBAL.isWindow()) {
+      idl_array.add_objects({ Navigator: ['navigator'] });
+    } else if (self.GLOBAL.isWorker()) {
+      idl_array.add_objects({ WorkerNavigator: ['navigator'] });
+    }
+
+    idl_array.add_objects({
+      NavigatorML: ['navigator'],
+      ML: ['navigator.ml'],
+      MLContext: ['context'],
+      MLOperand: ['input', 'filter', 'output'],
+      MLActivation: ['relu'],
+      MLGraphBuilder: ['builder'],
+      MLGraph: ['graph']
+    });
+
+    for (const executionType of ExecutionArray) {
+      const isSync = executionType === 'sync';
+      if (self.GLOBAL.isWindow() && isSync) {
+        continue;
+      }
+
+      if (isSync) {
+        self.context = navigator.ml.createContextSync();
+      } else {
+        self.context = await navigator.ml.createContext();
+      }
+
+      self.builder = new MLGraphBuilder(self.context);
+      self.input = builder.input('input', {dataType: 'float32', dimensions: [1, 1, 5, 5]});
+      self.filter = builder.constant({dataType: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array(9).fill(1));
+      self.relu = builder.relu();
+      self.output = builder.conv2d(input, filter, {activation: relu, inputLayout: "nchw"});
+
+      if (isSync) {
+        self.graph = builder.buildSync({output});
+      } else {
+        self.graph = await builder.build({output});
+      }
+    }
+  }
+);
diff --git a/testing/web-platform/tests/webnn/instance_normalization.https.any.js b/testing/web-platform/tests/webnn/instance_normalization.https.any.js
new file mode 100644
index 0000000000..0b7a708917
--- /dev/null
+++ b/testing/web-platform/tests/webnn/instance_normalization.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API instanceNormalization operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-instancenorm
+
+testWebNNOperation('instanceNormalization', buildLayerNorm);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/layer_normalization.https.any.js b/testing/web-platform/tests/webnn/layer_normalization.https.any.js
new file mode 100644
index 0000000000..380db4ea52
--- /dev/null
+++ b/testing/web-platform/tests/webnn/layer_normalization.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API layerNormalization operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-layernorm
+
+testWebNNOperation('layerNormalization', buildLayerNorm);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/leaky_relu.https.any.js b/testing/web-platform/tests/webnn/leaky_relu.https.any.js
new file mode 100644
index 0000000000..61539ce92e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/leaky_relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API leakyRelu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-leakyrelu
+
+testWebNNOperation('leakyRelu', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/linear.https.any.js b/testing/web-platform/tests/webnn/linear.https.any.js
new file mode 100644
index 0000000000..4b2c05540b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/linear.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API linear operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-linear
+
+testWebNNOperation('linear', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/matmul.https.any.js b/testing/web-platform/tests/webnn/matmul.https.any.js
new file mode 100644
index 0000000000..8a9882afe6
--- /dev/null
+++ b/testing/web-platform/tests/webnn/matmul.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API matmul operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-matmul
+
+testWebNNOperation('matmul', buildOperationWithTwoInputs);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/pad.https.any.js b/testing/web-platform/tests/webnn/pad.https.any.js
new file mode 100644
index 0000000000..05eec653c6
--- /dev/null
+++ b/testing/web-platform/tests/webnn/pad.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API pad operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-pad
+
+testWebNNOperation('pad', buildPad);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/pooling.https.any.js b/testing/web-platform/tests/webnn/pooling.https.any.js
new file mode 100644
index 0000000000..df19e57709
--- /dev/null
+++ b/testing/web-platform/tests/webnn/pooling.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API pooling operations
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-pool2d
+
+testWebNNOperation(['averagePool2d', 'maxPool2d'], buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/prelu.https.any.js b/testing/web-platform/tests/webnn/prelu.https.any.js
new file mode 100644
index 0000000000..c1b2e9fa2a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/prelu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API prelu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-prelu
+
+testWebNNOperation('prelu', buildOperationWithTwoInputs);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/reduction.https.any.js b/testing/web-platform/tests/webnn/reduction.https.any.js
new file mode 100644
index 0000000000..20bd74d8de
--- /dev/null
+++ b/testing/web-platform/tests/webnn/reduction.https.any.js
@@ -0,0 +1,24 @@
+// META: title=test WebNN API reduction  operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reduce
+
+testWebNNOperation(
+  [
+    'reduceL1',
+    'reduceL2',
+    'reduceLogSum',
+    'reduceLogSumExp',
+    'reduceMax',
+    'reduceMean',
+    'reduceMin',
+    'reduceProduct',
+    'reduceSum',
+    'reduceSumSquare',
+  ],
+  buildOperationWithSingleInput
+);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/relu.https.any.js b/testing/web-platform/tests/webnn/relu.https.any.js
new file mode 100644
index 0000000000..42b64e11de
--- /dev/null
+++ b/testing/web-platform/tests/webnn/relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API relu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-relu
+
+testWebNNOperation('relu', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/reshape.https.any.js b/testing/web-platform/tests/webnn/reshape.https.any.js
new file mode 100644
index 0000000000..e0733635f8
--- /dev/null
+++ b/testing/web-platform/tests/webnn/reshape.https.any.js
@@ -0,0 +1,11 @@
+// META: title=test WebNN API reshape operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reshape
+
+testWebNNOperation('reshape', buildReshape);
+
diff --git a/testing/web-platform/tests/webnn/resources/test_data/abs.json b/testing/web-platform/tests/webnn/resources/test_data/abs.json
new file mode 100644
index 0000000000..d665da6bb4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/abs.json
@@ -0,0 +1,401 @@
+{
+  "tests": [
+    {
+      "name": "abs float32 1D constant tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            49.8372426667043,
+            82.0929095991747,
+            3.1989055476550305,
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+            -74.21503493794903,
+            95.60974816793694,
+            6.61428748549389,
+            31.283208179548808,
+            -53.20605974670913
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          49.837242126464844,
+          82.09291076660156,
+          3.1989054679870605,
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+          15.520709991455078,
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+          53.064762115478516,
+          99.6537094116211,
+          21.285049438476562,
+          90.01982879638672,
+          18.32451820373535,
+          33.06915283203125,
+          30.097660064697266,
+          74.21503448486328,
+          95.60974884033203,
+          6.614287376403809,
+          31.2832088470459,
+          53.206058502197266
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "abs float32 1D tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            49.8372426667043,
+            82.0929095991747,
+            3.1989055476550305,
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+            31.283208179548808,
+            -53.20605974670913
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          49.837242126464844,
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+          31.2832088470459,
+          53.206058502197266
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "abs float32 2D tensor",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            49.8372426667043,
+            82.0929095991747,
+            3.1989055476550305,
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+            31.283208179548808,
+            -53.20605974670913
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          49.837242126464844,
+          82.09291076660156,
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+          31.2832088470459,
+          53.206058502197266
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "abs float32 3D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            49.8372426667043,
+            82.0929095991747,
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+            6.61428748549389,
+            31.283208179548808,
+            -53.20605974670913
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
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+          53.206058502197266
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "abs float32 4D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            49.8372426667043,
+            82.0929095991747,
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+            31.283208179548808,
+            -53.20605974670913
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "abs float32 5D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            49.8372426667043,
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+            -53.20605974670913
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 1, 3],
+        "data": [
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+          53.206058502197266
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/add.json b/testing/web-platform/tests/webnn/resources/test_data/add.json
new file mode 100644
index 0000000000..dba361228b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/add.json
@@ -0,0 +1,882 @@
+{
+  "tests": [
+    {
+      "name": "add float32 1D constant tensors",
+      "inputs": {
+        "a": {
+          "shape": [24],
+          "data": [
+            -43.33331702607182,
+            -67.89795492110251,
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+            -29.986529326327684
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        "b": {
+          "shape": [24],
+          "data": [
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+            -44.814940102501396,
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+            28.281854673339666,
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+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
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+          -2.3239879608154297
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "add float32 1D tensors",
+      "inputs": {
+        "a": {
+          "shape": [24],
+          "data": [
+            -43.33331702607182,
+            -67.89795492110251,
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+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [24],
+          "data": [
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+          "type": "float32"
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+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "add float32 2D tensors",
+      "inputs": {
+        "a": {
+          "shape": [4, 6],
+          "data": [
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/arg_max.json b/testing/web-platform/tests/webnn/resources/test_data/arg_max.json
new file mode 100644
index 0000000000..d2fe9e62ca
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/arg_max.json
@@ -0,0 +1,963 @@
+{
+  "tests": [
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+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/arg_min.json b/testing/web-platform/tests/webnn/resources/test_data/arg_min.json
new file mode 100644
index 0000000000..132a2dc3e8
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/arg_min.json
@@ -0,0 +1,963 @@
+{
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json b/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json
new file mode 100644
index 0000000000..5a9f4e28b1
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json
@@ -0,0 +1,1684 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/batch_normalization.json b/testing/web-platform/tests/webnn/resources/test_data/batch_normalization.json
new file mode 100644
index 0000000000..04ab0d0d6f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/batch_normalization.json
@@ -0,0 +1,1181 @@
+{
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+    },
+    {
+      "name": "batchNormalization float32 4D NCHW tensor options.bias",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 2, 2],
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+          "type": "float32",
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+      "expected": {
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+        "data": [
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+    },
+    {
+      "name": "batchNormalization float32 4D NCHW tensor options.epsilon",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 2, 2],
+          "data": [
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+          "type": "float32"
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+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "epsilon": 1e-6 // A small value to prevent computational error due to divide-by-zero.
+      },
+      "expected": {
+        "shape": [2, 3, 2, 2],
+        "data": [
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+    },
+    {
+      "name": "batchNormalization float32 4D NCHW tensor options.activation relu",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 2, 2],
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+          "type": "float32",
+          "constant": true
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+      },
+      "options": {
+        "activation": "relu"
+      },
+      "expected": {
+        "shape": [2, 3, 2, 2],
+        "data": [
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "batchNormalization float32 4D NHWC tensor all options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/cast.json b/testing/web-platform/tests/webnn/resources/test_data/cast.json
new file mode 100644
index 0000000000..59d1036eee
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/cast.json
@@ -0,0 +1,3040 @@
+{
+  "tests": [
+    {
+      "name": "cast float32 0D tensor to int32",
+      "inputs": {
+        "input": {
+          "shape": [],
+          "data": [
+            84.77753448486328
+          ],
+          "type": "float32"
+        }
+      },
+      "type": "int32",
+      "expected": {
+        "name": "output",
+        "shape": [],
+        "data": [
+          84
+        ],
+        "type": "int32"
+      }
+    },
+    {
+      "name": "cast float32 1D tensor to int32",
+      "inputs": {
+        "input": {
+          "shape": [24],
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+            70.77123260498047,
+            83.5
+          ],
+          "type": "float32"
+        }
+      },
+      "type": "int32",
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          102,
+          -43,
+          52,
+          -99,
+          6,
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+          -27,
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+          83
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+        "type": "int32"
+      }
+    },
+    {
+      "name": "cast float32 2D tensor to int32",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
+            102.1578369140625,
+            -43.5,
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+            70.77123260498047,
+            83.5
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+          "type": "float32"
+        }
+      },
+      "type": "int32",
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
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+          -27,
+          -23,
+          70,
+          83
+        ],
+        "type": "int32"
+      }
+    },
+    {
+      "name": "cast float32 3D tensor to int32",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            102.1578369140625,
+            -43.5,
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+            70.77123260498047,
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+          "type": "float32"
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+      },
+      "type": "int32",
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
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+          -27,
+          -23,
+          70,
+          83
+        ],
+        "type": "int32"
+      }
+    },
+    {
+      "name": "cast float32 4D tensor to int32",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            102.1578369140625,
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+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/ceil.json b/testing/web-platform/tests/webnn/resources/test_data/ceil.json
new file mode 100644
index 0000000000..b9bccbc3a0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/ceil.json
@@ -0,0 +1,401 @@
+{
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+            69.71428621372127,
+            5.0031021954943355,
+            84.3683411023199,
+            -9.390542062321572,
+            -27.85661776414834,
+            -34.89593295662057
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 1, 3],
+        "data": [
+          68,
+          37,
+          100,
+          -22,
+          33,
+          18,
+          6,
+          13,
+          84,
+          -29,
+          20,
+          66,
+          27,
+          25,
+          -48,
+          -5,
+          62,
+          -87,
+          70,
+          6,
+          85,
+          -9,
+          -27,
+          -34
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/clamp.json b/testing/web-platform/tests/webnn/resources/test_data/clamp.json
new file mode 100644
index 0000000000..0e948f9931
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/clamp.json
@@ -0,0 +1,1169 @@
+{
+  "tests": [
+    // default options
+    {
+      "name": "clamp float32 1D constant tensor default options",
+      "inputs": {
+        "x": { // use 'x' for input operand name
+          "shape": [24],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1.537420630455017,
+          -7.34310245513916,
+          7.880751132965088,
+          -2.0564088821411133,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          9.280223846435547,
+          -2.3130595684051514,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "clamp float32 1D tensor default options",
+      "inputs": {
+        "x": { // use 'x' for input operand name
+          "shape": [24],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1.537420630455017,
+          -7.34310245513916,
+          7.880751132965088,
+          -2.0564088821411133,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          9.280223846435547,
+          -2.3130595684051514,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "clamp float32 2D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1.537420630455017,
+          -7.34310245513916,
+          7.880751132965088,
+          -2.0564088821411133,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          9.280223846435547,
+          -2.3130595684051514,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "clamp float32 3D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1.537420630455017,
+          -7.34310245513916,
+          7.880751132965088,
+          -2.0564088821411133,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          9.280223846435547,
+          -2.3130595684051514,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "clamp float32 4D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [3, 2, 2, 2],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 2, 2, 2],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1.537420630455017,
+          -7.34310245513916,
+          7.880751132965088,
+          -2.0564088821411133,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          9.280223846435547,
+          -2.3130595684051514,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "clamp float32 5D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 1, 1, 2, 3],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 1, 1, 2, 3],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1.537420630455017,
+          -7.34310245513916,
+          7.880751132965088,
+          -2.0564088821411133,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          9.280223846435547,
+          -2.3130595684051514,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      }
+    },
+    // default options.maxValue and specified options.minValue
+    {
+      "name": "clamp float32 4D tensor default options.maxValue and specified negative options.minValue",
+      "inputs": {
+        "x": {
+          "shape": [2, 1, 4, 3],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 3],
+        "data": [
+          -1,
+          -1,
+          -1,
+          -1,
+          -1,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          -1,
+          -1,
+          7.880751132965088,
+          -1,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          -1,
+          -1,
+          9.280223846435547,
+          -1,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          -1
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": -1.0
+      }
+    },
+    {
+      "name": "clamp float32 3D tensor default options.maxValue and specified options.minValue=0.0",
+      "inputs": {
+        "x": {
+          "shape": [6, 2, 2],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [6, 2, 2],
+        "data": [
+          0,
+          0,
+          0,
+          0,
+          0,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          0,
+          0,
+          7.880751132965088,
+          0,
+          6.3438639640808105,
+          5.525737762451172,
+          0.8433118462562561,
+          0,
+          0,
+          9.280223846435547,
+          0,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          0
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": 0.0
+      }
+    },
+    {
+      "name": "clamp float32 2D tensor default options.maxValue and specified positive options.minValue",
+      "inputs": {
+        "x": {
+          "shape": [3, 8],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 8],
+        "data": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          9.524681091308594,
+          3.7292487621307373,
+          6.481687068939209,
+          1,
+          1,
+          7.880751132965088,
+          1,
+          6.3438639640808105,
+          5.525737762451172,
+          1,
+          1,
+          1,
+          9.280223846435547,
+          1,
+          9.549695014953613,
+          5.788925647735596,
+          5.549378395080566,
+          7.409400463104248,
+          1
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": 1.0
+      }
+    },
+    // default options.minValue and specified options.maxValue
+    {
+      "name": "clamp float32 5D tensor default options.minValue and specified negative options.maxValue",
+      "inputs": {
+        "x": {
+          "shape": [2, 2, 1, 2, 3],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 3],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          -2,
+          -2,
+          -2,
+          -2,
+          -7.34310245513916,
+          -2,
+          -2.0564088821411133,
+          -2,
+          -2,
+          -2,
+          -8.199960708618164,
+          -7.786487102508545,
+          -2,
+          -2.3130595684051514,
+          -2,
+          -2,
+          -2,
+          -2,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "maxValue": -2.0
+      }
+    },
+    {
+      "name": "clamp float32 1D tensor default options.minValue and specified options.maxValue=0.0",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          0,
+          0,
+          0,
+          -1.537420630455017,
+          -7.34310245513916,
+          0,
+          -2.0564088821411133,
+          0,
+          0,
+          0,
+          -8.199960708618164,
+          -7.786487102508545,
+          0,
+          -2.3130595684051514,
+          0,
+          0,
+          0,
+          0,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "maxValue": 0.0
+      }
+    },
+    {
+      "name": "clamp float32 3D tensor default options.minValue and specified positive options.maxValue",
+      "inputs": {
+        "x": {
+          "shape": [3, 4, 2],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 4, 2],
+        "data": [
+          -9.817828178405762,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          3,
+          3,
+          3,
+          -1.537420630455017,
+          -7.34310245513916,
+          3,
+          -2.0564088821411133,
+          3,
+          3,
+          0.8433118462562561,
+          -8.199960708618164,
+          -7.786487102508545,
+          3,
+          -2.3130595684051514,
+          3,
+          3,
+          3,
+          3,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "maxValue": 3.0
+      }
+    },
+    // specified both options.minValue and options.maxValue
+    {
+      "name": "clamp float32 5D tensor specified both negative options.minValue and options.maxValue",
+      "inputs": {
+        "x": {
+          "shape": [3, 2, 1, 1, 4],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 2, 1, 1, 4],
+        "data": [
+          -8,
+          -6.024064064025879,
+          -4.0725626945495605,
+          -6.575078010559082,
+          -7.755683898925781,
+          -1,
+          -1,
+          -1,
+          -1.537420630455017,
+          -7.34310245513916,
+          -1,
+          -2.0564088821411133,
+          -1,
+          -1,
+          -1,
+          -8,
+          -7.786487102508545,
+          -1,
+          -2.3130595684051514,
+          -1,
+          -1,
+          -1,
+          -1,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": -8.0,
+        "maxValue": -1.0
+      }
+    },
+    {
+      "name": "clamp float32 4D tensor specified negative options.minValue and options.maxValue=0.0",
+      "inputs": {
+        "x": {
+          "shape": [1, 4, 3, 2],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 4, 3, 2],
+        "data": [
+          -6,
+          -6,
+          -4.0725626945495605,
+          -6,
+          -6,
+          0,
+          0,
+          0,
+          -1.537420630455017,
+          -6,
+          0,
+          -2.0564088821411133,
+          0,
+          0,
+          0,
+          -6,
+          -6,
+          0,
+          -2.3130595684051514,
+          0,
+          0,
+          0,
+          0,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": -6.0,
+        "maxValue": 0.0
+      }
+    },
+    {
+      "name": "clamp float32 3D tensor specified negative options.minValue and positive options.maxValue",
+      "inputs": {
+        "x": {
+          "shape": [2, 6, 2],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 6, 2],
+        "data": [
+          -3,
+          -3,
+          -3,
+          -3,
+          -3,
+          4,
+          3.7292487621307373,
+          4,
+          -1.537420630455017,
+          -3,
+          4,
+          -2.0564088821411133,
+          4,
+          4,
+          0.8433118462562561,
+          -3,
+          -3,
+          4,
+          -2.3130595684051514,
+          4,
+          4,
+          4,
+          4,
+          -2.123614549636841
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": -3.0,
+        "maxValue": 4.0
+      }
+    },
+    {
+      "name": "clamp float32 2D tensor specified options.minValue=0.0 and positive options.maxValue",
+      "inputs": {
+        "x": {
+          "shape": [6, 4],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [6, 4],
+        "data": [
+          0,
+          0,
+          0,
+          0,
+          0,
+          6,
+          3.7292487621307373,
+          6,
+          0,
+          0,
+          6,
+          0,
+          6,
+          5.525737762451172,
+          0.8433118462562561,
+          0,
+          0,
+          6,
+          0,
+          6,
+          5.788925647735596,
+          5.549378395080566,
+          6,
+          0
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": 0.0,
+        "maxValue": 6.0
+      }
+    },
+    {
+      "name": "clamp float32 1D tensor specified both positive options.minValue and options.maxValue",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -9.817828475355284,
+            -6.024063916325786,
+            -4.072562498632983,
+            -6.575078191902692,
+            -7.7556836912181915,
+            9.524681107378463,
+            3.7292487446449307,
+            6.4816868736447475,
+            -1.5374205904252634,
+            -7.343102426698445,
+            7.880751290929794,
+            -2.056408790509967,
+            6.34386375786449,
+            5.52573787183,
+            0.8433118207347725,
+            -8.19996033345526,
+            -7.786487326213716,
+            9.280223823954241,
+            -2.31305948485121,
+            9.549695091037119,
+            5.788925460130297,
+            5.549378312916486,
+            7.409400528051194,
+            -2.1236145770503745
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          2,
+          2,
+          2,
+          2,
+          2,
+          7,
+          3.7292487621307373,
+          6.481687068939209,
+          2,
+          2,
+          7,
+          2,
+          6.3438639640808105,
+          5.525737762451172,
+          2,
+          2,
+          2,
+          7,
+          2,
+          7,
+          5.788925647735596,
+          5.549378395080566,
+          7,
+          2
+        ],
+        "type": "float32"
+      },
+      "options": {
+        "minValue": 2.0,
+        "maxValue": 7.0
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/concat.json b/testing/web-platform/tests/webnn/resources/test_data/concat.json
new file mode 100644
index 0000000000..5e2285b75a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/concat.json
@@ -0,0 +1,3666 @@
+{
+  "tests": [
+    // concat 1D tensors
+    {
+      "name": "concat two float32 1D constant tensors of same shape along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [12],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        {
+          "name": "input2",
+          "shape": [12],
+          "data": [
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.3944413363933563,
+          0.861982524394989,
+          0.337996244430542,
+          -0.990639865398407,
+          0.576785683631897,
+          0.32276400923728943,
+          -0.44735023379325867,
+          0.11028251051902771,
+          -0.5945112705230713,
+          -0.402848482131958,
+          -0.9531654119491577,
+          -0.6731740236282349,
+          0.49189892411231995,
+          -0.15864109992980957,
+          -0.3418811559677124,
+          -0.9158143401145935,
+          -0.7206121683120728,
+          -0.7993468642234802,
+          0.6653800010681152,
+          0.03886038810014725,
+          0.5182055234909058,
+          -0.8742017149925232,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat two float32 1D tensors of same shape along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [12],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [12],
+          "data": [
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float32"
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.3944413363933563,
+          0.861982524394989,
+          0.337996244430542,
+          -0.990639865398407,
+          0.576785683631897,
+          0.32276400923728943,
+          -0.44735023379325867,
+          0.11028251051902771,
+          -0.5945112705230713,
+          -0.402848482131958,
+          -0.9531654119491577,
+          -0.6731740236282349,
+          0.49189892411231995,
+          -0.15864109992980957,
+          -0.3418811559677124,
+          -0.9158143401145935,
+          -0.7206121683120728,
+          -0.7993468642234802,
+          0.6653800010681152,
+          0.03886038810014725,
+          0.5182055234909058,
+          -0.8742017149925232,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat two float16 1D tensors of same shape along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [12],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [12],
+          "data": [
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float16"
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.39453125,
+          0.86181640625,
+          0.337890625,
+          -0.99072265625,
+          0.57666015625,
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+      }
+    },
+    {
+      "name": "concat three float32 1D tensors of different 1st dimension along axis 0",
+      "inputs": [
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+          "type": "float32"
+        }
+      ],
+      "axis": 0,
+      "expected": {
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+    {
+      "name": "concat three float16 1D tensors of different 1st dimension along axis 0",
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+      ],
+      "axis": 0,
+      "expected": {
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+        "shape": [24],
+        "data": [
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+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat four float32 1D tensors of same 1st dimension along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [6],
+          "data": [
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+        },
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+          "type": "float32"
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+        {
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+      "axis": 0,
+      "expected": {
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+        "data": [
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat four float16 1D tensors of same 1st dimension along axis 0",
+      "inputs": [
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+          "name": "input1",
+          "shape": [6],
+          "data": [
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+          "type": "float16"
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+        {
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+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat four float32 1D tensors of different 1st dimension along axis 0",
+      "inputs": [
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+          "name": "input1",
+          "shape": [2],
+          "data": [
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+          "type": "float32"
+        },
+        {
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+          "shape": [4],
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+        },
+        {
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+      ],
+      "axis": 0,
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat four float16 1D tensors of different 1st dimension along axis 0",
+      "inputs": [
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+          "name": "input1",
+          "shape": [2],
+          "data": [
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+          "type": "float16"
+        },
+        {
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+          "shape": [4],
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+            0.576785657225761,
+            0.3227640108329237
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+          "type": "float16"
+        },
+        {
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+          "shape": [8],
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+          "type": "float16"
+        },
+        {
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+          "shape": [10],
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+          "type": "float16"
+        }
+      ],
+      "axis": 0,
+      "expected": {
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+          -0.47900390625,
+          0.12115478515625
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+        "type": "float16"
+      }
+    },
+    // concat 2D tensors
+    {
+      "name": "concat two float32 2D tensors of same shape along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [2, 6],
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+          "type": "float32"
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+      ],
+      "axis": 0,
+      "expected": {
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+    },
+    {
+      "name": "concat two float16 2D tensors of same shape along axis 0",
+      "inputs": [
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+          "shape": [2, 6],
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+            0.1211843166661235
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+          "type": "float16"
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
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+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat two float32 2D tensors of same others dimensions except different 1st dimension along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 6],
+          "data": [
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+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [3, 6],
+          "data": [
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+          -0.5945112705230713,
+          -0.15864109992980957,
+          -0.3418811559677124,
+          -0.8742017149925232,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat four float16 2D tensors of same others dimensions except different 2nd dimension along axis 1",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [3, 1],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [3, 2],
+          "data": [
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input3",
+          "shape": [3, 2],
+          "data": [
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input4",
+          "shape": [3, 3],
+          "data": [
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float16"
+        }
+      ],
+      "axis": 1,
+      "expected": {
+        "name": "output",
+        "shape": [3, 8],
+        "data": [
+          -0.39453125,
+          -0.99072265625,
+          0.57666015625,
+          -0.40283203125,
+          -0.953125,
+          -0.916015625,
+          -0.720703125,
+          -0.79931640625,
+          0.86181640625,
+          0.32275390625,
+          -0.447265625,
+          -0.67333984375,
+          0.491943359375,
+          0.66552734375,
+          0.038848876953125,
+          0.51806640625,
+          0.337890625,
+          0.11029052734375,
+          -0.5947265625,
+          -0.15869140625,
+          -0.341796875,
+          -0.8740234375,
+          -0.47900390625,
+          0.12115478515625
+        ],
+        "type": "float16"
+      }
+    },
+    // concat 3D tensors
+    {
+      "name": "concat two float32 3D tensors of same others dimensions except different 1st dimension along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [2, 1, 3],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [6, 1, 3],
+          "data": [
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float32"
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [8, 1, 3],
+        "data": [
+          -0.3944413363933563,
+          0.861982524394989,
+          0.337996244430542,
+          -0.990639865398407,
+          0.576785683631897,
+          0.32276400923728943,
+          -0.44735023379325867,
+          0.11028251051902771,
+          -0.5945112705230713,
+          -0.402848482131958,
+          -0.9531654119491577,
+          -0.6731740236282349,
+          0.49189892411231995,
+          -0.15864109992980957,
+          -0.3418811559677124,
+          -0.9158143401145935,
+          -0.7206121683120728,
+          -0.7993468642234802,
+          0.6653800010681152,
+          0.03886038810014725,
+          0.5182055234909058,
+          -0.8742017149925232,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat two float16 3D tensors of same others dimensions except different 1st dimension along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [2, 1, 3],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [6, 1, 3],
+          "data": [
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float16"
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [8, 1, 3],
+        "data": [
+          -0.39453125,
+          0.86181640625,
+          0.337890625,
+          -0.99072265625,
+          0.57666015625,
+          0.32275390625,
+          -0.447265625,
+          0.11029052734375,
+          -0.5947265625,
+          -0.40283203125,
+          -0.953125,
+          -0.67333984375,
+          0.491943359375,
+          -0.15869140625,
+          -0.341796875,
+          -0.916015625,
+          -0.720703125,
+          -0.79931640625,
+          0.66552734375,
+          0.038848876953125,
+          0.51806640625,
+          -0.8740234375,
+          -0.47900390625,
+          0.12115478515625
+        ],
+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat four float32 3D tensors of same others dimensions except different 2nd dimension along axis 1",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [3, 1, 1],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [3, 2, 1],
+          "data": [
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input3",
+          "shape": [3, 2, 1],
+          "data": [
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input4",
+          "shape": [3, 3, 1],
+          "data": [
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float32"
+        }
+      ],
+      "axis": 1,
+      "expected": {
+        "name": "output",
+        "shape": [3, 8, 1],
+        "data": [
+          -0.3944413363933563,
+          -0.990639865398407,
+          0.576785683631897,
+          -0.402848482131958,
+          -0.9531654119491577,
+          -0.9158143401145935,
+          -0.7206121683120728,
+          -0.7993468642234802,
+          0.861982524394989,
+          0.32276400923728943,
+          -0.44735023379325867,
+          -0.6731740236282349,
+          0.49189892411231995,
+          0.6653800010681152,
+          0.03886038810014725,
+          0.5182055234909058,
+          0.337996244430542,
+          0.11028251051902771,
+          -0.5945112705230713,
+          -0.15864109992980957,
+          -0.3418811559677124,
+          -0.8742017149925232,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat four float16 3D tensors of same others dimensions except different 2nd dimension along axis 1",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [3, 1, 1],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [3, 2, 1],
+          "data": [
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input3",
+          "shape": [3, 2, 1],
+          "data": [
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input4",
+          "shape": [3, 3, 1],
+          "data": [
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float16"
+        }
+      ],
+      "axis": 1,
+      "expected": {
+        "name": "output",
+        "shape": [3, 8, 1],
+        "data": [
+          -0.39453125,
+          -0.99072265625,
+          0.57666015625,
+          -0.40283203125,
+          -0.953125,
+          -0.916015625,
+          -0.720703125,
+          -0.79931640625,
+          0.86181640625,
+          0.32275390625,
+          -0.447265625,
+          -0.67333984375,
+          0.491943359375,
+          0.66552734375,
+          0.038848876953125,
+          0.51806640625,
+          0.337890625,
+          0.11029052734375,
+          -0.5947265625,
+          -0.15869140625,
+          -0.341796875,
+          -0.8740234375,
+          -0.47900390625,
+          0.12115478515625
+        ],
+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat three float32 3D tensors of same shape along axis 2",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [2, 2, 2],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [2, 2, 2],
+          "data": [
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input3",
+          "shape": [2, 2, 2],
+          "data": [
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float32"
+        }
+      ],
+      "axis": 2,
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 6],
+        "data": [
+          -0.3944413363933563,
+          0.861982524394989,
+          -0.5945112705230713,
+          -0.402848482131958,
+          -0.7206121683120728,
+          -0.7993468642234802,
+          0.337996244430542,
+          -0.990639865398407,
+          -0.9531654119491577,
+          -0.6731740236282349,
+          0.6653800010681152,
+          0.03886038810014725,
+          0.576785683631897,
+          0.32276400923728943,
+          0.49189892411231995,
+          -0.15864109992980957,
+          0.5182055234909058,
+          -0.8742017149925232,
+          -0.44735023379325867,
+          0.11028251051902771,
+          -0.3418811559677124,
+          -0.9158143401145935,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat three float16 3D tensors of same shape along axis 2",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [2, 2, 2],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237,
+            -0.44735022799701873,
+            0.11028251232581932
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [2, 2, 2],
+          "data": [
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input3",
+          "shape": [2, 2, 2],
+          "data": [
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float16"
+        }
+      ],
+      "axis": 2,
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 6],
+        "data": [
+          -0.39453125,
+          0.86181640625,
+          -0.5947265625,
+          -0.40283203125,
+          -0.720703125,
+          -0.79931640625,
+          0.337890625,
+          -0.99072265625,
+          -0.953125,
+          -0.67333984375,
+          0.66552734375,
+          0.038848876953125,
+          0.57666015625,
+          0.32275390625,
+          0.491943359375,
+          -0.15869140625,
+          0.51806640625,
+          -0.8740234375,
+          -0.447265625,
+          0.11029052734375,
+          -0.341796875,
+          -0.916015625,
+          -0.47900390625,
+          0.12115478515625
+        ],
+        "type": "float16"
+      }
+    },
+    // concat 4D tensors
+    {
+      "name": "concat two float32 4D tensors of same others dimensions except different 1st dimension along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 3, 1, 2],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237
+          ],
+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [3, 3, 1, 2],
+          "data": [
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635,
+            0.6653799854931952,
+            0.03886038855553897,
+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
+            0.1211843166661235
+          ],
+          "type": "float32"
+        }
+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [4, 3, 1, 2],
+        "data": [
+          -0.3944413363933563,
+          0.861982524394989,
+          0.337996244430542,
+          -0.990639865398407,
+          0.576785683631897,
+          0.32276400923728943,
+          -0.44735023379325867,
+          0.11028251051902771,
+          -0.5945112705230713,
+          -0.402848482131958,
+          -0.9531654119491577,
+          -0.6731740236282349,
+          0.49189892411231995,
+          -0.15864109992980957,
+          -0.3418811559677124,
+          -0.9158143401145935,
+          -0.7206121683120728,
+          -0.7993468642234802,
+          0.6653800010681152,
+          0.03886038810014725,
+          0.5182055234909058,
+          -0.8742017149925232,
+          -0.4790218770503998,
+          0.1211843192577362
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat two float16 4D tensors of same others dimensions except different 1st dimension along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 3, 1, 2],
+          "data": [
+            -0.39444134019222243,
+            0.8619825316530809,
+            0.3379962524218807,
+            -0.9906398615400507,
+            0.576785657225761,
+            0.3227640108329237
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [3, 3, 1, 2],
+          "data": [
+            -0.44735022799701873,
+            0.11028251232581932,
+            -0.5945112749179908,
+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844,
+            0.4918989118791477,
+            -0.15864110312378976,
+            -0.34188115459083157,
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+          "type": "float16"
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+      ],
+      "axis": 0,
+      "expected": {
+        "name": "output",
+        "shape": [4, 3, 1, 2],
+        "data": [
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+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat three float32 4D tensors of same shape along axis 1",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [2, 2, 1, 2],
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+            -0.34188115459083157,
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+          "type": "float32"
+        },
+        {
+          "name": "input3",
+          "shape": [2, 2, 1, 2],
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+            -0.8742016938344297,
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+            0.1211843166661235
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+          "type": "float32"
+        }
+      ],
+      "axis": 1,
+      "expected": {
+        "name": "output",
+        "shape": [2, 6, 1, 2],
+        "data": [
+          -0.3944413363933563,
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+      }
+    },
+    {
+      "name": "concat three float16 4D tensors of same shape along axis 1",
+      "inputs": [
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+          "shape": [2, 2, 1, 2],
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+        {
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+            -0.34188115459083157,
+            -0.9158143500894873
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+          "type": "float16"
+        },
+        {
+          "name": "input3",
+          "shape": [2, 2, 1, 2],
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+            0.1211843166661235
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+          "type": "float16"
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+      ],
+      "axis": 1,
+      "expected": {
+        "name": "output",
+        "shape": [2, 6, 1, 2],
+        "data": [
+          -0.39453125,
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+          0.337890625,
+          -0.99072265625,
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+          -0.47900390625,
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+        ],
+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat three float32 4D tensors of same others dimensions except different 3rd dimension along axis 2",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 2, 2, 1],
+          "data": [
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+            -0.9906398615400507
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+        {
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+          "type": "float32"
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+        {
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+            0.1211843166661235
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+          "type": "float32"
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+      ],
+      "axis": 2,
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 12, 1],
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+    },
+    {
+      "name": "concat three float16 4D tensors of same others dimensions except different 3rd dimension along axis 2",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 2, 2, 1],
+          "data": [
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+        {
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+        },
+        {
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+      ],
+      "axis": 2,
+      "expected": {
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+        "shape": [1, 2, 12, 1],
+        "data": [
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+        "type": "float16"
+      }
+    },
+    {
+      "name": "concat four float32 4D tensors of same others dimensions except different 4th dimension along axis 3",
+      "inputs": [
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+          "name": "input1",
+          "shape": [1, 3, 1, 1],
+          "data": [
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+          "type": "float32"
+        },
+        {
+          "name": "input2",
+          "shape": [1, 3, 1, 1],
+          "data": [
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+            0.576785657225761,
+            0.3227640108329237
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+          "type": "float32"
+        },
+        {
+          "name": "input3",
+          "shape": [1, 3, 1, 2],
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+            -0.9531654171044694,
+            -0.6731740531810844
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+          "type": "float32"
+        },
+        {
+          "name": "input4",
+          "shape": [1, 3, 1, 4],
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+            0.1211843166661235
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+          "type": "float32"
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+      ],
+      "axis": 3,
+      "expected": {
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+        "shape": [1, 3, 1, 8],
+        "data": [
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat four float16 4D tensors of same others dimensions except different 4th dimension along axis 3",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 3, 1, 1],
+          "data": [
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+            0.8619825316530809,
+            0.3379962524218807
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+          "type": "float16"
+        },
+        {
+          "name": "input2",
+          "shape": [1, 3, 1, 1],
+          "data": [
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+            0.576785657225761,
+            0.3227640108329237
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input3",
+          "shape": [1, 3, 1, 2],
+          "data": [
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+            -0.9531654171044694,
+            -0.6731740531810844
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+          "type": "float16"
+        },
+        {
+          "name": "input4",
+          "shape": [1, 3, 1, 4],
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+            0.1211843166661235
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+          "type": "float16"
+        }
+      ],
+      "axis": 3,
+      "expected": {
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+        "shape": [1, 3, 1, 8],
+        "data": [
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+          -0.47900390625,
+          0.12115478515625
+        ],
+        "type": "float16"
+      }
+    },
+    // concat 5D tensors
+    {
+      "name": "concat four float32 5D tensors of same shape along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 2, 1, 1, 3],
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+          "type": "float32"
+        },
+        {
+          "name": "input3",
+          "shape": [1, 2, 1, 1, 3],
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+          "type": "float32"
+        },
+        {
+          "name": "input4",
+          "shape":[1, 2, 1, 1, 3],
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+          "type": "float32"
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+      ],
+      "axis": 0,
+      "expected": {
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+        "shape": [4, 2, 1, 1, 3],
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "concat four float16 5D tensors of same shape along axis 0",
+      "inputs": [
+        {
+          "name": "input1",
+          "shape": [1, 2, 1, 1, 3],
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+          "type": "float16"
+        },
+        {
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+            -0.40284849555754754,
+            -0.9531654171044694,
+            -0.6731740531810844
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+          "type": "float16"
+        },
+        {
+          "name": "input3",
+          "shape": [1, 2, 1, 1, 3],
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+            -0.15864110312378976,
+            -0.34188115459083157,
+            -0.9158143500894873,
+            -0.7206121708970712,
+            -0.7993468785008635
+          ],
+          "type": "float16"
+        },
+        {
+          "name": "input4",
+          "shape":[1, 2, 1, 1, 3],
+          "data": [
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+            0.5182055416768865,
+            -0.8742016938344297,
+            -0.479021891130635,
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+          "type": "float16"
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/conv2d.json b/testing/web-platform/tests/webnn/resources/test_data/conv2d.json
new file mode 100644
index 0000000000..5f8cd814a9
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/conv2d.json
@@ -0,0 +1,2213 @@
+{
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+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json b/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json
new file mode 100644
index 0000000000..42274e6fa3
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json
@@ -0,0 +1,2581 @@
+{
+  "tests": [
+    {
+      "name": "convTranspose2d float32 4D input and filter(non-constant) tensors default options",
+      "inputs": {
+        "input": {
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+      "expected": {
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+    {
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+      "name": "convTranspose2d float32 4D input and filter tensors, both negative input tensor and options.bias",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 3, 3],
+          "data": [
+            -0.1088905666228781,
+            -0.2980190376593821,
+            -0.39077856560563506,
+            -0.5624061411820445,
+            -0.732209362325523,
+            -0.8421320780728827,
+            -0.3059865397862138,
+            -0.9766599001467926,
+            -0.014158561610918996
+          ],
+          "type": "float32"
+        },
+        "filter": {
+          "shape": [1, 1, 3, 3],
+          "data": [
+            0.6161394585554596,
+            0.262246148184051,
+            0.7951397994036709,
+            0.87305609470791,
+            0.8309101763741125,
+            0.8549605393357718,
+            0.5552039743814143,
+            0.8400921243865438,
+            0.8530843869413023
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "bias": {
+          "shape": [1],
+          "data": [
+            -0.8457866242505638
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "shape": [1, 1, 5, 5],
+        "data": [
+          -0.9128783941268921,
+          -1.0579640865325928,
+          -1.2512983083724976,
+          -1.1852335929870605,
+          -1.1565102338790894,
+          -1.2873748540878296,
+          -1.7950842380523682,
+          -2.6857638359069824,
+          -2.2283377647399902,
+          -1.8494995832443237,
+          -1.5857856273651123,
+          -2.8912975788116455,
+          -3.738619565963745,
+          -3.5343525409698486,
+          -1.910401463508606,
+          -1.425180196762085,
+          -2.8317112922668457,
+          -3.49372935295105,
+          -3.0246617794036865,
+          -1.5763013362884521,
+          -1.0156716108322144,
+          -1.645089030265808,
+          -1.935164213180542,
+          -1.6908544301986694,
+          -0.8578650951385498
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "convTranspose2d float32 4D input and filter tensors options.activation=relu",
+      "inputs": {
+        "input": {
+          "shape": [1, 1, 3, 3],
+          "data": [
+            0.11095754619056808,
+            0.8681362056660518,
+            0.7342095941654914,
+            0.43077004493855875,
+            0.5981626867187566,
+            0.1232164990736122,
+            0.16109748910776922,
+            0.08840264384189989,
+            0.2910097194319563
+          ],
+          "type": "float32"
+        },
+        "filter": {
+          "shape": [1, 1, 3, 3],
+          "data": [
+            0.6161394585554596,
+            0.262246148184051,
+            0.7951397994036709,
+            0.87305609470791,
+            0.8309101763741125,
+            0.8549605393357718,
+            0.5552039743814143,
+            0.8400921243865438,
+            0.8530843869413023
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "activation": "relu" // relu operator
+      },
+      "expected": {
+        "shape": [1, 1, 5, 5],
+        "data": [
+          0.06836532801389694,
+          0.5639911890029907,
+          0.7682676911354065,
+          0.8828333020210266,
+          0.5837992429733276,
+          0.36228659749031067,
+          1.3316468000411987,
+          2.0325205326080322,
+          1.8602205514907837,
+          0.7256945967674255,
+          0.5369489789009094,
+          1.5520836114883423,
+          2.5350732803344727,
+          2.117793083190918,
+          0.963081419467926,
+          0.3798123896121979,
+          0.9050268530845642,
+          1.403659701347351,
+          0.9311801791191101,
+          0.35391589999198914,
+          0.08944196254014969,
+          0.1844182312488556,
+          0.3732658624649048,
+          0.3198899030685425,
+          0.24825584888458252
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/cos.json b/testing/web-platform/tests/webnn/resources/test_data/cos.json
new file mode 100644
index 0000000000..db7ade11e3
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/cos.json
@@ -0,0 +1,401 @@
+{
+  "tests": [
+    {
+      "name": "cos float32 1D constant tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            85.56368722678593,
+            -45.09983640817779,
+            -94.6775036804249,
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+            -83.00758001757269,
+            61.51649571682452,
+            -32.51877900985289,
+            -48.376587517032775,
+            -58.02735967650854,
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+            -76.14168597373077,
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+            -98.67289449592316,
+            -63.611559926077675,
+            26.857246203003186,
+            83.70417131490925,
+            76.56606838780263,
+            -47.83436545380422
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.7380040884017944,
+          0.43789437413215637,
+          0.9090799689292908,
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+          0.4510514736175537,
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+          0.09197491407394409,
+          -0.2537800967693329,
+          -0.9583188891410828,
+          -0.11282006651163101,
+          0.736129879951477,
+          -0.80721116065979,
+          -0.4045141637325287,
+          -0.283336341381073,
+          0.7111190557479858,
+          -0.1531042903661728,
+          -0.43673399090766907,
+          0.39213326573371887,
+          -0.7580515146255493
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "cos float32 1D tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            85.56368722678593,
+            -45.09983640817779,
+            -94.6775036804249,
+            83.49029219662862,
+            -31.367904335227692,
+            70.1804254973957,
+            -90.36229826898601,
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+            26.857246203003186,
+            83.70417131490925,
+            76.56606838780263,
+            -47.83436545380422
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.7380040884017944,
+          0.43789437413215637,
+          0.9090799689292908,
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+          0.25266921520233154,
+          0.4510514736175537,
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+          0.09197491407394409,
+          -0.2537800967693329,
+          -0.9583188891410828,
+          -0.11282006651163101,
+          0.736129879951477,
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+          -0.4045141637325287,
+          -0.283336341381073,
+          0.7111190557479858,
+          -0.1531042903661728,
+          -0.43673399090766907,
+          0.39213326573371887,
+          -0.7580515146255493
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "cos float32 2D tensor",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            85.56368722678593,
+            -45.09983640817779,
+            -94.6775036804249,
+            83.49029219662862,
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+            -83.00758001757269,
+            61.51649571682452,
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+            -59.05887786348942,
+            77.38546821194151,
+            -98.67289449592316,
+            -63.611559926077675,
+            26.857246203003186,
+            83.70417131490925,
+            76.56606838780263,
+            -47.83436545380422
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          -0.7380040884017944,
+          0.43789437413215637,
+          0.9090799689292908,
+          -0.23584702610969543,
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+          0.48416373133659363,
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+          0.4510514736175537,
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+          0.09197491407394409,
+          -0.2537800967693329,
+          -0.9583188891410828,
+          -0.11282006651163101,
+          0.736129879951477,
+          -0.80721116065979,
+          -0.4045141637325287,
+          -0.283336341381073,
+          0.7111190557479858,
+          -0.1531042903661728,
+          -0.43673399090766907,
+          0.39213326573371887,
+          -0.7580515146255493
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "cos float32 3D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            85.56368722678593,
+            -45.09983640817779,
+            -94.6775036804249,
+            83.49029219662862,
+            -31.367904335227692,
+            70.1804254973957,
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+            26.857246203003186,
+            83.70417131490925,
+            76.56606838780263,
+            -47.83436545380422
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          -0.7380040884017944,
+          0.43789437413215637,
+          0.9090799689292908,
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+          0.24218930304050446,
+          0.25266921520233154,
+          0.4510514736175537,
+          -0.31276169419288635,
+          0.09197491407394409,
+          -0.2537800967693329,
+          -0.9583188891410828,
+          -0.11282006651163101,
+          0.736129879951477,
+          -0.80721116065979,
+          -0.4045141637325287,
+          -0.283336341381073,
+          0.7111190557479858,
+          -0.1531042903661728,
+          -0.43673399090766907,
+          0.39213326573371887,
+          -0.7580515146255493
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "cos float32 4D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            85.56368722678593,
+            -45.09983640817779,
+            -94.6775036804249,
+            83.49029219662862,
+            -31.367904335227692,
+            70.1804254973957,
+            -90.36229826898601,
+            -83.00758001757269,
+            61.51649571682452,
+            -32.51877900985289,
+            -48.376587517032775,
+            -58.02735967650854,
+            89.79197789644661,
+            -84.53326629430444,
+            -58.23252607768894,
+            -76.14168597373077,
+            -59.05887786348942,
+            77.38546821194151,
+            -98.67289449592316,
+            -63.611559926077675,
+            26.857246203003186,
+            83.70417131490925,
+            76.56606838780263,
+            -47.83436545380422
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          -0.7380040884017944,
+          0.43789437413215637,
+          0.9090799689292908,
+          -0.23584702610969543,
+          0.9988471865653992,
+          0.48416373133659363,
+          -0.7358400821685791,
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+          -0.4045141637325287,
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+          0.7111190557479858,
+          -0.1531042903661728,
+          -0.43673399090766907,
+          0.39213326573371887,
+          -0.7580515146255493
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "cos float32 5D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            85.56368722678593,
+            -45.09983640817779,
+            -94.6775036804249,
+            83.49029219662862,
+            -31.367904335227692,
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+            -48.376587517032775,
+            -58.02735967650854,
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+            -58.23252607768894,
+            -76.14168597373077,
+            -59.05887786348942,
+            77.38546821194151,
+            -98.67289449592316,
+            -63.611559926077675,
+            26.857246203003186,
+            83.70417131490925,
+            76.56606838780263,
+            -47.83436545380422
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 1, 3],
+        "data": [
+          -0.7380040884017944,
+          0.43789437413215637,
+          0.9090799689292908,
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+          0.9988471865653992,
+          0.48416373133659363,
+          -0.7358400821685791,
+          0.24218930304050446,
+          0.25266921520233154,
+          0.4510514736175537,
+          -0.31276169419288635,
+          0.09197491407394409,
+          -0.2537800967693329,
+          -0.9583188891410828,
+          -0.11282006651163101,
+          0.736129879951477,
+          -0.80721116065979,
+          -0.4045141637325287,
+          -0.283336341381073,
+          0.7111190557479858,
+          -0.1531042903661728,
+          -0.43673399090766907,
+          0.39213326573371887,
+          -0.7580515146255493
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/div.json b/testing/web-platform/tests/webnn/resources/test_data/div.json
new file mode 100644
index 0000000000..a60341181d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/div.json
@@ -0,0 +1,882 @@
+{
+  "tests": [
+    {
+      "name": "div float32 1D constant tensors",
+      "inputs": {
+        "a": {
+          "shape": [24],
+          "data": [
+            60.42374184854583,
+            -86.92247771592565,
+            -19.496113538677307,
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+            -3.275369300234928,
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+            44.15916607007162,
+            -13.083211898368276,
+            44.565997906464105,
+            -34.892785072690245,
+            -74.09375203216162
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        "b": {
+          "shape": [24],
+          "data": [
+            -95.02903333542001,
+            62.804865007594174,
+            -85.32865597192423,
+            -68.20919717822437,
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+            -10.000411029009541,
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+            -5.957828048196006,
+            89.49882849628409,
+            94.95791079054848,
+            -79.00059056550418,
+            -79.87597038424425,
+            74.997874802068,
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+            48.214831959279195,
+            63.37012091471408,
+            10.62638472655793,
+            46.12662564540156,
+            77.2232730204774
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.6358450651168823,
+          -1.3840086460113525,
+          0.22848258912563324,
+          0.22211983799934387,
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+          -0.6614248752593994,
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+          -7.071591854095459,
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+          -0.9594743251800537
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+        "type": "float32"
+      }
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+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 1],
+          "data": [
+            75.08295083033457,
+            -46.22666869833023,
+            15.761880659498928,
+            8.922299330668352
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          0.8047597408294678,
+          -1.1576858758926392,
+          -0.2596609592437744,
+          0.3277462124824524,
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+          3.8753535747528076,
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+          -3.910738945007324,
+          -8.304333686828613
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "div float32 broadcast 4D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [1, 1, 1, 1],
+          "data": [
+            -41.8274145895075
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            60.42374184854583,
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+            -19.496113538677307,
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+            44.565997906464105,
+            -34.892785072690245,
+            -74.09375203216162
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          -0.6922348141670227,
+          0.48120367527008057,
+          2.145423412322998,
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+          -0.5914608240127563,
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+          -1.0726968050003052,
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+          1.19874107837677,
+          0.5645201802253723
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/elu.json b/testing/web-platform/tests/webnn/resources/test_data/elu.json
new file mode 100644
index 0000000000..b1c443282e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/elu.json
@@ -0,0 +1,539 @@
+{ // elu: The calculation follows the expression max(0, x) + alpha * (exp(min(0, x)) - 1).
+  "tests": [
+    {
+      "name": "elu float32 1D constant tensor default options", // default options: {alpha: 1}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            4.721739616402454,
+            0.37687805459885837,
+            1.4189998297338988,
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+            -3.8641844556612392,
+            7.839725573639406,
+            -6.690801377003339,
+            0.545640671378024,
+            5.776711453839759,
+            7.263273186498619,
+            -7.419948093040434,
+            5.665064356187175,
+            -6.712906923265587,
+            -3.3348948531721945,
+            -1.2103675328053143,
+            7.255546982014842,
+            8.903468527480992,
+            -4.019860311974934,
+            7.114678383091487,
+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          4.721739768981934,
+          0.3768780529499054,
+          1.4189997911453247,
+          -0.9790657162666321,
+          1.3590080738067627,
+          -0.9790199995040894,
+          7.839725494384766,
+          -0.9987577199935913,
+          0.5456406474113464,
+          5.776711463928223,
+          7.263273239135742,
+          -0.999400794506073,
+          5.665064334869385,
+          -0.9987848997116089,
+          -0.9643816947937012,
+          -0.7019122838973999,
+          7.255547046661377,
+          8.903468132019043,
+          -0.982044517993927,
+          7.114678382873535,
+          -0.10607059299945831,
+          -0.9749990105628967,
+          6.135150909423828,
+          -0.99994957447052
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 1D tensor default options", // default options: {alpha: 1}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            4.721739616402454,
+            0.37687805459885837,
+            1.4189998297338988,
+            -3.866366516102673,
+            1.3590080831697868,
+            -3.8641844556612392,
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+            -6.690801377003339,
+            0.545640671378024,
+            5.776711453839759,
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+            -6.712906923265587,
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+            -1.2103675328053143,
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+            8.903468527480992,
+            -4.019860311974934,
+            7.114678383091487,
+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          4.721739768981934,
+          0.3768780529499054,
+          1.4189997911453247,
+          -0.9790657162666321,
+          1.3590080738067627,
+          -0.9790199995040894,
+          7.839725494384766,
+          -0.9987577199935913,
+          0.5456406474113464,
+          5.776711463928223,
+          7.263273239135742,
+          -0.999400794506073,
+          5.665064334869385,
+          -0.9987848997116089,
+          -0.9643816947937012,
+          -0.7019122838973999,
+          7.255547046661377,
+          8.903468132019043,
+          -0.982044517993927,
+          7.114678382873535,
+          -0.10607059299945831,
+          -0.9749990105628967,
+          6.135150909423828,
+          -0.99994957447052
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 2D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            4.721739616402454,
+            0.37687805459885837,
+            1.4189998297338988,
+            -3.866366516102673,
+            1.3590080831697868,
+            -3.8641844556612392,
+            7.839725573639406,
+            -6.690801377003339,
+            0.545640671378024,
+            5.776711453839759,
+            7.263273186498619,
+            -7.419948093040434,
+            5.665064356187175,
+            -6.712906923265587,
+            -3.3348948531721945,
+            -1.2103675328053143,
+            7.255546982014842,
+            8.903468527480992,
+            -4.019860311974934,
+            7.114678383091487,
+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
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+          0.3768780529499054,
+          1.4189997911453247,
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+          -0.9749990105628967,
+          6.135150909423828,
+          -0.99994957447052
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 3D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            4.721739616402454,
+            0.37687805459885837,
+            1.4189998297338988,
+            -3.866366516102673,
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+            -3.8641844556612392,
+            7.839725573639406,
+            -6.690801377003339,
+            0.545640671378024,
+            5.776711453839759,
+            7.263273186498619,
+            -7.419948093040434,
+            5.665064356187175,
+            -6.712906923265587,
+            -3.3348948531721945,
+            -1.2103675328053143,
+            7.255546982014842,
+            8.903468527480992,
+            -4.019860311974934,
+            7.114678383091487,
+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          4.721739768981934,
+          0.3768780529499054,
+          1.4189997911453247,
+          -0.9790657162666321,
+          1.3590080738067627,
+          -0.9790199995040894,
+          7.839725494384766,
+          -0.9987577199935913,
+          0.5456406474113464,
+          5.776711463928223,
+          7.263273239135742,
+          -0.999400794506073,
+          5.665064334869385,
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+          -0.9643816947937012,
+          -0.7019122838973999,
+          7.255547046661377,
+          8.903468132019043,
+          -0.982044517993927,
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+          6.135150909423828,
+          -0.99994957447052
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 4D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
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+            0.37687805459885837,
+            1.4189998297338988,
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+            -3.8641844556612392,
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+            5.776711453839759,
+            7.263273186498619,
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+            -3.3348948531721945,
+            -1.2103675328053143,
+            7.255546982014842,
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+            -4.019860311974934,
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+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
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+          0.3768780529499054,
+          1.4189997911453247,
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+          -0.9749990105628967,
+          6.135150909423828,
+          -0.99994957447052
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 5D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 1, 3, 4],
+          "data": [
+            4.721739616402454,
+            0.37687805459885837,
+            1.4189998297338988,
+            -3.866366516102673,
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+            -3.8641844556612392,
+            7.839725573639406,
+            -6.690801377003339,
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+            6.135151096361078,
+            -9.895182558358613
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 1, 3, 4],
+        "data": [
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 4D tensor positive options.alpha",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
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+            -7.419948093040434,
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+            -6.712906923265587,
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+            8.903468527480992,
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+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "alpha": 0.3607245505146506
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "elu float32 4D tensor negative options.alpha",
+        "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
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+            0.37687805459885837,
+            1.4189998297338988,
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+            -4.019860311974934,
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+            -0.11212847159184136,
+            -3.688840965850466,
+            6.135151096361078,
+            -9.895182558358613
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "alpha": -3.468180406374035
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/equal.json b/testing/web-platform/tests/webnn/resources/test_data/equal.json
new file mode 100644
index 0000000000..bbd9143a03
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/equal.json
@@ -0,0 +1,982 @@
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/erf.json b/testing/web-platform/tests/webnn/resources/test_data/erf.json
new file mode 100644
index 0000000000..6f0b550cf7
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/erf.json
@@ -0,0 +1,421 @@
+{
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/exp.json b/testing/web-platform/tests/webnn/resources/test_data/exp.json
new file mode 100644
index 0000000000..2de52d951e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/exp.json
@@ -0,0 +1,401 @@
+{
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/expand.json b/testing/web-platform/tests/webnn/resources/test_data/expand.json
new file mode 100644
index 0000000000..00bf4a37b4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/expand.json
@@ -0,0 +1,994 @@
+{
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+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/floor.json b/testing/web-platform/tests/webnn/resources/test_data/floor.json
new file mode 100644
index 0000000000..3b5dba1576
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/floor.json
@@ -0,0 +1,401 @@
+{
+  "tests": [
+    {
+      "name": "floor float32 1D constant tensor",
+      "inputs": {
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/gather.json b/testing/web-platform/tests/webnn/resources/test_data/gather.json
new file mode 100644
index 0000000000..a01654637f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/gather.json
@@ -0,0 +1,1268 @@
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+          0.9129875898361206,
+          -7.699817180633545,
+          25.76774024963379,
+          25.76774024963379,
+          25.76774024963379,
+          73.60064697265625
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gather float32 4D tensor and 2D indices explict options.axis=0",
+      "inputs": {
+        "input": {
+          "shape": [8, 1, 1, 3],
+          "data": [
+            -66.05901336669922,
+            -68.9197006225586,
+            -77.02045440673828,
+            -26.158037185668945,
+            89.0337142944336,
+            -45.89653396606445,
+            43.84803771972656,
+            48.81806945800781,
+            51.79948425292969,
+            41.94132614135742,
+            -1.1303654909133911,
+            -50.42131042480469,
+            90.2870101928711,
+            55.620765686035156,
+            44.92119598388672,
+            56.828636169433594,
+            10.829925537109375,
+            -19.693084716796875,
+            -37.696800231933594,
+            43.11057662963867,
+            0.9129875898361206,
+            -7.699817180633545,
+            25.76774024963379,
+            73.60064697265625
+          ],
+          "type": "float32"
+        },
+        "indices": {
+          "shape": [2, 2],
+          "data": [
+            0,
+            0,
+            7,
+            4
+          ],
+          "type": "int64"
+        }
+      },
+      "options": {
+        "axis": 0
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 1, 1, 3],
+        "data": [
+          -66.05901336669922,
+          -68.9197006225586,
+          -77.02045440673828,
+          -66.05901336669922,
+          -68.9197006225586,
+          -77.02045440673828,
+          -7.699817180633545,
+          25.76774024963379,
+          73.60064697265625,
+          90.2870101928711,
+          55.620765686035156,
+          44.92119598388672
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gather float32 5D tensor and 0D scalar indices options.axis=4",
+      "inputs": {
+        "input": {
+          "shape": [4, 2, 1, 1, 3],
+          "data": [
+            -66.05901336669922,
+            -68.9197006225586,
+            -77.02045440673828,
+            -26.158037185668945,
+            89.0337142944336,
+            -45.89653396606445,
+            43.84803771972656,
+            48.81806945800781,
+            51.79948425292969,
+            41.94132614135742,
+            -1.1303654909133911,
+            -50.42131042480469,
+            90.2870101928711,
+            55.620765686035156,
+            44.92119598388672,
+            56.828636169433594,
+            10.829925537109375,
+            -19.693084716796875,
+            -37.696800231933594,
+            43.11057662963867,
+            0.9129875898361206,
+            -7.699817180633545,
+            25.76774024963379,
+            73.60064697265625
+          ],
+          "type": "float32"
+        },
+        "indices": {
+          "shape": [],
+          "data": [
+            1
+          ],
+          "type": "int64"
+        }
+      },
+      "options": {
+        "axis": 4
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 2, 1, 1],
+        "data": [
+          -68.9197006225586,
+          89.0337142944336,
+          48.81806945800781,
+          -1.1303654909133911,
+          55.620765686035156,
+          10.829925537109375,
+          43.11057662963867,
+          25.76774024963379
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/gemm.json b/testing/web-platform/tests/webnn/resources/test_data/gemm.json
new file mode 100644
index 0000000000..23a18b316c
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/gemm.json
@@ -0,0 +1,1953 @@
+{
+  "tests": [
+    {
+      "name": "gemm two float32 2D tensors (b is non-constant) default options",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
+            25.14739246484985,
+            66.69238549579231,
+            63.29909902139696,
+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8161.14697265625,
+          14025.82421875,
+          14607.5009765625,
+          14048.5322265625,
+          18757.09765625,
+          11892.5107421875,
+          14659.9755859375,
+          19125.74609375,
+          15034.27734375,
+          18273.080078125,
+          5516.6904296875,
+          9259.267578125,
+          9167.3916015625,
+          6347.330078125,
+          10868.6025390625
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm two float32 2D tensors default options",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
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+            63.29909902139696,
+            10.629964783998469,
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+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8161.14697265625,
+          14025.82421875,
+          14607.5009765625,
+          14048.5322265625,
+          18757.09765625,
+          11892.5107421875,
+          14659.9755859375,
+          19125.74609375,
+          15034.27734375,
+          18273.080078125,
+          5516.6904296875,
+          9259.267578125,
+          9167.3916015625,
+          6347.330078125,
+          10868.6025390625
+        ],
+        "type": "float32"
+      }
+    },
+    // options.c
+    {
+      "name": "gemm two float32 2D constant tensors options.c",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
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+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
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+            66.69238549579231,
+            63.29909902139696,
+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
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+            3.0366680571384252,
+            93.14021782678614,
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+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
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+            91.38520749770859,
+            12.651897708721549,
+            20.12200327935334,
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+            13.33466578964102,
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+            30.577471042677097,
+            69.47062151883239,
+            7.166217909076766,
+            28.337108113083744,
+            90.69412588106762,
+            71.23025762837965
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
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+          14117.208984375,
+          14620.15234375,
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+          6438.0244140625,
+          10939.8330078125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm two float32 2D tensors options.c",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
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+            90.51641254722982,
+            59.6385201162642,
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+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
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+            66.69238549579231,
+            63.29909902139696,
+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
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+            91.38520749770859,
+            12.651897708721549,
+            20.12200327935334,
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+            35.2024171023056,
+            13.33466578964102,
+            50.785469741049205,
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+            7.166217909076766,
+            28.337108113083744,
+            90.69412588106762,
+            71.23025762837965
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8243.9189453125,
+          14117.208984375,
+          14620.15234375,
+          14068.654296875,
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+          15122.8896484375,
+          18303.658203125,
+          5586.16064453125,
+          9266.43359375,
+          9195.7294921875,
+          6438.0244140625,
+          10939.8330078125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm two float32 2D tensors broadcast options.c [1, 5] => [3, 5]",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
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+            43.89479290862091,
+            98.89487912764056,
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+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
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+            66.69238549579231,
+            63.29909902139696,
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+            93.14021782678614,
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+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
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+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [1, 5],
+          "data": [
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+            39.375354775876346,
+            50.94260262706696,
+            31.87430758223413,
+            31.210525151769097
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8247.0546875,
+          14065.19921875,
+          14658.443359375,
+          14080.40625,
+          18788.30859375,
+          11978.4189453125,
+          14699.3505859375,
+          19176.689453125,
+          15066.1513671875,
+          18304.291015625,
+          5602.5986328125,
+          9298.642578125,
+          9218.3349609375,
+          6379.20458984375,
+          10899.8125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm two float32 2D tensors broadcast options.c [3, 1] => [3, 5]",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
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+            66.69238549579231,
+            63.29909902139696,
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+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 1],
+          "data": [
+            6.2025197606838445,
+            81.40642123187278,
+            73.00516839070796
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8167.349609375,
+          14032.0263671875,
+          14613.703125,
+          14054.734375,
+          18763.30078125,
+          11973.9169921875,
+          14741.3818359375,
+          19207.154296875,
+          15115.68359375,
+          18354.486328125,
+          5589.6953125,
+          9332.2724609375,
+          9240.3974609375,
+          6420.33544921875,
+          10941.607421875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm two float32 2D tensors broadcast options.c [1, 1] => [3, 5]",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
+            25.14739246484985,
+            66.69238549579231,
+            63.29909902139696,
+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [1, 1],
+          "data": [18.78192846614879],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8179.9287109375,
+          14044.6064453125,
+          14626.2822265625,
+          14067.314453125,
+          18775.87890625,
+          11911.29296875,
+          14678.7578125,
+          19144.529296875,
+          15053.0595703125,
+          18291.86328125,
+          5535.47216796875,
+          9278.048828125,
+          9186.173828125,
+          6366.1123046875,
+          10887.384765625
+        ],
+        "type": "float32"
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+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "bTranspose": false
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8161.14697265625,
+          14025.82421875,
+          14607.5009765625,
+          14048.5322265625,
+          18757.09765625,
+          11892.5107421875,
+          14659.9755859375,
+          19125.74609375,
+          15034.27734375,
+          18273.080078125,
+          5516.6904296875,
+          9259.267578125,
+          9167.3916015625,
+          6347.330078125,
+          10868.6025390625
+        ],
+        "type": "float32"
+      }
+    },
+    // alpha * A x B + beta * C
+    {
+      "name": "gemm two float32 2D tensors all options", // All alpha, A, B, beta and C are positive
+      "inputs": {
+        "a": {
+          "shape": [4, 3],
+          "data": [
+            82.98884511142329,
+            18.96485038946509,
+            50.51683887619669,
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+            36.27187180381433,
+            91.46013741088626,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [5, 4],
+          "data": [
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+            0.003725653326558387,
+            75.74880854000601,
+            42.90679144336773,
+            66.69238549579231,
+            16.499146388625483,
+            96.6068806977233,
+            34.08055187293266,
+            63.29909902139696,
+            3.0366680571384252,
+            99.10042039275365,
+            87.37654561544477,
+            10.629964783998469,
+            93.14021782678614,
+            23.243775283337477,
+            92.34209530787089,
+            61.32737573972831,
+            70.08265680040718,
+            86.1185682132947,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
+            82.77202028708963,
+            91.38520749770859,
+            12.651897708721549,
+            20.12200327935334,
+            68.51224585053419,
+            35.2024171023056,
+            13.33466578964102,
+            50.785469741049205,
+            88.61195160882922,
+            30.577471042677097,
+            69.47062151883239,
+            7.166217909076766,
+            28.337108113083744,
+            90.69412588106762,
+            71.23025762837965
+          ],
+          "type": "float32"
+        },
+        "alpha": 74.43204170482103,
+        "beta": 62.01828598608989,
+        "aTranspose": true,
+        "bTranspose": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          612584.25,
+          1049638.375,
+          1088050.75,
+          1046908.875,
+          1400378.125,
+          887367.0625,
+          1091999,
+          1426718.125,
+          1124527.625,
+          1361999.125,
+          414927,
+          689630.625,
+          684105.1875,
+          478069.46875,
+          813389.875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm both negative options.alpha and 1st float32 input tensor",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            -87.94973451710236,
+            -14.765121645895519,
+            -80.86310680534218,
+            -59.27638399609938,
+            -28.748250806066665,
+            -9.038779559502402,
+            -76.04662906780386,
+            -83.94807592959552,
+            -95.02689722012639,
+            -47.897331378960686,
+            -69.01161938187514,
+            -86.66899315604435
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
+            25.14739246484985,
+            66.69238549579231,
+            63.29909902139696,
+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
+            82.77202028708963,
+            91.38520749770859,
+            12.651897708721549,
+            20.12200327935334,
+            68.51224585053419,
+            35.2024171023056,
+            13.33466578964102,
+            50.785469741049205,
+            88.61195160882922,
+            30.577471042677097,
+            69.47062151883239,
+            7.166217909076766,
+            28.337108113083744,
+            90.69412588106762,
+            71.23025762837965
+          ],
+          "type": "float32"
+        },
+        "alpha": -22.611149749186296
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          246101.234375,
+          360542.34375,
+          425213.25,
+          218520.65625,
+          383734.40625,
+          228076.8125,
+          277543.625,
+          378076,
+          241282.15625,
+          316800.71875,
+          256391.5625,
+          378711.34375,
+          465195.8125,
+          341034.875,
+          460338.3125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm both negative options.alpha and 2nd float32 input tensor",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
+            -64.35197380859643,
+            -87.64981186480712,
+            -96.04495397065658,
+            -31.07155980709375,
+            -6.548066933465876,
+            -64.64146348646094,
+            -14.020616857474465,
+            -88.93306726614543,
+            -24.17941082566574,
+            -97.31866304932058,
+            -93.46081221136554,
+            -39.49039520100528,
+            -94.37982420323672,
+            -75.8333604154835,
+            -11.403324388031308,
+            -26.28259194817595,
+            -56.237981538646295,
+            -46.474048229320665,
+            -89.20683997562287,
+            -53.49532488423292
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
+            82.77202028708963,
+            91.38520749770859,
+            12.651897708721549,
+            20.12200327935334,
+            68.51224585053419,
+            35.2024171023056,
+            13.33466578964102,
+            50.785469741049205,
+            88.61195160882922,
+            30.577471042677097,
+            69.47062151883239,
+            7.166217909076766,
+            28.337108113083744,
+            90.69412588106762,
+            71.23025762837965
+          ],
+          "type": "float32"
+        },
+        "alpha": -22.611149749186296
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          400725.03125,
+          292635.75,
+          527642.375,
+          283236.40625,
+          270787.40625,
+          355130.96875,
+          256121.421875,
+          436658.0625,
+          391465.40625,
+          235557.03125,
+          245540.5625,
+          170154.125,
+          309239.4375,
+          163583.015625,
+          120958.7578125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm both negative options.beta and 3rd float32 input tensor (options.c)",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
+            43.89479290862091,
+            98.89487912764056,
+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
+            25.14739246484985,
+            66.69238549579231,
+            63.29909902139696,
+            10.629964783998469,
+            61.32737573972831,
+            0.003725653326558387,
+            16.499146388625483,
+            3.0366680571384252,
+            93.14021782678614,
+            70.08265680040718,
+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
+            42.90679144336773,
+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
+            -42.66268253622787,
+            -93.66379731044412,
+            -40.7084931979834,
+            -45.060979792171786,
+            -87.90339205403255,
+            -71.16189172949525,
+            -85.56422647952868,
+            -10.049133962829984,
+            -46.105405162474256,
+            -66.76168168609703,
+            -25.392492019110875,
+            -65.6298788225413,
+            -40.50155350367264,
+            -81.5303936185148,
+            -41.396294482178405
+          ],
+          "type": "float32"
+        },
+        "beta": -11.68521964935509
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          8659.669921875,
+          15120.3056640625,
+          15083.1884765625,
+          14575.0791015625,
+          19784.267578125,
+          12724.052734375,
+          15659.8125,
+          19243.173828125,
+          15573.029296875,
+          19053.205078125,
+          5813.4072265625,
+          10026.1669921875,
+          9640.6611328125,
+          7300.03076171875,
+          11352.3271484375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm both negative options.alpha and options.beta",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
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+            98.89487912764056,
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+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
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+            66.69238549579231,
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+            10.629964783998469,
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+            16.499146388625483,
+            3.0366680571384252,
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+            75.74880854000601,
+            96.6068806977233,
+            99.10042039275365,
+            23.243775283337477,
+            86.1185682132947,
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+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
+            82.77202028708963,
+            91.38520749770859,
+            12.651897708721549,
+            20.12200327935334,
+            68.51224585053419,
+            35.2024171023056,
+            13.33466578964102,
+            50.785469741049205,
+            88.61195160882922,
+            30.577471042677097,
+            69.47062151883239,
+            7.166217909076766,
+            28.337108113083744,
+            90.69412588106762,
+            71.23025762837965
+          ],
+          "type": "float32"
+        },
+        "alpha": -22.611149749186296,
+        "beta": -11.68521964935509
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          -185500.109375,
+          -318207.84375,
+          -330440.21875,
+          -317888.59375,
+          -424920.125,
+          -269314.6875,
+          -331634.71875,
+          -433048.5625,
+          -340977.71875,
+          -413532.65625,
+          -125550.484375,
+          -209446.40625,
+          -207616.390625,
+          -144580.21875,
+          -246583.921875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "gemm both negative options.alpha and 3rd float32 input tensor (options.c)",
+      "inputs": {
+        "a": {
+          "shape": [3, 4],
+          "data": [
+            82.98884511142329,
+            90.51641254722982,
+            59.6385201162642,
+            36.27187180381433,
+            18.96485038946509,
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+            91.46013741088626,
+            50.51683887619669,
+            40.45679629199866,
+            50.76741460760452,
+            9.336554329198155
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [4, 5],
+          "data": [
+            25.14739246484985,
+            66.69238549579231,
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+            10.629964783998469,
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+            34.08055187293266,
+            87.37654561544477,
+            92.34209530787089,
+            60.32208994273669
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "options": {
+        "c": {
+          "shape": [3, 5],
+          "data": [
+            -42.66268253622787,
+            -93.66379731044412,
+            -40.7084931979834,
+            -45.060979792171786,
+            -87.90339205403255,
+            -71.16189172949525,
+            -85.56422647952868,
+            -10.049133962829984,
+            -46.105405162474256,
+            -66.76168168609703,
+            -25.392492019110875,
+            -65.6298788225413,
+            -40.50155350367264,
+            -81.5303936185148,
+            -41.396294482178405
+          ],
+          "type": "float32"
+        },
+        "alpha": -22.611149749186296
+      },
+      "expected": {
+        "name": "output",
+        "shape": [3, 5],
+        "data": [
+          -184575.5625,
+          -317233.65625,
+          -330333.09375,
+          -317698.5,
+          -424207.4375,
+          -268974.5,
+          -331564.4375,
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+          -339988.375,
+          -413242.09375,
+          -124764.09375,
+          -209428.296875,
+          -207325.765625,
+          -143601.96875,
+          -245792.984375
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/greater.json b/testing/web-platform/tests/webnn/resources/test_data/greater.json
new file mode 100644
index 0000000000..d7dc34d18b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/greater.json
@@ -0,0 +1,982 @@
+{
+  "tests": [
+    {
+      "name": "greater float32 0D scalar",
+      "inputs": {
+        "a": {
+          "shape": [],
+          "data": [
+            3.6851014232119716
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [],
+          "data": [
+            1.7231997782518338
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [],
+        "data": [
+          1
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "greater float32 1D constant tensors",
+      "inputs": {
+        "a": {
+          "shape": [24],
+          "data": [
+            -5.394711458674926,
+            -7.189248660357093,
+            -3.108176577372168,
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+            -6.254901877789392,
+            4.40903450389083,
+            -6.775286805113776
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        "b": {
+          "shape": [24],
+          "data": [
+            -6.155234276085855,
+            -4.023341341511664,
+            5.9525983178014386,
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/greater_or_equal.json b/testing/web-platform/tests/webnn/resources/test_data/greater_or_equal.json
new file mode 100644
index 0000000000..e47ac4350a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/greater_or_equal.json
@@ -0,0 +1,970 @@
+{
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+            -5.068354954162224,
+            1.5821936652025705,
+            5.675583814922739,
+            -4.34159498166756,
+            1.6946222397391875,
+            2.926734617639429,
+            -7.000074175864097,
+            -2.5270822950013105,
+            1.4437268276959525,
+            7.862768559473771,
+            5.782288879751384,
+            1.8712013338616238,
+            -0.5233999645803635,
+            0.43433289218891247,
+            8.938365119196128,
+            1.65683957584843
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          0,
+          1,
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+          1,
+          0,
+          0,
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+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "greaterOrEqual float32 broadcast 2D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -8.049108184315305,
+            -5.522611295148119,
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+            -2.5270822950013105,
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+            7.862768559473771,
+            5.782288879751384,
+            1.8712013338616238,
+            -0.5233999645803635,
+            0.43433289218891247,
+            8.938365119196128,
+            1.65683957584843
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 3],
+          "data": [
+            -4.19451362795888,
+            3.8917840404002177,
+            -3.5139974150349484,
+            6.279316420756992,
+            0.0017881708061917578,
+            -0.7928582491851124
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          0,
+          0,
+          1,
+          0,
+          0,
+          1,
+          1,
+          1,
+          0,
+          0,
+          1,
+          0,
+          1,
+          0,
+          0,
+          0,
+          1,
+          1,
+          1,
+          0,
+          1,
+          0,
+          1,
+          1
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "greaterOrEqual float32 broadcast 3D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -8.049108184315305,
+            -5.522611295148119,
+            -2.097508268712267,
+            -7.455326346855671,
+            -5.4503762765457475,
+            9.802918209039795,
+            -3.6045179646016123,
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+            -5.068354954162224,
+            1.5821936652025705,
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+            8.938365119196128,
+            1.65683957584843
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 1],
+          "data": [
+            -3.282304702993888,
+            -1.397563682654157,
+            0.4905360718662557,
+            -6.882648917469618
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          0,
+          0,
+          1,
+          0,
+          0,
+          1,
+          0,
+          1,
+          0,
+          1,
+          1,
+          1,
+          1,
+          1,
+          0,
+          0,
+          1,
+          1,
+          1,
+          1,
+          0,
+          1,
+          1,
+          1
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "greaterOrEqual float32 broadcast 4D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [1, 1, 1, 1],
+          "data": [
+            -1.0187572964482339
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+            -0.5233999645803635,
+            0.43433289218891247,
+            8.938365119196128,
+            1.65683957584843
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          0,
+          1,
+          0,
+          1,
+          0,
+          0,
+          1,
+          0,
+          0,
+          1,
+          1,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0
+        ],
+        "type": "uint8"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/hard_sigmoid.json b/testing/web-platform/tests/webnn/resources/test_data/hard_sigmoid.json
new file mode 100644
index 0000000000..5d8c0c5daf
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/hard_sigmoid.json
@@ -0,0 +1,957 @@
+{ // hardSigmoid: Calculate the non-smooth function used in place of a sigmoid function on the input tensor.
+  "tests": [
+    {
+      "name": "hardSigmoid float32 positive 1D constant tensor default options", // default options: {alpha: 0.2, beta: 0.5}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            0.05907066981577147,
+            0.7076089198897,
+            0.5228405296259637,
+            0.42310158753617455,
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+            0.3048996433987061,
+            0.8025872652109942,
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+            0.554761182646025,
+            0.5542584257213008,
+            0.7311381962152534,
+            0.48809603333206986,
+            0.7766845231174959,
+            0.8455570201933926,
+            0.5553020911955322,
+            0.5603444395469193
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          0.5118141174316406,
+          0.6415218114852905,
+          0.6045681238174438,
+          0.5846202969551086,
+          0.6328738331794739,
+          0.6900588274002075,
+          0.5218378305435181,
+          0.5025954246520996,
+          0.5951059460639954,
+          0.6064510345458984,
+          0.6368615627288818,
+          0.5932421684265137,
+          0.5609799027442932,
+          0.6605174541473389,
+          0.5497192740440369,
+          0.6327379941940308,
+          0.6109522581100464,
+          0.6108517050743103,
+          0.6462276577949524,
+          0.5976191759109497,
+          0.6553369164466858,
+          0.669111430644989,
+          0.6110604405403137,
+          0.6120688915252686
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "hardSigmoid float32 positive 1D tensor default options", // default options: {alpha: 0.2, beta: 0.5}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            0.05907066981577147,
+            0.7076089198897,
+            0.5228405296259637,
+            0.42310158753617455,
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+            0.7766845231174959,
+            0.8455570201933926,
+            0.5553020911955322,
+            0.5603444395469193
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          0.5118141174316406,
+          0.6415218114852905,
+          0.6045681238174438,
+          0.5846202969551086,
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+          0.6900588274002075,
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+          0.5025954246520996,
+          0.5951059460639954,
+          0.6064510345458984,
+          0.6368615627288818,
+          0.5932421684265137,
+          0.5609799027442932,
+          0.6605174541473389,
+          0.5497192740440369,
+          0.6327379941940308,
+          0.6109522581100464,
+          0.6108517050743103,
+          0.6462276577949524,
+          0.5976191759109497,
+          0.6553369164466858,
+          0.669111430644989,
+          0.6110604405403137,
+          0.6120688915252686
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "hardSigmoid float32 positive 2D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            0.05907066981577147,
+            0.7076089198897,
+            0.5228405296259637,
+            0.42310158753617455,
+            0.6643692569867639,
+            0.9502940424761401,
+            0.10918906453593258,
+            0.012977113903015258,
+            0.4755297159390164,
+            0.5322551665299613,
+            0.684307857800798,
+            0.4662107602754757,
+            0.3048996433987061,
+            0.8025872652109942,
+            0.2485963985673023,
+            0.6636898942337357,
+            0.554761182646025,
+            0.5542584257213008,
+            0.7311381962152534,
+            0.48809603333206986,
+            0.7766845231174959,
+            0.8455570201933926,
+            0.5553020911955322,
+            0.5603444395469193
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          0.5118141174316406,
+          0.6415218114852905,
+          0.6045681238174438,
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+          0.669111430644989,
+          0.6110604405403137,
+          0.6120688915252686
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "hardSigmoid float32 positive 3D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
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+            0.7076089198897,
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+            0.5542584257213008,
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+            0.48809603333206986,
+            0.7766845231174959,
+            0.8455570201933926,
+            0.5553020911955322,
+            0.5603444395469193
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          0.5118141174316406,
+          0.6415218114852905,
+          0.6045681238174438,
+          0.5846202969551086,
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+          0.6900588274002075,
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+          0.6110604405403137,
+          0.6120688915252686
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "hardSigmoid float32 positive 4D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
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+            0.5553020911955322,
+            0.5603444395469193
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
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+          0.6415218114852905,
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+          0.6110604405403137,
+          0.6120688915252686
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "hardSigmoid float32 positive 5D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 1, 3, 4],
+          "data": [
+            0.05907066981577147,
+            0.7076089198897,
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 1, 3, 4],
+        "data": [
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "hardSigmoid float32 positive 4D tensor specified positive options.alpha default options.beta",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
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+            0.7076089198897,
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/hard_swish.json b/testing/web-platform/tests/webnn/resources/test_data/hard_swish.json
new file mode 100644
index 0000000000..e43f62ede0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/hard_swish.json
@@ -0,0 +1,401 @@
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/identity.json b/testing/web-platform/tests/webnn/resources/test_data/identity.json
new file mode 100644
index 0000000000..6024e8786f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/identity.json
@@ -0,0 +1,422 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/instance_normalization.json b/testing/web-platform/tests/webnn/resources/test_data/instance_normalization.json
new file mode 100644
index 0000000000..a9fd493c99
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/instance_normalization.json
@@ -0,0 +1,519 @@
+{
+  "tests": [
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/layer_normalization.json b/testing/web-platform/tests/webnn/resources/test_data/layer_normalization.json
new file mode 100644
index 0000000000..6005776de5
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/layer_normalization.json
@@ -0,0 +1,842 @@
+{
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json b/testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json
new file mode 100644
index 0000000000..be810a90ce
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json
@@ -0,0 +1,609 @@
+{
+  "tests": [
+    {
+      "name": "leakyRelu float32 1D constant tensor default options", // default options.alpha=0.01
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
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+            -65.59528852425132
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
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+          -0.4127694368362427,
+          -0.6559529304504395
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 1D tensor default options", // default options.alpha=0.01
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
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+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.19053640961647034,
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+          -0.8747624158859253,
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+          -0.4127694368362427,
+          -0.6559529304504395
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 2D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            -19.053641157857015,
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+            -69.54966768567536,
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+            -90.40109641580112,
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+            98.61402761165104,
+            55.33923216295767,
+            -33.178605382176,
+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          -0.19053640961647034,
+          50.77590560913086,
+          -0.695496678352356,
+          -0.8057432770729065,
+          -0.9040110111236572,
+          76.02884674072266,
+          66.33873748779297,
+          -0.8410186767578125,
+          -0.1719101369380951,
+          -0.8747624158859253,
+          -0.0341646634042263,
+          -0.2277235984802246,
+          -0.02509489096701145,
+          18.933284759521484,
+          98.61402893066406,
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+          -0.6147925853729248,
+          64.26514434814453,
+          21.469341278076172,
+          -0.31514689326286316,
+          -0.4127694368362427,
+          -0.6559529304504395
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 3D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
+            -80.57432185346957,
+            -90.40109641580112,
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+            66.33873525494391,
+            -84.10186452043264,
+            -17.19101282693427,
+            -87.47624329964907,
+            -3.4164664941905585,
+            -22.7723589638245,
+            -2.509489125727569,
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+            98.61402761165104,
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+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          -0.19053640961647034,
+          50.77590560913086,
+          -0.695496678352356,
+          -0.8057432770729065,
+          -0.9040110111236572,
+          76.02884674072266,
+          66.33873748779297,
+          -0.8410186767578125,
+          -0.1719101369380951,
+          -0.8747624158859253,
+          -0.0341646634042263,
+          -0.2277235984802246,
+          -0.02509489096701145,
+          18.933284759521484,
+          98.61402893066406,
+          55.3392333984375,
+          -0.33178603649139404,
+          -0.4603901207447052,
+          -0.6147925853729248,
+          64.26514434814453,
+          21.469341278076172,
+          -0.31514689326286316,
+          -0.4127694368362427,
+          -0.6559529304504395
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 4D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
+            -80.57432185346957,
+            -90.40109641580112,
+            76.02884674163622,
+            66.33873525494391,
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+            -17.19101282693427,
+            -87.47624329964907,
+            -3.4164664941905585,
+            -22.7723589638245,
+            -2.509489125727569,
+            18.933284857582677,
+            98.61402761165104,
+            55.33923216295767,
+            -33.178605382176,
+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
+          -0.19053640961647034,
+          50.77590560913086,
+          -0.695496678352356,
+          -0.8057432770729065,
+          -0.9040110111236572,
+          76.02884674072266,
+          66.33873748779297,
+          -0.8410186767578125,
+          -0.1719101369380951,
+          -0.8747624158859253,
+          -0.0341646634042263,
+          -0.2277235984802246,
+          -0.02509489096701145,
+          18.933284759521484,
+          98.61402893066406,
+          55.3392333984375,
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+          -0.6147925853729248,
+          64.26514434814453,
+          21.469341278076172,
+          -0.31514689326286316,
+          -0.4127694368362427,
+          -0.6559529304504395
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 5D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 1, 3, 4],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
+            -80.57432185346957,
+            -90.40109641580112,
+            76.02884674163622,
+            66.33873525494391,
+            -84.10186452043264,
+            -17.19101282693427,
+            -87.47624329964907,
+            -3.4164664941905585,
+            -22.7723589638245,
+            -2.509489125727569,
+            18.933284857582677,
+            98.61402761165104,
+            55.33923216295767,
+            -33.178605382176,
+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 1, 3, 4],
+        "data": [
+          -0.19053640961647034,
+          50.77590560913086,
+          -0.695496678352356,
+          -0.8057432770729065,
+          -0.9040110111236572,
+          76.02884674072266,
+          66.33873748779297,
+          -0.8410186767578125,
+          -0.1719101369380951,
+          -0.8747624158859253,
+          -0.0341646634042263,
+          -0.2277235984802246,
+          -0.02509489096701145,
+          18.933284759521484,
+          98.61402893066406,
+          55.3392333984375,
+          -0.33178603649139404,
+          -0.4603901207447052,
+          -0.6147925853729248,
+          64.26514434814453,
+          21.469341278076172,
+          -0.31514689326286316,
+          -0.4127694368362427,
+          -0.6559529304504395
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 1D tensor negative options.alpha",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
+            -80.57432185346957,
+            -90.40109641580112,
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+            -3.4164664941905585,
+            -22.7723589638245,
+            -2.509489125727569,
+            18.933284857582677,
+            98.61402761165104,
+            55.33923216295767,
+            -33.178605382176,
+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "alpha": -97.70109193608776
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          1861.5615234375,
+          50.77590560913086,
+          6795.07861328125,
+          7872.19970703125,
+          8832.2861328125,
+          76.02884674072266,
+          66.33873748779297,
+          8216.8447265625,
+          1679.580810546875,
+          8546.5244140625,
+          333.7925109863281,
+          2224.884521484375,
+          245.17982482910156,
+          18.933284759521484,
+          98.61402893066406,
+          55.3392333984375,
+          3241.5859375,
+          4498.06201171875,
+          6006.5908203125,
+          64.26514434814453,
+          21.469341278076172,
+          3079.019775390625,
+          4032.802490234375,
+          6408.73193359375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 2D tensor positive options.alpha",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
+            -80.57432185346957,
+            -90.40109641580112,
+            76.02884674163622,
+            66.33873525494391,
+            -84.10186452043264,
+            -17.19101282693427,
+            -87.47624329964907,
+            -3.4164664941905585,
+            -22.7723589638245,
+            -2.509489125727569,
+            18.933284857582677,
+            98.61402761165104,
+            55.33923216295767,
+            -33.178605382176,
+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "alpha": 35.799162942273234
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          -682.1043701171875,
+          50.77590560913086,
+          -2489.81982421875,
+          -2884.493408203125,
+          -3236.28369140625,
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+          -3010.776611328125,
+          -615.4238891601562,
+          -3131.576416015625,
+          -122.306640625,
+          -815.2314453125,
+          -89.83760833740234,
+          18.933284759521484,
+          98.61402893066406,
+          55.3392333984375,
+          -1187.7662353515625,
+          -1648.158203125,
+          -2200.906005859375,
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+          21.469341278076172,
+          -1128.1995849609375,
+          -1477.6800537109375,
+          -2348.256591796875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "leakyRelu float32 5D tensor options.alpha=0.0", // relu
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 1, 3, 4],
+          "data": [
+            -19.053641157857015,
+            50.77590443347373,
+            -69.54966768567536,
+            -80.57432185346957,
+            -90.40109641580112,
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+            -3.4164664941905585,
+            -22.7723589638245,
+            -2.509489125727569,
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+            98.61402761165104,
+            55.33923216295767,
+            -33.178605382176,
+            -46.03901297809681,
+            -61.47925538549912,
+            64.26514742116822,
+            21.46934200700099,
+            -31.51468946013378,
+            -41.27694294131783,
+            -65.59528852425132
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "alpha": 0.0
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 1, 3, 4],
+        "data": [
+          0,
+          50.77590560913086,
+          0,
+          0,
+          0,
+          76.02884674072266,
+          66.33873748779297,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          18.933284759521484,
+          98.61402893066406,
+          55.3392333984375,
+          0,
+          0,
+          0,
+          64.26514434814453,
+          21.469341278076172,
+          0,
+          0,
+          0
+        ],
+        "type": "float32"
+      },
+      "type": "float32"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/lesser.json b/testing/web-platform/tests/webnn/resources/test_data/lesser.json
new file mode 100644
index 0000000000..7527ea5c53
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/lesser.json
@@ -0,0 +1,970 @@
+{
+  "tests": [
+    {
+      "name": "lesser float32 0D scalar",
+      "inputs": {
+        "a": {
+          "shape": [],
+          "data": [
+            -0.5228080371919397
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [],
+          "data": [
+            0.8150388698055
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [],
+        "data": [
+          1
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "lesser float32 1D constant tensors",
+      "inputs": {
+        "a": {
+          "shape": [24],
+          "data": [
+            -1.1472189199107738,
+            -8.409373914977403,
+            -2.2753309465448535,
+            -0.5770801181952088,
+            8.171789524073006,
+            -0.907120961653046,
+            5.290845327676683,
+            -3.9134646002455975,
+            9.825094822582592,
+            -8.931730412646708,
+            -3.4574016573266553,
+            -7.331231867596579,
+            1.2320041500405665,
+            4.312076818806343,
+            1.2715546106634825,
+            4.184540686856884,
+            -6.710920186561022,
+            3.0768423616317655,
+            1.0030865825397903,
+            -9.076244529342748,
+            8.907161848861044,
+            4.232614785327211,
+            2.1005889661509425,
+            -6.201345656840638
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        "b": {
+          "shape": [24],
+          "data": [
+            2.9453755897484726,
+            3.7304716829497266,
+            4.025375190729793,
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+        }
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+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "lesser float32 broadcast 4D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [1, 1, 1, 1],
+          "data": [
+            -5.678369818327527
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+      "expected": {
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+          1,
+          0
+        ],
+        "type": "uint8"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/lesser_or_equal.json b/testing/web-platform/tests/webnn/resources/test_data/lesser_or_equal.json
new file mode 100644
index 0000000000..f47bd3b9d9
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/lesser_or_equal.json
@@ -0,0 +1,970 @@
+{
+  "tests": [
+    {
+      "name": "lesserOrEqual float32 0D scalar",
+      "inputs": {
+        "a": {
+          "shape": [],
+          "data": [
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+      "expected": {
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+        "data": [
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+        "type": "uint8"
+      }
+    },
+    {
+      "name": "lesserOrEqual float32 1D constant tensors",
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+    {
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+    },
+    {
+      "name": "lesserOrEqual float32 2D tensors",
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+    },
+    {
+      "name": "lesserOrEqual float32 3D tensors",
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+    {
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+            5.5837429023307585,
+            1.5017805895450707,
+            6.592243911021967,
+            -9.925486373960158,
+            -7.134800104191736,
+            -4.915772925181514,
+            -9.137166899591985,
+            9.403683415346574,
+            -9.831502572671727,
+            -2.01231810920369,
+            -6.5976898409038665,
+            -8.269321065543842,
+            8.281030248217476,
+            1.645282309888799,
+            -1.486272662516086,
+            -4.998753723234106,
+            -0.9209934547883556,
+            -9.434256078100471,
+            9.81323851825412
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          0,
+          0,
+          0,
+          0,
+          0,
+          1,
+          0,
+          1,
+          0,
+          0,
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+          0,
+          0,
+          0,
+          1,
+          0,
+          0,
+          0,
+          0,
+          0,
+          1
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "lesserOrEqual float32 broadcast 2D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -8.28400872674051,
+            -3.2192645259702735,
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+            -6.5976898409038665,
+            -8.269321065543842,
+            8.281030248217476,
+            1.645282309888799,
+            -1.486272662516086,
+            -4.998753723234106,
+            -0.9209934547883556,
+            -9.434256078100471,
+            9.81323851825412
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 3],
+          "data": [
+            -8.499546963452499,
+            -8.321309725362518,
+            -7.182070889034953,
+            3.418306360706925,
+            5.389469370006754,
+            6.9043128878217
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          0,
+          0,
+          0,
+          1,
+          1,
+          1,
+          0,
+          0,
+          1,
+          1,
+          1,
+          1,
+          0,
+          1,
+          0,
+          1,
+          1,
+          0,
+          0,
+          0,
+          0,
+          1,
+          1,
+          0
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "lesserOrEqual float32 broadcast 3D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -8.28400872674051,
+            -3.2192645259702735,
+            -6.543179908675301,
+            -0.5402850743134735,
+            -5.413843546081334,
+            5.5837429023307585,
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+            -9.434256078100471,
+            9.81323851825412
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+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 1],
+          "data": [
+            4.195140983741625,
+            7.828658911400325,
+            6.690202989995797,
+            0.924701041097018
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
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+          1,
+          1,
+          1,
+          1,
+          0,
+          1,
+          1,
+          1,
+          1,
+          1,
+          0
+        ],
+        "type": "uint8"
+      }
+    },
+    {
+      "name": "lesserOrEqual float32 broadcast 4D to 4D",
+      "inputs": {
+        "a": {
+          "shape": [1, 1, 1, 1],
+          "data": [
+            4.840611135629258
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 2, 3],
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+            -4.998753723234106,
+            -0.9209934547883556,
+            -9.434256078100471,
+            9.81323851825412
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "shape": [2, 2, 2, 3],
+        "data": [
+          0,
+          0,
+          0,
+          0,
+          0,
+          1,
+          0,
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+          0,
+          0,
+          0,
+          0,
+          1
+        ],
+        "type": "uint8"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/linear.json b/testing/web-platform/tests/webnn/resources/test_data/linear.json
new file mode 100644
index 0000000000..6cbb84e9cb
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/linear.json
@@ -0,0 +1,818 @@
+{ // linear: Calculate a linear function y = alpha * x + beta on the input tensor.
+  "tests": [
+    {
+      "name": "linear float32 1D constant tensor default options", // default options: {alpha: 1, beta: 0}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
+            -1.9555539248683687,
+            -4.598548331438281,
+            4.234208072658522,
+            3.097542121236385,
+            3.7465923873418348,
+            -4.487029772637743,
+            6.407402114982858,
+            -4.354544013606705,
+            -5.8190925441357955,
+            3.7214346452179328,
+            -6.330114110872294,
+            8.580595073862398,
+            -6.764922344398516,
+            6.433565829855674,
+            -9.708685944356201,
+            2.6431380232113746,
+            5.214088864824586,
+            9.658617012096975,
+            -8.72174939837989,
+            -0.4533396492565025,
+            9.992619953447026,
+            -6.469674921617137
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -1.12251615524292,
+          -6.605223178863525,
+          -1.9555538892745972,
+          -4.598548412322998,
+          4.234208106994629,
+          3.0975420475006104,
+          3.7465922832489014,
+          -4.487029552459717,
+          6.407402038574219,
+          -4.354544162750244,
+          -5.819092750549316,
+          3.7214345932006836,
+          -6.330113887786865,
+          8.580595016479492,
+          -6.764922142028809,
+          6.433565616607666,
+          -9.708685874938965,
+          2.6431379318237305,
+          5.2140889167785645,
+          9.65861701965332,
+          -8.721749305725098,
+          -0.4533396363258362,
+          9.992619514465332,
+          -6.469675064086914
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "linear float32 1D tensor default options", // default options: {alpha: 1, beta: 0}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
+            -1.9555539248683687,
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+            -8.72174939837989,
+            -0.4533396492565025,
+            9.992619953447026,
+            -6.469674921617137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -1.12251615524292,
+          -6.605223178863525,
+          -1.9555538892745972,
+          -4.598548412322998,
+          4.234208106994629,
+          3.0975420475006104,
+          3.7465922832489014,
+          -4.487029552459717,
+          6.407402038574219,
+          -4.354544162750244,
+          -5.819092750549316,
+          3.7214345932006836,
+          -6.330113887786865,
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+          -6.764922142028809,
+          6.433565616607666,
+          -9.708685874938965,
+          2.6431379318237305,
+          5.2140889167785645,
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+          -8.721749305725098,
+          -0.4533396363258362,
+          9.992619514465332,
+          -6.469675064086914
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "linear float32 2D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
+            -1.9555539248683687,
+            -4.598548331438281,
+            4.234208072658522,
+            3.097542121236385,
+            3.7465923873418348,
+            -4.487029772637743,
+            6.407402114982858,
+            -4.354544013606705,
+            -5.8190925441357955,
+            3.7214346452179328,
+            -6.330114110872294,
+            8.580595073862398,
+            -6.764922344398516,
+            6.433565829855674,
+            -9.708685944356201,
+            2.6431380232113746,
+            5.214088864824586,
+            9.658617012096975,
+            -8.72174939837989,
+            -0.4533396492565025,
+            9.992619953447026,
+            -6.469674921617137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          -1.12251615524292,
+          -6.605223178863525,
+          -1.9555538892745972,
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+          -0.4533396363258362,
+          9.992619514465332,
+          -6.469675064086914
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "linear float32 3D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
+            -1.9555539248683687,
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+            -4.487029772637743,
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+            3.7214346452179328,
+            -6.330114110872294,
+            8.580595073862398,
+            -6.764922344398516,
+            6.433565829855674,
+            -9.708685944356201,
+            2.6431380232113746,
+            5.214088864824586,
+            9.658617012096975,
+            -8.72174939837989,
+            -0.4533396492565025,
+            9.992619953447026,
+            -6.469674921617137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          -1.12251615524292,
+          -6.605223178863525,
+          -1.9555538892745972,
+          -4.598548412322998,
+          4.234208106994629,
+          3.0975420475006104,
+          3.7465922832489014,
+          -4.487029552459717,
+          6.407402038574219,
+          -4.354544162750244,
+          -5.819092750549316,
+          3.7214345932006836,
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+          -6.764922142028809,
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+          2.6431379318237305,
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+          -0.4533396363258362,
+          9.992619514465332,
+          -6.469675064086914
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "linear float32 4D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
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+            -6.764922344398516,
+            6.433565829855674,
+            -9.708685944356201,
+            2.6431380232113746,
+            5.214088864824586,
+            9.658617012096975,
+            -8.72174939837989,
+            -0.4533396492565025,
+            9.992619953447026,
+            -6.469674921617137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          -1.12251615524292,
+          -6.605223178863525,
+          -1.9555538892745972,
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+          3.0975420475006104,
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+          -4.354544162750244,
+          -5.819092750549316,
+          3.7214345932006836,
+          -6.330113887786865,
+          8.580595016479492,
+          -6.764922142028809,
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+          5.2140889167785645,
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+          -8.721749305725098,
+          -0.4533396363258362,
+          9.992619514465332,
+          -6.469675064086914
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "linear float32 5D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
+            -1.9555539248683687,
+            -4.598548331438281,
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+            3.097542121236385,
+            3.7465923873418348,
+            -4.487029772637743,
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+            -9.708685944356201,
+            2.6431380232113746,
+            5.214088864824586,
+            9.658617012096975,
+            -8.72174939837989,
+            -0.4533396492565025,
+            9.992619953447026,
+            -6.469674921617137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 1, 3],
+        "data": [
+          -1.12251615524292,
+          -6.605223178863525,
+          -1.9555538892745972,
+          -4.598548412322998,
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "linear float32 4D tensor specified options.alpha and default options.beta",
+      "inputs": {
+        "x": {
+         "shape": [2, 2, 2, 3],
+          "data": [
+            -1.1225161816883542,
+            -6.605223195131225,
+            -1.9555539248683687,
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/log.json b/testing/web-platform/tests/webnn/resources/test_data/log.json
new file mode 100644
index 0000000000..0481061272
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/log.json
@@ -0,0 +1,401 @@
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+    }
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/logical_not.json b/testing/web-platform/tests/webnn/resources/test_data/logical_not.json
new file mode 100644
index 0000000000..3a37652a13
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/logical_not.json
@@ -0,0 +1,421 @@
+{
+  "tests": [
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+      "name": "logicalNot uint8 0D scalar",
+      "inputs": {
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+          "data": [
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+          "type": "uint8"
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+    {
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/matmul.json b/testing/web-platform/tests/webnn/resources/test_data/matmul.json
new file mode 100644
index 0000000000..f3a03e442e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/matmul.json
@@ -0,0 +1,1439 @@
+{
+  "tests": [
+    {
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/max.json b/testing/web-platform/tests/webnn/resources/test_data/max.json
new file mode 100644
index 0000000000..26285042d0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/max.json
@@ -0,0 +1,882 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json b/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json
new file mode 100644
index 0000000000..4532843d2b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json
@@ -0,0 +1,1439 @@
+{
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/min.json b/testing/web-platform/tests/webnn/resources/test_data/min.json
new file mode 100644
index 0000000000..a08e651428
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/min.json
@@ -0,0 +1,882 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/mul.json b/testing/web-platform/tests/webnn/resources/test_data/mul.json
new file mode 100644
index 0000000000..3de9018427
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/mul.json
@@ -0,0 +1,882 @@
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/neg.json b/testing/web-platform/tests/webnn/resources/test_data/neg.json
new file mode 100644
index 0000000000..34dee53df1
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/neg.json
@@ -0,0 +1,401 @@
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/pad.json b/testing/web-platform/tests/webnn/resources/test_data/pad.json
new file mode 100644
index 0000000000..53e8ed8f59
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/pad.json
@@ -0,0 +1,797 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/pow.json b/testing/web-platform/tests/webnn/resources/test_data/pow.json
new file mode 100644
index 0000000000..1738722b10
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/pow.json
@@ -0,0 +1,880 @@
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+          0.06714485585689545,
+          -0.008288968354463577,
+          -4.1750155416186985e-11,
+          1210.5478515625,
+          3.8231124932508465e-8,
+          -1667.201416015625,
+          -0.16149713099002838,
+          -0.00015812950732652098,
+          485079424,
+          3.584487018315485e-8
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "pow float32 5D base tensor and 5D integer exponent tensor",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 1, 2, 3],
+          "data": [
+            17.84601042972939,
+            -0.06310698009783522,
+            -9.868203150557209,
+            11.177720889025082,
+            -17.346274412274497,
+            11.862249907078805,
+            -16.832276297112916,
+            2.657481705284937,
+            -2.783346559701183,
+            -13.756399883988184,
+            13.131383020668672,
+            -0.4376337155223524,
+            -15.678688537967744,
+            10.283306376871472,
+            14.89317426754188,
+            -4.941208383494091,
+            -14.231812435141862,
+            3.2646027287637835,
+            17.229148713687444,
+            -2.885918185998449,
+            -1.440015043233842,
+            -5.757015935913907,
+            17.41126899618849,
+            17.415215284302022
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 1, 2, 3],
+          "data": [
+            1,
+            6,
+            -7,
+            7,
+            -2,
+            1,
+            4,
+            -10,
+            -2,
+            -5,
+            -2,
+            -10,
+            -8,
+            -7,
+            -1,
+            -3,
+            -9,
+            6,
+            -6,
+            7,
+            -5,
+            -5,
+            7,
+            -6
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 1, 2, 3],
+        "data": [
+          17.846010208129883,
+          6.316321332633379e-8,
+          -1.0973203501407625e-7,
+          21800822,
+          0.0033234376460313797,
+          11.862250328063965,
+          80273.3359375,
+          0.00005692423656000756,
+          0.12908191978931427,
+          -0.0000020299064544815337,
+          0.005799346603453159,
+          3880.540283203125,
+          2.7385585465999895e-10,
+          8.223764069725803e-8,
+          0.06714485585689545,
+          -0.008288968354463577,
+          -4.1750155416186985e-11,
+          1210.5478515625,
+          3.8231124932508465e-8,
+          -1667.201416015625,
+          -0.16149713099002838,
+          -0.00015812950732652098,
+          485079424,
+          3.584487018315485e-8
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "pow (square) float32 4D base tensor and broadcastable 0D integer exponent scalar",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            17.84601042972939,
+            -0.06310698009783522,
+            -9.868203150557209,
+            11.177720889025082,
+            -17.346274412274497,
+            11.862249907078805,
+            -16.832276297112916,
+            2.657481705284937,
+            -2.783346559701183,
+            -13.756399883988184,
+            13.131383020668672,
+            -0.4376337155223524,
+            -15.678688537967744,
+            10.283306376871472,
+            14.89317426754188,
+            -4.941208383494091,
+            -14.231812435141862,
+            3.2646027287637835,
+            17.229148713687444,
+            -2.885918185998449,
+            -1.440015043233842,
+            -5.757015935913907,
+            17.41126899618849,
+            17.415215284302022
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [],
+          "data": [2], // square
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          318.4800720214844,
+          0.00398249039426446,
+          97.38143157958984,
+          124.94144439697266,
+          300.8932800292969,
+          140.71298217773438,
+          283.32550048828125,
+          7.062208652496338,
+          7.747018814086914,
+          189.23854064941406,
+          172.43321228027344,
+          0.19152326881885529,
+          245.8212890625,
+          105.74638366699219,
+          221.806640625,
+          24.41554069519043,
+          202.5444793701172,
+          10.657630920410156,
+          296.84356689453125,
+          8.328523635864258,
+          2.073643445968628,
+          33.14323043823242,
+          303.1522521972656,
+          303.2897033691406
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "pow (sqrt) float32 4D positive base tensor and broadcastable 0D integer exponent scalar",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            1.4186520416325132,
+            19.384846302672862,
+            12.983916515701083,
+            2.460315836803,
+            7.818154966175683,
+            6.944444510618668,
+            12.183951502858129,
+            17.91247403540764,
+            11.35656818684932,
+            8.924248275595188,
+            17.63682367890858,
+            11.49622917976384,
+            18.51627975602645,
+            2.2580489773802714,
+            2.231948203000913,
+            13.629855345962834,
+            17.54841363842334,
+            0.5390733851574936,
+            5.891367616729561,
+            0.12803148642270834,
+            19.654494983574544,
+            3.412272498152542,
+            4.9450348267065225,
+            4.437101298745225
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [],
+          "data": [0.5], // sqrt
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          1.19107186794281,
+          4.402822494506836,
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+          4.4333391189575195,
+          1.847233772277832,
+          2.223743438720703,
+          2.106442928314209
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "pow float32 4D base tensor and broadcastable 2D integer exponent tensor",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            17.84601042972939,
+            -0.06310698009783522,
+            -9.868203150557209,
+            11.177720889025082,
+            -17.346274412274497,
+            11.862249907078805,
+            -16.832276297112916,
+            2.657481705284937,
+            -2.783346559701183,
+            -13.756399883988184,
+            13.131383020668672,
+            -0.4376337155223524,
+            -15.678688537967744,
+            10.283306376871472,
+            14.89317426754188,
+            -4.941208383494091,
+            -14.231812435141862,
+            3.2646027287637835,
+            17.229148713687444,
+            -2.885918185998449,
+            -1.440015043233842,
+            -5.757015935913907,
+            17.41126899618849,
+            17.415215284302022
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 3],
+          "data": [
+            5,
+            -10,
+            -10,
+            7,
+            -7,
+            -9
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          1810113,
+          998220038144,
+          1.1418765932802444e-10,
+          21800822,
+          -2.11619832768406e-9,
+          2.1502860603206386e-10,
+          -1351182.875,
+          0.00005692423656000756,
+          0.000035836616007145494,
+          -93225256,
+          1.4853429597394552e-8,
+          -1698.2552490234375,
+          -947433.5,
+          7.562621362477984e-11,
+          1.8626330946375225e-12,
+          -71917.1015625,
+          -8.45626324519344e-9,
+          0.00002374253199377563,
+          1518165.5,
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+          0.026081321761012077,
+          -209595.46875,
+          2.0615180673644318e-9,
+          6.786416914539295e-12
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "pow float32 4D base tensor and broadcastable 3D integer exponent tensor",
+      "inputs": {
+        "a": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            17.84601042972939,
+            -0.06310698009783522,
+            -9.868203150557209,
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+            -17.346274412274497,
+            11.862249907078805,
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+            3.2646027287637835,
+            17.229148713687444,
+            -2.885918185998449,
+            -1.440015043233842,
+            -5.757015935913907,
+            17.41126899618849,
+            17.415215284302022
+          ],
+          "type": "float32"
+        },
+        "b": {
+          "shape": [2, 2, 1],
+          "data": [
+            -5,
+            -10,
+            9,
+            -6
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          5.524516950572433e-7,
+          -999109.625,
+          -0.000010685862434911542,
+          3.284485530774539e-11,
+          4.0545030440680696e-13,
+          1.81271334748212e-11,
+          -108463955968,
+          6610.47265625,
+          -10025.4921875,
+          1.4756086841316574e-7,
+          1.9504606996179064e-7,
+          142.34274291992188,
+          -0.0000010554830396358739,
+          0.000008696333679836243,
+          0.0000013647832020069472,
+          1.1525726506533829e-7,
+          2.9335795945217846e-12,
+          0.000007272717084561009,
+          133774827520,
+          -13885.326171875,
+          -26.625843048095703,
+          0.000027467271138448268,
+          3.5893645389251105e-8,
+          3.584487018315485e-8
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/prelu.json b/testing/web-platform/tests/webnn/resources/test_data/prelu.json
new file mode 100644
index 0000000000..cf79bee7a9
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/prelu.json
@@ -0,0 +1,966 @@
+{
+  "tests": [
+    {
+      "name": "prelu float32 1D constant tensors",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -2.5491681672317235,
+            -4.794857500523286,
+            8.413617520687886,
+            6.108623511441287,
+            -8.492292847462334,
+            3.3143364551069876,
+            1.1687212422078108,
+            -0.1417628422981423,
+            -6.714651930677875,
+            5.787421746282195,
+            -3.7556274239369847,
+            -4.898286418001732,
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+            -3.9542297833176665,
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+            -2.5581801134299775,
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+            4.551425642121982,
+            -9.267870491380243,
+            -0.26217744010265776,
+            1.325895534951659,
+            -7.418311114031475
+          ],
+          "type": "float32",
+          "constant": true
+        },
+        "slope": {
+          "shape": [24],
+          "data": [
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+            -4.617085001507579,
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+            -1.9615313732548287,
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+            -5.505102187715796,
+            -2.3927369956553557,
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+            -1.9816573593209075,
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+            -1.7995220850663962,
+            9.29585020267449
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -23.817113876342773,
+          -1.342889666557312,
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+          1.3258955478668213,
+          -68.95950317382812
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "prelu float32 1D tensors",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -2.5491681672317235,
+            -4.794857500523286,
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+          "type": "float32"
+        },
+        "slope": {
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+            -1.7995220850663962,
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+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "prelu float32 2D tensors",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
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+          "type": "float32"
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+        "slope": {
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+            -2.5581801134299775,
+            -8.658834185932482,
+            8.475073893236225,
+            4.551425642121982,
+            -9.267870491380243,
+            -0.26217744010265776,
+            1.325895534951659,
+            -7.418311114031475
+          ],
+          "type": "float32"
+        },
+        "slope": {
+          "shape": [1, 1, 1, 1], // broadcast [1, 1, 1, 1] --> [2, 2, 2, 3]
+          "data": [
+            5.0114545056636395
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          -12.775040626525879,
+          -24.029211044311523,
+          8.413617134094238,
+          6.108623504638672,
+          -42.558738708496094,
+          3.3143365383148193,
+          1.1687211990356445,
+          -0.7104380130767822,
+          -33.65017318725586,
+          5.787421703338623,
+          -18.821155548095703,
+          -24.54753875732422,
+          7.3295159339904785,
+          -19.816442489624023,
+          7.067296981811523,
+          9.439736366271973,
+          -12.82020378112793,
+          -43.39335632324219,
+          8.47507381439209,
+          4.551425457000732,
+          -46.44551467895508,
+          -1.3138903379440308,
+          1.3258955478668213,
+          -37.17652893066406
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reciprocal.json b/testing/web-platform/tests/webnn/resources/test_data/reciprocal.json
new file mode 100644
index 0000000000..c71ac28005
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reciprocal.json
@@ -0,0 +1,421 @@
+{
+  "tests": [
+    {
+      "name": "reciprocal float32 0D scalar",
+      "inputs": {
+        "x": {
+          "shape": [],
+          "data": [
+            -9.363490722293237
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [],
+        "data": [
+          -0.10679777711629868
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reciprocal float32 1D constant tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -1.011086269320547,
+            4.6215385203163315,
+            2.4173768502126727,
+            -8.934871773645101,
+            -3.348558485147799,
+            -8.803302423903414,
+            8.82632175955639,
+            6.572828698368518,
+            6.639494748363678,
+            2.6842704650429496,
+            -4.627160556770815,
+            -6.873653395070409,
+            -1.185412145486616,
+            0.9572811852263605,
+            0.021320551999082227,
+            0.35645850544829827,
+            4.827780063442164,
+            1.312808185701284,
+            8.246278209083616,
+            -0.20391698798075097,
+            -9.039937028172055,
+            9.94280947774324,
+            -9.540112102987099,
+            -0.08022661906399975
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.9890353083610535,
+          0.21637815237045288,
+          0.4136715531349182,
+          -0.111921027302742,
+          -0.29863595962524414,
+          -0.11359372735023499,
+          0.1132974773645401,
+          0.1521414965391159,
+          0.15061387419700623,
+          0.3725407123565674,
+          -0.21611526608467102,
+          -0.14548304677009583,
+          -0.8435884118080139,
+          1.044625163078308,
+          46.90310287475586,
+          2.805375576019287,
+          0.20713452994823456,
+          0.761725902557373,
+          0.12126683443784714,
+          -4.903956413269043,
+          -0.11062023788690567,
+          0.10057520121335983,
+          -0.10482056438922882,
+          -12.464690208435059
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reciprocal float32 1D tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            -1.011086269320547,
+            4.6215385203163315,
+            2.4173768502126727,
+            -8.934871773645101,
+            -3.348558485147799,
+            -8.803302423903414,
+            8.82632175955639,
+            6.572828698368518,
+            6.639494748363678,
+            2.6842704650429496,
+            -4.627160556770815,
+            -6.873653395070409,
+            -1.185412145486616,
+            0.9572811852263605,
+            0.021320551999082227,
+            0.35645850544829827,
+            4.827780063442164,
+            1.312808185701284,
+            8.246278209083616,
+            -0.20391698798075097,
+            -9.039937028172055,
+            9.94280947774324,
+            -9.540112102987099,
+            -0.08022661906399975
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          -0.9890353083610535,
+          0.21637815237045288,
+          0.4136715531349182,
+          -0.111921027302742,
+          -0.29863595962524414,
+          -0.11359372735023499,
+          0.1132974773645401,
+          0.1521414965391159,
+          0.15061387419700623,
+          0.3725407123565674,
+          -0.21611526608467102,
+          -0.14548304677009583,
+          -0.8435884118080139,
+          1.044625163078308,
+          46.90310287475586,
+          2.805375576019287,
+          0.20713452994823456,
+          0.761725902557373,
+          0.12126683443784714,
+          -4.903956413269043,
+          -0.11062023788690567,
+          0.10057520121335983,
+          -0.10482056438922882,
+          -12.464690208435059
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reciprocal float32 2D tensor",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            -1.011086269320547,
+            4.6215385203163315,
+            2.4173768502126727,
+            -8.934871773645101,
+            -3.348558485147799,
+            -8.803302423903414,
+            8.82632175955639,
+            6.572828698368518,
+            6.639494748363678,
+            2.6842704650429496,
+            -4.627160556770815,
+            -6.873653395070409,
+            -1.185412145486616,
+            0.9572811852263605,
+            0.021320551999082227,
+            0.35645850544829827,
+            4.827780063442164,
+            1.312808185701284,
+            8.246278209083616,
+            -0.20391698798075097,
+            -9.039937028172055,
+            9.94280947774324,
+            -9.540112102987099,
+            -0.08022661906399975
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          -0.9890353083610535,
+          0.21637815237045288,
+          0.4136715531349182,
+          -0.111921027302742,
+          -0.29863595962524414,
+          -0.11359372735023499,
+          0.1132974773645401,
+          0.1521414965391159,
+          0.15061387419700623,
+          0.3725407123565674,
+          -0.21611526608467102,
+          -0.14548304677009583,
+          -0.8435884118080139,
+          1.044625163078308,
+          46.90310287475586,
+          2.805375576019287,
+          0.20713452994823456,
+          0.761725902557373,
+          0.12126683443784714,
+          -4.903956413269043,
+          -0.11062023788690567,
+          0.10057520121335983,
+          -0.10482056438922882,
+          -12.464690208435059
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reciprocal float32 3D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            -1.011086269320547,
+            4.6215385203163315,
+            2.4173768502126727,
+            -8.934871773645101,
+            -3.348558485147799,
+            -8.803302423903414,
+            8.82632175955639,
+            6.572828698368518,
+            6.639494748363678,
+            2.6842704650429496,
+            -4.627160556770815,
+            -6.873653395070409,
+            -1.185412145486616,
+            0.9572811852263605,
+            0.021320551999082227,
+            0.35645850544829827,
+            4.827780063442164,
+            1.312808185701284,
+            8.246278209083616,
+            -0.20391698798075097,
+            -9.039937028172055,
+            9.94280947774324,
+            -9.540112102987099,
+            -0.08022661906399975
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          -0.9890353083610535,
+          0.21637815237045288,
+          0.4136715531349182,
+          -0.111921027302742,
+          -0.29863595962524414,
+          -0.11359372735023499,
+          0.1132974773645401,
+          0.1521414965391159,
+          0.15061387419700623,
+          0.3725407123565674,
+          -0.21611526608467102,
+          -0.14548304677009583,
+          -0.8435884118080139,
+          1.044625163078308,
+          46.90310287475586,
+          2.805375576019287,
+          0.20713452994823456,
+          0.761725902557373,
+          0.12126683443784714,
+          -4.903956413269043,
+          -0.11062023788690567,
+          0.10057520121335983,
+          -0.10482056438922882,
+          -12.464690208435059
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reciprocal float32 4D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -1.011086269320547,
+            4.6215385203163315,
+            2.4173768502126727,
+            -8.934871773645101,
+            -3.348558485147799,
+            -8.803302423903414,
+            8.82632175955639,
+            6.572828698368518,
+            6.639494748363678,
+            2.6842704650429496,
+            -4.627160556770815,
+            -6.873653395070409,
+            -1.185412145486616,
+            0.9572811852263605,
+            0.021320551999082227,
+            0.35645850544829827,
+            4.827780063442164,
+            1.312808185701284,
+            8.246278209083616,
+            -0.20391698798075097,
+            -9.039937028172055,
+            9.94280947774324,
+            -9.540112102987099,
+            -0.08022661906399975
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          -0.9890353083610535,
+          0.21637815237045288,
+          0.4136715531349182,
+          -0.111921027302742,
+          -0.29863595962524414,
+          -0.11359372735023499,
+          0.1132974773645401,
+          0.1521414965391159,
+          0.15061387419700623,
+          0.3725407123565674,
+          -0.21611526608467102,
+          -0.14548304677009583,
+          -0.8435884118080139,
+          1.044625163078308,
+          46.90310287475586,
+          2.805375576019287,
+          0.20713452994823456,
+          0.761725902557373,
+          0.12126683443784714,
+          -4.903956413269043,
+          -0.11062023788690567,
+          0.10057520121335983,
+          -0.10482056438922882,
+          -12.464690208435059
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reciprocal float32 5D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            -1.011086269320547,
+            4.6215385203163315,
+            2.4173768502126727,
+            -8.934871773645101,
+            -3.348558485147799,
+            -8.803302423903414,
+            8.82632175955639,
+            6.572828698368518,
+            6.639494748363678,
+            2.6842704650429496,
+            -4.627160556770815,
+            -6.873653395070409,
+            -1.185412145486616,
+            0.9572811852263605,
+            0.021320551999082227,
+            0.35645850544829827,
+            4.827780063442164,
+            1.312808185701284,
+            8.246278209083616,
+            -0.20391698798075097,
+            -9.039937028172055,
+            9.94280947774324,
+            -9.540112102987099,
+            -0.08022661906399975
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 1, 3],
+        "data": [
+          -0.9890353083610535,
+          0.21637815237045288,
+          0.4136715531349182,
+          -0.111921027302742,
+          -0.29863595962524414,
+          -0.11359372735023499,
+          0.1132974773645401,
+          0.1521414965391159,
+          0.15061387419700623,
+          0.3725407123565674,
+          -0.21611526608467102,
+          -0.14548304677009583,
+          -0.8435884118080139,
+          1.044625163078308,
+          46.90310287475586,
+          2.805375576019287,
+          0.20713452994823456,
+          0.761725902557373,
+          0.12126683443784714,
+          -4.903956413269043,
+          -0.11062023788690567,
+          0.10057520121335983,
+          -0.10482056438922882,
+          -12.464690208435059
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_l1.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_l1.json
new file mode 100644
index 0000000000..7cbc442511
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_l1.json
@@ -0,0 +1,743 @@
+{
+  "tests": [
+    {
+      "name": "reduceL1 float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 1D tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -98.83928567774318,
+            -57.66743928106195,
+            -57.101201850012814,
+            -6.693042101576822,
+            -45.305847421047595,
+            -86.68338950685379,
+            -74.71875033016443,
+            -76.46740234611214,
+            -75.37676973458242,
+            -18.22093937407911,
+            -54.64426752595342,
+            -36.45240031615904,
+            -18.322682023656796,
+            -47.94379514838312,
+            -40.1997828250384,
+            -15.830483512310494,
+            -48.88335705253115,
+            -41.60024225085446,
+            -20.65563368305152,
+            -92.29933856234294,
+            -46.288581883419646,
+            -80.57185804741187,
+            -25.494730168492268,
+            -48.96730121051369
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1215.228515625,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            18,
+            29,
+            35,
+            36,
+            4,
+            76,
+            41,
+            18,
+            53,
+            29,
+            25,
+            94,
+            26,
+            1,
+            3,
+            68,
+            39,
+            25,
+            87,
+            30,
+            39,
+            75,
+            76,
+            66
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 993,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -92,
+            -52,
+            -88,
+            -78,
+            -20,
+            -73,
+            -42,
+            -57,
+            -39,
+            -75,
+            -17,
+            -36,
+            -81,
+            -24,
+            -88,
+            -91,
+            -76,
+            -5,
+            -44,
+            -66,
+            -96,
+            -8,
+            -69,
+            -27
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1344,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
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+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
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+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
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+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
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+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          142.01541137695312,
+          106.62430572509766,
+          175.39280700683594,
+          286.7269592285156,
+          169.36322021484375,
+          212.59750366210938
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          258.57110595703125,
+          174.42807006835938,
+          102.19830322265625,
+          134.52191162109375,
+          207.92910766601562,
+          215.07168579101562
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          1092.72021484375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 1092.72021484375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          1092.72021484375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          108.43173217773438,
+          315.6007995605469,
+          359.5506591796875,
+          309.13702392578125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL1 float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            5.508826448139126,
+            50.61575563515446,
+            1.6773050539454992,
+            84.21352292914005,
+            15.664374631289203,
+            52.89714899357876,
+            9.12515782683123,
+            28.93762412170997,
+            12.567061064158146,
+            11.399989904078245,
+            86.91246376729916,
+            64.51329469980782,
+            71.28342153263038,
+            76.34410635436484,
+            41.53409150301335,
+            97.56533284219819,
+            31.803831597266385,
+            6.0897547388209405,
+            61.70843555684953,
+            69.76119433406723,
+            38.91940474525253,
+            52.28833355637117,
+            22.31783714884341,
+            99.0719229634137
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          108.43173217773438,
+          315.6007995605469,
+          359.5506591796875,
+          309.13702392578125
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_l2.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_l2.json
new file mode 100644
index 0000000000..7e59a45d5e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_l2.json
@@ -0,0 +1,743 @@
+{
+  "tests": [
+    {
+      "name": "reduceL2 float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
+            6.855991445231058,
+            91.3987084155092,
+            41.888577644260664,
+            73.65445188651776,
+            35.31573479319383,
+            48.345430062443896,
+            82.3919051922353,
+            77.86200561022456,
+            93.31141688733462,
+            62.486883544675194,
+            60.292898644959706,
+            13.23059966965019,
+            20.535988317971942,
+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 1D tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
+            6.855991445231058,
+            91.3987084155092,
+            41.888577644260664,
+            73.65445188651776,
+            35.31573479319383,
+            48.345430062443896,
+            82.3919051922353,
+            77.86200561022456,
+            93.31141688733462,
+            62.486883544675194,
+            60.292898644959706,
+            13.23059966965019,
+            20.535988317971942,
+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -66.80042739154972,
+            -53.00005108985402,
+            -59.58587770117833,
+            -46.14393008028177,
+            -49.60613922872308,
+            -12.832738832929593,
+            -88.05061277508285,
+            -75.56246522621561,
+            -50.767774970060664,
+            -36.96630141451547,
+            -26.34404432713906,
+            -58.905464928590476,
+            -94.28752832403735,
+            -22.780279721601616,
+            -84.34877530084705,
+            -60.477345782692815,
+            -41.45580712299912,
+            -92.84781353832356,
+            -85.0544854150549,
+            -30.23526041853337,
+            -47.33807956591096,
+            -25.26842900087003,
+            -78.1195878278582,
+            -28.3309437256642
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 292.57574462890625,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            4,
+            29,
+            8,
+            56,
+            42,
+            78,
+            89,
+            64,
+            56,
+            81,
+            85,
+            18,
+            6,
+            39,
+            35,
+            63,
+            87,
+            50,
+            81,
+            89,
+            5,
+            8,
+            37,
+            37
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 274.4029846191406,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -70,
+            -78,
+            -65,
+            -77,
+            -25,
+            -47,
+            -63,
+            -67,
+            -66,
+            -15,
+            -28,
+            -75,
+            -88,
+            -54,
+            -13,
+            -27,
+            -5,
+            -18,
+            -68,
+            -71,
+            -50,
+            -56,
+            -99,
+            -99
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 300.3830871582031,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
+            4.860227954324237,
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            4.860227954324237,
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+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
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+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            4.860227954324237,
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+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
+            6.855991445231058,
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+            73.65445188651776,
+            35.31573479319383,
+            48.345430062443896,
+            82.3919051922353,
+            77.86200561022456,
+            93.31141688733462,
+            62.486883544675194,
+            60.292898644959706,
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+            53.451616950541414,
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+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          122.352783203125,
+          124.8213119506836,
+          128.20062255859375,
+          128.14801025390625,
+          87.18083953857422,
+          55.043975830078125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
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+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          114.44775390625,
+          110.26422882080078,
+          133.47344970703125,
+          64.96752166748047,
+          128.0914764404297,
+          101.677734375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
+            6.855991445231058,
+            91.3987084155092,
+            41.888577644260664,
+            73.65445188651776,
+            35.31573479319383,
+            48.345430062443896,
+            82.3919051922353,
+            77.86200561022456,
+            93.31141688733462,
+            62.486883544675194,
+            60.292898644959706,
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+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
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+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          272.0996398925781
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
+            6.855991445231058,
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+            48.345430062443896,
+            82.3919051922353,
+            77.86200561022456,
+            93.31141688733462,
+            62.486883544675194,
+            60.292898644959706,
+            13.23059966965019,
+            20.535988317971942,
+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 272.0996398925781,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
+            64.75027573238448,
+            6.855991445231058,
+            91.3987084155092,
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+            93.31141688733462,
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+            60.292898644959706,
+            13.23059966965019,
+            20.535988317971942,
+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          272.0996398925781
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
+            54.489688892595865,
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+            77.86200561022456,
+            93.31141688733462,
+            62.486883544675194,
+            60.292898644959706,
+            13.23059966965019,
+            20.535988317971942,
+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          138.580078125,
+          166.67791748046875,
+          149.91552734375,
+          67.6578598022461
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceL2 float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            4.860227954324237,
+            88.23184532015614,
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+            53.451616950541414,
+            11.32008585820996,
+            64.75763553379184,
+            43.65894603418228,
+            0.8374307376408652,
+            0.6848266407317727,
+            33.504704671527506
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          138.580078125,
+          166.67791748046875,
+          149.91552734375,
+          67.6578598022461
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_log_sum.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_log_sum.json
new file mode 100644
index 0000000000..250398d227
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_log_sum.json
@@ -0,0 +1,663 @@
+{
+  "tests": [
+    {
+      "name": "reduceLogSum float32 1D constant tensor all non-negative default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
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+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 1D tensor all non-negative default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 1D tensor all non-negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            63,
+            82,
+            49,
+            23,
+            98,
+            67,
+            15,
+            9,
+            89,
+            7,
+            69,
+            61,
+            47,
+            50,
+            41,
+            39,
+            58,
+            52,
+            35,
+            83,
+            81,
+            7,
+            34,
+            9
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.063048362731934,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
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+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
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+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
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+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          5.593751907348633,
+          4.773046016693115,
+          5.3115739822387695,
+          5.2497639656066895,
+          4.973392486572266,
+          5.373587131500244
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
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+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          5.410027980804443,
+          5.367736339569092,
+          5.399682998657227,
+          4.652334213256836,
+          4.744638442993164,
+          5.565346717834473
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          7.039101600646973
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 7.039101600646973,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          7.039101600646973
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
+            50.394988743199434,
+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          5.7273993492126465,
+          5.64375114440918,
+          5.453810214996338,
+          5.758983135223389
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSum float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            64.54826901463852,
+            97.87423374006126,
+            26.529027351852186,
+            79.79046651888517,
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+            14.578406888832252,
+            20.86681803845336,
+            32.43873432263156,
+            64.91234005889837,
+            71.54029915166349,
+            11.13706890220647,
+            55.07930697185586,
+            43.791352286892106,
+            13.831947671728972,
+            97.39019470916091,
+            35.50775651228035,
+            52.275862097336166,
+            82.838655037009,
+            8.568099335705881,
+            0.8337112078307563,
+            69.23145833259072,
+            3.8541642374881446,
+            70.55673656397397,
+            71.99264788951747
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          5.7273993492126465,
+          5.64375114440918,
+          5.453810214996338,
+          5.758983135223389
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_log_sum_exp.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_log_sum_exp.json
new file mode 100644
index 0000000000..b7f39abd52
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_log_sum_exp.json
@@ -0,0 +1,743 @@
+{
+  "tests": [
+    {
+      "name": "reduceLogSumExp float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            0.7974132976078829,
+            5.046889970229296,
+            8.520371090961714,
+            1.4063042363728728,
+            0.11882661415275164,
+            0.285854490277071,
+            1.9325640291685864,
+            3.7939957856803797,
+            2.6040233063490925,
+            4.937509735746763,
+            4.571482286709769,
+            0.7865120436262685,
+            0.21018670996660216,
+            9.063042370844672,
+            4.099809553408898,
+            4.596248815627706,
+            0.2549232349678032,
+            1.1594801326588255,
+            6.802876838759606,
+            5.23432518570901,
+            8.914905562420849,
+            9.166799240859705,
+            5.71750713886038,
+            0.3255050658627967
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 10.39477825164795,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSumExp float32 1D tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            0.7974132976078829,
+            5.046889970229296,
+            8.520371090961714,
+            1.4063042363728728,
+            0.11882661415275164,
+            0.285854490277071,
+            1.9325640291685864,
+            3.7939957856803797,
+            2.6040233063490925,
+            4.937509735746763,
+            4.571482286709769,
+            0.7865120436262685,
+            0.21018670996660216,
+            9.063042370844672,
+            4.099809553408898,
+            4.596248815627706,
+            0.2549232349678032,
+            1.1594801326588255,
+            6.802876838759606,
+            5.23432518570901,
+            8.914905562420849,
+            9.166799240859705,
+            5.71750713886038,
+            0.3255050658627967
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 10.39477825164795,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSumExp float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -4.025670276125137,
+            -9.44434807349057,
+            -3.1193981742372,
+            -5.943697868896303,
+            -0.37018045102283637,
+            -4.3971262954406605,
+            -6.6059682563332744,
+            -5.534277354031625,
+            -7.361471172291552,
+            -1.9987547534811796,
+            -9.093968158437377,
+            -8.693618799659165,
+            -8.416788434713155,
+            -1.010741070662755,
+            -9.814584506221344,
+            -9.725259665431539,
+            -9.157071010493805,
+            -0.0016988189527857855,
+            -9.963414768875285,
+            -5.991659641614526,
+            -6.180599892028498,
+            -1.2336505614624649,
+            -0.44234340543470196,
+            -6.990072309054243
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1.1666961908340454,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSumExp float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            1,
+            5,
+            7,
+            5,
+            7,
+            5,
+            4,
+            2,
+            1,
+            5,
+            8,
+            2,
+            4,
+            1,
+            4,
+            5,
+            4,
+            8,
+            6,
+            2,
+            7,
+            7,
+            8,
+            5
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 9.607237815856934,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSumExp float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -6,
+            -3,
+            -5,
+            -1,
+            -9,
+            -5,
+            -1,
+            -2,
+            -10,
+            -1,
+            -5,
+            -7,
+            -7,
+            -3,
+            -10,
+            -10,
+            -8,
+            -6,
+            -2,
+            -6,
+            -1,
+            -9,
+            -5,
+            -2
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 0.7001367211341858,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceLogSumExp float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
+            0.7974132976078829,
+            5.046889970229296,
+            8.520371090961714,
+            1.4063042363728728,
+            0.11882661415275164,
+            0.285854490277071,
+            1.9325640291685864,
+            3.7939957856803797,
+            2.6040233063490925,
+            4.937509735746763,
+            4.571482286709769,
+            0.7865120436262685,
+            0.21018670996660216,
+            9.063042370844672,
+            4.099809553408898,
+            4.596248815627706,
+            0.2549232349678032,
+            1.1594801326588255,
+            6.802876838759606,
+            5.23432518570901,
+            8.914905562420849,
+            9.166799240859705,
+            5.71750713886038,
+            0.3255050658627967
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 10.39477825164795,
+        "type": "float32"
+      }
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+    {
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_max.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_max.json
new file mode 100644
index 0000000000..967aea8bf4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_max.json
@@ -0,0 +1,623 @@
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+      "expected": {
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+        "data": 99.77313232421875,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
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+      "expected": {
+        "name": "output",
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            32.166585683408215,
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+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
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+          94.99645233154297,
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+          99.77313232421875,
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+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            32.166585683408215,
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
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+        "shape": [2, 3],
+        "data": [
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 99.77313232421875,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            32.166585683408215,
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          99.77313232421875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 99.77313232421875,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          99.77313232421875
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMax float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          90.42288208007812,
+          94.99645233154297,
+          96.55397033691406,
+          99.77313232421875
+        ],
+        "type": "float32"
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+    },
+    {
+      "name": "reduceMax float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+            -51.287275192229046,
+            -42.588327517032454
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
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+          94.99645233154297,
+          96.55397033691406,
+          99.77313232421875
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_mean.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_mean.json
new file mode 100644
index 0000000000..5a48952c06
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_mean.json
@@ -0,0 +1,735 @@
+{
+  "tests": [
+    {
+      "name": "reduceMean float32 1D constant tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+            21.637750690378144,
+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 1D tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+            75.69370054099012,
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+            21.637750690378144,
+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
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+      "expected": {
+        "name": "output",
+        "data": -34.74319839477539,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            42,
+            24,
+            44,
+            38,
+            82,
+            93,
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+            8,
+            21,
+            19,
+            62,
+            71
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 48.41666793823242,
+        "type": "float32"
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+    },
+    {
+      "name": "reduceMean float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -73,
+            -8,
+            -55,
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+            -72,
+            -67,
+            -16,
+            -21,
+            -31
+          ],
+          "type": "float32"
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+      },
+      "expected": {
+        "name": "output",
+        "data": -47.54166793823242,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
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+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            14.160698325191756,
+            15.127351396412191
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 40.31047439575195,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceMean float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+            75.69370054099012,
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_min.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_min.json
new file mode 100644
index 0000000000..92de75e92a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_min.json
@@ -0,0 +1,623 @@
+{
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_product.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_product.json
new file mode 100644
index 0000000000..691bf4da9b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_product.json
@@ -0,0 +1,623 @@
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+          1280298.5,
+          -1343475.375,
+          1280118.75
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
+            79.97555172142995,
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+            49.231095225588376,
+            30.058706923717068,
+            69.45106401985521,
+            -20.15709647137976,
+            -58.02551401530734,
+            51.89660954258355,
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+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 1.5855958784642327e+37,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
+            79.97555172142995,
+            14.46502415218545,
+            49.231095225588376,
+            30.058706923717068,
+            69.45106401985521,
+            -20.15709647137976,
+            -58.02551401530734,
+            51.89660954258355,
+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
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+            -85.19523905815305,
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+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          1.5855958784642327e+37
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
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+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
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+            -20.15709647137976,
+            -58.02551401530734,
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+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 1.5855958784642327e+37,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
+            79.97555172142995,
+            14.46502415218545,
+            49.231095225588376,
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+            69.45106401985521,
+            -20.15709647137976,
+            -58.02551401530734,
+            51.89660954258355,
+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          1.5855958784642327e+37
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
+            -22.69974404385384,
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+            -58.02551401530734,
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+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          -3638925568,
+          6523364352,
+          -414643360,
+          1610916352
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceProduct float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            -68.75911760233478,
+            99.4496130391401,
+            24.860555622046746,
+            -44.23515424391753,
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+            30.058706923717068,
+            69.45106401985521,
+            -20.15709647137976,
+            -58.02551401530734,
+            51.89660954258355,
+            -2.020799112914105,
+            39.392973241316554,
+            26.780734701055266,
+            -97.97651593223856,
+            48.66154478867011,
+            -85.19523905815305,
+            -18.169861657911653,
+            64.83759066937682,
+            -14.958838503675935,
+            -74.50931980014732,
+            -11.319679210869765
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          -3638925568,
+          6523364352,
+          -414643360,
+          1610916352
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_sum.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_sum.json
new file mode 100644
index 0000000000..df47a1a2b2
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_sum.json
@@ -0,0 +1,735 @@
+{
+  "tests": [
+    {
+      "name": "reduceSum float32 1D constant tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
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+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all positive default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
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+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -51.77016701194195,
+            -34.46467785404002,
+            -40.983506494422414,
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+            -20.281060841405974,
+            -20.123054810748656,
+            -83.63451764199297,
+            -23.651085471445683,
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+            -36.212902987180804,
+            -76.26201536868358,
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+            -53.88933855716023,
+            -67.52341028201596,
+            -71.14580362299527,
+            -82.04484921713924,
+            -96.29924980334548,
+            -68.46700365583722,
+            -26.107193300700942,
+            -68.01821308673175,
+            -4.833076925370335,
+            -48.90069809349882
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -1163.642578125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            56,
+            90,
+            67,
+            33,
+            20,
+            58,
+            22,
+            15,
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+            79,
+            59,
+            99,
+            16,
+            95,
+            67,
+            11,
+            60,
+            89,
+            50,
+            57,
+            77,
+            89,
+            10,
+            2
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1307,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -55,
+            -36,
+            -74,
+            -17,
+            -67,
+            -95,
+            -3,
+            -67,
+            -95,
+            -13,
+            -45,
+            -9,
+            -33,
+            -98,
+            -86,
+            -11,
+            -70,
+            -44,
+            -31,
+            -68,
+            -79,
+            -24,
+            -60,
+            -36
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": -1216,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
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+            99.1748548274599,
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+          "type": "float32"
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+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            60.17613590317996
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
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+            99.1748548274599,
+            32.782341359545676,
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+            32.64223805640243,
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+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
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+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
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+            99.1748548274599,
+            32.782341359545676,
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+            57.60172895173401,
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+            25.62685451963782,
+            60.17613590317996
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+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
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+            99.1748548274599,
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+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
+          210.44241333007812,
+          104.54179382324219,
+          226.2177734375,
+          239.025390625,
+          260.405029296875,
+          273.2470397949219
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
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+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
+        "shape": [2, 3],
+        "data": [
+          179.63900756835938,
+          260.37457275390625,
+          219.3611297607422,
+          246.83712768554688,
+          157.4895782470703,
+          250.1780242919922
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "shape": [1, 1, 1],
+        "data": [
+          1313.87939453125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "data": 1313.87939453125,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "shape": [1, 1, 1, 1],
+        "data": [
+          1313.87939453125
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "shape": [2, 2],
+        "data": [
+          355.21942138671875,
+          185.98255920410156,
+          362.3598937988281,
+          410.3175354003906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSum float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            69.6038605453285,
+            99.1748548274599,
+            32.782341359545676,
+            8.881362932730319,
+            16.094296257493014,
+            11.806894918510213,
+            32.64223805640243,
+            43.998363476438975,
+            77.01777099153088,
+            79.79425759062173,
+            45.007949804090174,
+            24.39779644056914,
+            57.50268541631618,
+            57.60172895173401,
+            80.26985885745457,
+            43.65110628848945,
+            87.50368667545843,
+            94.50203768533922,
+            35.54289690580114,
+            42.856416416096344,
+            88.58631651618441,
+            98.85772999308332,
+            25.62685451963782,
+            60.17613590317996
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "shape": [1, 2, 2, 1],
+        "data": [
+          355.21942138671875,
+          185.98255920410156,
+          362.3598937988281,
+          410.3175354003906
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reduce_sum_square.json b/testing/web-platform/tests/webnn/resources/test_data/reduce_sum_square.json
new file mode 100644
index 0000000000..8ac373e4b3
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reduce_sum_square.json
@@ -0,0 +1,743 @@
+{
+  "tests": [
+    {
+      "name": "reduceSumSquare float32 1D constant tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 1D tensor all positive default options", // default options: {axes: null, keepDimensions: false}
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 1D tensor all negative default options",
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -21.452013864072896,
+            -57.30725907408611,
+            -72.83901116638569,
+            -0.059761520988928396,
+            -71.73678939977705,
+            -44.61909261218302,
+            -43.120022676836946,
+            -91.33738230600645,
+            -33.172438449832086,
+            -48.55593151678299,
+            -95.62864137865712,
+            -20.876631045259614,
+            -16.69083735987158,
+            -39.52110386011864,
+            -7.51078560359592,
+            -90.59026921164444,
+            -42.21683259824742,
+            -76.74274815005997,
+            -98.2242026863753,
+            -60.27295167122231,
+            -74.7320278737084,
+            -8.543684369072977,
+            -59.88873484887647,
+            -17.99894136421551
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 80052.015625,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 1D tensor all positive integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            52,
+            48,
+            2,
+            66,
+            30,
+            39,
+            14,
+            23,
+            81,
+            94,
+            78,
+            64,
+            38,
+            16,
+            63,
+            11,
+            46,
+            95,
+            17,
+            47,
+            40,
+            53,
+            87,
+            43
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 71347,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 1D tensor all negative integers default options", // test with float32 values which are equivalent to integers
+      "inputs": {
+        "input": {
+          "shape": [24],
+          "data": [
+            -10,
+            -60,
+            -69,
+            -88,
+            -35,
+            -84,
+            -74,
+            -42,
+            -93,
+            -26,
+            -40,
+            -55,
+            -92,
+            -26,
+            -39,
+            -30,
+            -61,
+            -16,
+            -16,
+            -36,
+            -9,
+            -89,
+            -45,
+            -29
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73634,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 2D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [4, 6],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 3D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 4D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 5D tensor default options",
+      "inputs": {
+        "input": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 3D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          9709.013671875,
+          10817.7685546875,
+          14548.470703125,
+          16385.8515625,
+          9287.357421875,
+          12527.3974609375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 4D tensor options.axes",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [0, 2]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3],
+        "data": [
+          8585.87109375,
+          7700.654296875,
+          19889.1796875,
+          7113.0439453125,
+          16775.708984375,
+          13211.3994140625
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 3D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 3D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
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+            13.493006439137023,
+            44.15238362662202,
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+            70.20919468463771,
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+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1],
+        "data": [
+          73275.859375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 4D tensor options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
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+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": false
+      },
+      "expected": {
+        "name": "output",
+        "data": 73275.859375,
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 4D tensor options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
+            83.93837980977771,
+            47.44504659398938,
+            19.177289239472593,
+            13.493006439137023,
+            44.15238362662202,
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+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "keepDimensions": true
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 1, 1, 1],
+        "data": [
+          73275.859375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 4D tensor options.axes with options.keepDimensions=false",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
+            33.96051675758925,
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+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2],
+        "data": [
+          12302.474609375,
+          22772.77734375,
+          26919.09765625,
+          11281.5068359375
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "reduceSumSquare float32 4D tensor options.axes with options.keepDimensions=true",
+      "inputs": {
+        "input": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            52.5615351837915,
+            2.6261063680439234,
+            82.04877759500097,
+            14.401411284784604,
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+            13.493006439137023,
+            44.15238362662202,
+            86.53118079678906,
+            70.20919468463771,
+            25.67262870756528,
+            79.73770192548034,
+            66.42284577584445,
+            70.40363336301188,
+            13.503327808732468,
+            41.22539754662389,
+            6.654552839238637,
+            85.79743668166222,
+            89.91348950671976,
+            53.55647451130674,
+            39.48537158474081,
+            3.9460408540454184
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "axes": [1, 3]
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 2, 1],
+        "data": [
+          12302.474609375,
+          22772.77734375,
+          26919.09765625,
+          11281.5068359375
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/relu.json b/testing/web-platform/tests/webnn/resources/test_data/relu.json
new file mode 100644
index 0000000000..248902c665
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/relu.json
@@ -0,0 +1,401 @@
+{
+  "tests": [ // relu input tensor of 1D to 5D with same data values
+  {
+    "name": "relu float32 1D constant tensor",
+    "inputs": {
+      "x": {
+        "shape": [24],
+        "data": [
+          79.04725231657116,
+          2.2503608756501166,
+          80.73939090529203,
+          63.90392076789547,
+          77.67340745512104,
+          -71.09157819044825,
+          -82.74703468427575,
+          -26.814426600801028,
+          -99.16788836397058,
+          -35.71083406288831,
+          18.36165830990626,
+          -37.36091648205435,
+          -52.8386119809162,
+          -10.408374773008958,
+          60.60291560236189,
+          -13.644198913810342,
+          -76.54250291031946,
+          -8.132338049258351,
+          51.51447452437017,
+          -51.63370281825297,
+          -64.567999424324,
+          -5.093302411117136,
+          15.354103550744384,
+          90.03858807393516
+        ],
+        "type": "float32",
+        "constant": true
+      }
+    },
+    "expected": {
+      "name": "output",
+      "shape": [24],
+      "data": [
+        79.04724884033203,
+        2.2503609657287598,
+        80.73938751220703,
+        63.9039192199707,
+        77.67340850830078,
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+        0,
+        0,
+        0,
+        0,
+        18.361658096313477,
+        0,
+        0,
+        0,
+        60.6029167175293,
+        0,
+        0,
+        0,
+        51.51447296142578,
+        0,
+        0,
+        0,
+        15.354103088378906,
+        90.03858947753906
+      ],
+      "type": "float32"
+    }
+  },
+    {
+      "name": "relu float32 1D tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            79.04725231657116,
+            2.2503608756501166,
+            80.73939090529203,
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+            -35.71083406288831,
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+            -64.567999424324,
+            -5.093302411117136,
+            15.354103550744384,
+            90.03858807393516
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          79.04724884033203,
+          2.2503609657287598,
+          80.73938751220703,
+          63.9039192199707,
+          77.67340850830078,
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+          0,
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+          0,
+          0,
+          0,
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+          0,
+          0,
+          0,
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+          0,
+          0,
+          0,
+          15.354103088378906,
+          90.03858947753906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "relu float32 2D tensor",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            79.04725231657116,
+            2.2503608756501166,
+            80.73939090529203,
+            63.90392076789547,
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+            -71.09157819044825,
+            -82.74703468427575,
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+            -35.71083406288831,
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+            -52.8386119809162,
+            -10.408374773008958,
+            60.60291560236189,
+            -13.644198913810342,
+            -76.54250291031946,
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+            -51.63370281825297,
+            -64.567999424324,
+            -5.093302411117136,
+            15.354103550744384,
+            90.03858807393516
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          79.04724884033203,
+          2.2503609657287598,
+          80.73938751220703,
+          63.9039192199707,
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+          0,
+          0,
+          0,
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+          0,
+          0,
+          0,
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+          0,
+          0,
+          0,
+          51.51447296142578,
+          0,
+          0,
+          0,
+          15.354103088378906,
+          90.03858947753906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "relu float32 3D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            79.04725231657116,
+            2.2503608756501166,
+            80.73939090529203,
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+            -99.16788836397058,
+            -35.71083406288831,
+            18.36165830990626,
+            -37.36091648205435,
+            -52.8386119809162,
+            -10.408374773008958,
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+            -13.644198913810342,
+            -76.54250291031946,
+            -8.132338049258351,
+            51.51447452437017,
+            -51.63370281825297,
+            -64.567999424324,
+            -5.093302411117136,
+            15.354103550744384,
+            90.03858807393516
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          79.04724884033203,
+          2.2503609657287598,
+          80.73938751220703,
+          63.9039192199707,
+          77.67340850830078,
+          0,
+          0,
+          0,
+          0,
+          0,
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+          0,
+          0,
+          0,
+          60.6029167175293,
+          0,
+          0,
+          0,
+          51.51447296142578,
+          0,
+          0,
+          0,
+          15.354103088378906,
+          90.03858947753906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "relu float32 4D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            79.04725231657116,
+            2.2503608756501166,
+            80.73939090529203,
+            63.90392076789547,
+            77.67340745512104,
+            -71.09157819044825,
+            -82.74703468427575,
+            -26.814426600801028,
+            -99.16788836397058,
+            -35.71083406288831,
+            18.36165830990626,
+            -37.36091648205435,
+            -52.8386119809162,
+            -10.408374773008958,
+            60.60291560236189,
+            -13.644198913810342,
+            -76.54250291031946,
+            -8.132338049258351,
+            51.51447452437017,
+            -51.63370281825297,
+            -64.567999424324,
+            -5.093302411117136,
+            15.354103550744384,
+            90.03858807393516
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          79.04724884033203,
+          2.2503609657287598,
+          80.73938751220703,
+          63.9039192199707,
+          77.67340850830078,
+          0,
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+          0,
+          0,
+          0,
+          18.361658096313477,
+          0,
+          0,
+          0,
+          60.6029167175293,
+          0,
+          0,
+          0,
+          51.51447296142578,
+          0,
+          0,
+          0,
+          15.354103088378906,
+          90.03858947753906
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "relu float32 5D tensor",
+      "inputs": {
+        "x": {
+          "shape": [2, 1, 4, 1, 3],
+          "data": [
+            79.04725231657116,
+            2.2503608756501166,
+            80.73939090529203,
+            63.90392076789547,
+            77.67340745512104,
+            -71.09157819044825,
+            -82.74703468427575,
+            -26.814426600801028,
+            -99.16788836397058,
+            -35.71083406288831,
+            18.36165830990626,
+            -37.36091648205435,
+            -52.8386119809162,
+            -10.408374773008958,
+            60.60291560236189,
+            -13.644198913810342,
+            -76.54250291031946,
+            -8.132338049258351,
+            51.51447452437017,
+            -51.63370281825297,
+            -64.567999424324,
+            -5.093302411117136,
+            15.354103550744384,
+            90.03858807393516
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 1, 4, 1, 3],
+        "data": [
+          79.04724884033203,
+          2.2503609657287598,
+          80.73938751220703,
+          63.9039192199707,
+          77.67340850830078,
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+          0,
+          0,
+          0,
+          0,
+          18.361658096313477,
+          0,
+          0,
+          0,
+          60.6029167175293,
+          0,
+          0,
+          0,
+          51.51447296142578,
+          0,
+          0,
+          0,
+          15.354103088378906,
+          90.03858947753906
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reshape.json b/testing/web-platform/tests/webnn/resources/test_data/reshape.json
new file mode 100644
index 0000000000..ada3daa4e2
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reshape.json
@@ -0,0 +1,2056 @@
+{
+  "tests": [
+    {
+      "name": "reshape float32 tensor to a new shape (reorder all dimensions)",
+      "inputs": {
+        "input": {
+          "shape": [2, 3, 4],
+          "data": [
+            -30.0561466217041,
+            99.56941986083984,
+            88.04620361328125,
+            -91.87507629394531,
+            -23.7972354888916,
+            -91.28665161132812,
+            -63.15204620361328,
+            12.0669527053833,
+            -96.1172866821289,
+            -44.77365493774414,
+            -80.08650970458984,
+            -64.43756866455078,
+            27.64195442199707,
+            -96.86306762695312,
+            83.6834716796875,
+            50.599483489990234,
+            -20.18765640258789,
+            -1.3904608488082886,
+            -96.93603515625,
+            65.34143829345703,
+            34.835994720458984,
+            62.01485824584961,
+            -2.8698415756225586,
+            27.903749465942383
+          ],
+          "type": "float32"
+        }
+      },
+      "newShape": [4, 2, 3],
+      "expected": {
+        "name": "output",
+        "shape": [4, 2, 3],
+        "data": [
+          -30.0561466217041,
+          99.56941986083984,
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/sigmoid.json b/testing/web-platform/tests/webnn/resources/test_data/sigmoid.json
new file mode 100644
index 0000000000..69756fbb9c
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/sigmoid.json
@@ -0,0 +1,401 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/sin.json b/testing/web-platform/tests/webnn/resources/test_data/sin.json
new file mode 100644
index 0000000000..1fe76df45f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/sin.json
@@ -0,0 +1,401 @@
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/slice.json b/testing/web-platform/tests/webnn/resources/test_data/slice.json
new file mode 100644
index 0000000000..28cfd2c0e2
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/slice.json
@@ -0,0 +1,311 @@
+{
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/softmax.json b/testing/web-platform/tests/webnn/resources/test_data/softmax.json
new file mode 100644
index 0000000000..ebb12114fc
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/softmax.json
@@ -0,0 +1,203 @@
+{
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+            -3.7807213277512064,
+            -6.034926915403203,
+            -3.9196677453714273,
+            -2.223484249593408,
+            -9.326531526135806,
+            -1.4882492282868132,
+            -6.302842393071306,
+            -5.531475594479687,
+            -1.8421411696525603,
+            -4.994808274460977,
+            -9.527291818875955,
+            -4.985682906583053,
+            -8.421041200931548,
+            -6.235629579015209
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          0.2546302080154419,
+          0.24781952798366547,
+          0.2307596504688263,
+          0.008623254485428333,
+          0.002599793951958418,
+          0.2555675804615021,
+          0.40352678298950195,
+          0.13637976348400116,
+          0.0009232329903170466,
+          0.20185552537441254,
+          0.23287305235862732,
+          0.024441635236144066,
+          0.0551743283867836,
+          0.3008708655834198,
+          0.0002474947541486472,
+          0.6276082992553711,
+          0.0050902292132377625,
+          0.011008745059370995,
+          0.9090295433998108,
+          0.0388500951230526,
+          0.00041779119055718184,
+          0.039206232875585556,
+          0.0012629841221496463,
+          0.011233373545110226
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/softplus.json b/testing/web-platform/tests/webnn/resources/test_data/softplus.json
new file mode 100644
index 0000000000..eb05b7b281
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/softplus.json
@@ -0,0 +1,539 @@
+{ // softplus: The calculation follows the expression ln(1 + exp(steepness * x)) / steepness.
+  "tests": [
+    {
+      "name": "softplus float32 1D constant tensor default options", // default options: {steepness: 1}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          5.630208969116211,
+          5.1731696128845215,
+          4.032524108886719,
+          9.480109214782715,
+          9.989983558654785,
+          7.0662946701049805,
+          2.2446866035461426,
+          8.187429428100586,
+          5.175644874572754,
+          2.219529390335083,
+          3.552403688430786,
+          4.152195453643799,
+          1.903303623199463,
+          5.151034355163574,
+          5.022127628326416,
+          0.7163625359535217,
+          3.007754325866699,
+          3.978142499923706,
+          5.521933078765869,
+          7.1930742263793945,
+          8.765081405639648,
+          1.5991919040679932,
+          8.930801391601562,
+          8.660456657409668
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus float32 1D tensor default options", // default options: {steepness: 1}
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          5.630208969116211,
+          5.1731696128845215,
+          4.032524108886719,
+          9.480109214782715,
+          9.989983558654785,
+          7.0662946701049805,
+          2.2446866035461426,
+          8.187429428100586,
+          5.175644874572754,
+          2.219529390335083,
+          3.552403688430786,
+          4.152195453643799,
+          1.903303623199463,
+          5.151034355163574,
+          5.022127628326416,
+          0.7163625359535217,
+          3.007754325866699,
+          3.978142499923706,
+          5.521933078765869,
+          7.1930742263793945,
+          8.765081405639648,
+          1.5991919040679932,
+          8.930801391601562,
+          8.660456657409668
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus float32 2D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [4, 6],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [4, 6],
+        "data": [
+          5.630208969116211,
+          5.1731696128845215,
+          4.032524108886719,
+          9.480109214782715,
+          9.989983558654785,
+          7.0662946701049805,
+          2.2446866035461426,
+          8.187429428100586,
+          5.175644874572754,
+          2.219529390335083,
+          3.552403688430786,
+          4.152195453643799,
+          1.903303623199463,
+          5.151034355163574,
+          5.022127628326416,
+          0.7163625359535217,
+          3.007754325866699,
+          3.978142499923706,
+          5.521933078765869,
+          7.1930742263793945,
+          8.765081405639648,
+          1.5991919040679932,
+          8.930801391601562,
+          8.660456657409668
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus float32 3D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [2, 3, 4],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 3, 4],
+        "data": [
+          5.630208969116211,
+          5.1731696128845215,
+          4.032524108886719,
+          9.480109214782715,
+          9.989983558654785,
+          7.0662946701049805,
+          2.2446866035461426,
+          8.187429428100586,
+          5.175644874572754,
+          2.219529390335083,
+          3.552403688430786,
+          4.152195453643799,
+          1.903303623199463,
+          5.151034355163574,
+          5.022127628326416,
+          0.7163625359535217,
+          3.007754325866699,
+          3.978142499923706,
+          5.521933078765869,
+          7.1930742263793945,
+          8.765081405639648,
+          1.5991919040679932,
+          8.930801391601562,
+          8.660456657409668
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus float32 4D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
+          5.630208969116211,
+          5.1731696128845215,
+          4.032524108886719,
+          9.480109214782715,
+          9.989983558654785,
+          7.0662946701049805,
+          2.2446866035461426,
+          8.187429428100586,
+          5.175644874572754,
+          2.219529390335083,
+          3.552403688430786,
+          4.152195453643799,
+          1.903303623199463,
+          5.151034355163574,
+          5.022127628326416,
+          0.7163625359535217,
+          3.007754325866699,
+          3.978142499923706,
+          5.521933078765869,
+          7.1930742263793945,
+          8.765081405639648,
+          1.5991919040679932,
+          8.930801391601562,
+          8.660456657409668
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus float32 5D tensor default options",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 1, 3, 4],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 1, 3, 4],
+        "data": [
+          5.630208969116211,
+          5.1731696128845215,
+          4.032524108886719,
+          9.480109214782715,
+          9.989983558654785,
+          7.0662946701049805,
+          2.2446866035461426,
+          8.187429428100586,
+          5.175644874572754,
+          2.219529390335083,
+          3.552403688430786,
+          4.152195453643799,
+          1.903303623199463,
+          5.151034355163574,
+          5.022127628326416,
+          0.7163625359535217,
+          3.007754325866699,
+          3.978142499923706,
+          5.521933078765869,
+          7.1930742263793945,
+          8.765081405639648,
+          1.5991919040679932,
+          8.930801391601562,
+          8.660456657409668
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus both positive float32 4D tensor and options.steepness",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
+            5.626614582460632,
+            5.167487045486892,
+            4.0146356193402655,
+            9.48003299650489,
+            9.989938045769978,
+            7.0654412821434125,
+            2.132681001794825,
+            8.187151346059956,
+            5.169976220175496,
+            2.1044997879382077,
+            3.523329401138895,
+            4.136340646976668,
+            1.7418719794295656,
+            5.145224066290767,
+            5.015515309165462,
+            0.045903935074711466,
+            2.9570898924917377,
+            3.959244712098706,
+            5.517926978255181,
+            7.192322388417094,
+            8.76492480390928,
+            1.3734704039113388,
+            8.930669016709397,
+            8.660283210871246
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "steepness": 1.5104469060897827
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
+          5.626749515533447,
+          5.167757034301758,
+          4.016173362731934,
+          9.480032920837402,
+          9.989937782287598,
+          7.065456390380859,
+          2.158585548400879,
+          8.187153816223145,
+          5.170245170593262,
+          2.1315081119537354,
+          3.526555061340332,
+          4.137620449066162,
+          1.7879058122634888,
+          5.145503044128418,
+          5.015854835510254,
+          0.4822517931461334,
+          2.964651584625244,
+          3.960916519165039,
+          5.518085956573486,
+          7.19233512878418,
+          8.764925956726074,
+          1.4518096446990967,
+          8.930669784545898,
+          8.660284042358398
+        ],
+        "type": "float32"
+     }
+    },
+    {
+      "name": "softplus both negative float32 4D tensor and options.steepness",
+      "inputs": {
+        "x": {
+          "shape": [1, 2, 3, 4],
+          "data": [
+            -5.584833476104802,
+            -8.188738740810354,
+            -8.981280004134987,
+            -1.7315531899284586,
+            -0.7266543578958906,
+            -0.0034800119290885334,
+            -7.378389455552106,
+            -8.907525953796949,
+            -6.0483786568116304,
+            -6.328561142365743,
+            -2.6006513567654626,
+            -5.02005264196455,
+            -2.0647716093484414,
+            -1.5499896740695434,
+            -2.221591675966657,
+            -1.1088025713211636,
+            -2.7854626064634385,
+            -2.105037489961294,
+            -5.144277741727352,
+            -5.081219916574497,
+            -7.499426297617635,
+            -2.4305558382286545,
+            -8.390520024268328,
+            -0.07117499202643174
+          ],
+          "type": "float32"
+        }
+      },
+      "options": {
+        "steepness": -1.2985155767552126
+      },
+      "expected": {
+        "name": "output",
+        "shape": [1, 2, 3, 4],
+        "data": [
+          -5.585379123687744,
+          -8.188756942749023,
+          -8.981287002563477,
+          -1.8088372945785522,
+          -0.9798305630683899,
+          -0.5355416536331177,
+          -7.378442287445068,
+          -8.907533645629883,
+          -6.048677444458008,
+          -6.328769207000732,
+          -2.626511573791504,
+          -5.021188259124756,
+          -2.1157851219177246,
+          -1.6465802192687988,
+          -2.2634570598602295,
+          -1.2725814580917358,
+          -2.805877923965454,
+          -2.1535322666168213,
+          -5.145244121551514,
+          -5.082269191741943,
+          -7.499471664428711,
+          -2.4626762866973877,
+          -8.390534400939941,
+          -0.5702091455459595
+        ],
+        "type": "float32"
+     }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/softsign.json b/testing/web-platform/tests/webnn/resources/test_data/softsign.json
new file mode 100644
index 0000000000..290f6711d0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/softsign.json
@@ -0,0 +1,467 @@
+{ // softsign: The calculation follows the expression x / (1 + |x|).
+  "tests": [
+    {
+      "name": "softsign positive float32 1D constant tensor",
+      "inputs": {
+        "x": {
+          "shape": [24],
+          "data": [
+            1.5834133593790956,
+            4.0787189411490115,
+            8.883356617490337,
+            8.070860233252166,
+            8.211773633019915,
+            2.4554003891197818,
+            0.653374178120758,
+            7.866281154604682,
+            3.1239552336192666,
+            8.013792390668268,
+            3.9409862749371283,
+            1.8131727337833081,
+            2.390676102273188,
+            1.3359680092645565,
+            9.416410839467265,
+            0.44325690899325254,
+            5.236662013213598,
+            9.424242359034539,
+            7.816190418459348,
+            5.849185795081855,
+            8.780370640491928,
+            5.1205157788312246,
+            7.1172223514005095,
+            4.59910661262345
+          ],
+          "type": "float32",
+          "constant": true
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [24],
+        "data": [
+          0.6129152178764343,
+          0.8030999898910522,
+          0.8988198041915894,
+          0.8897568583488464,
+          0.8914432525634766,
+          0.7105979323387146,
+          0.3951762318611145,
+          0.8872131109237671,
+          0.7575143575668335,
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/split.json b/testing/web-platform/tests/webnn/resources/test_data/split.json
new file mode 100644
index 0000000000..6432044ea2
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/split.json
@@ -0,0 +1,744 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/sqrt.json b/testing/web-platform/tests/webnn/resources/test_data/sqrt.json
new file mode 100644
index 0000000000..fa59308d45
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/sqrt.json
@@ -0,0 +1,421 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/sub.json b/testing/web-platform/tests/webnn/resources/test_data/sub.json
new file mode 100644
index 0000000000..207d9106b4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/sub.json
@@ -0,0 +1,882 @@
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/tan.json b/testing/web-platform/tests/webnn/resources/test_data/tan.json
new file mode 100644
index 0000000000..63c7056d90
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/tan.json
@@ -0,0 +1,401 @@
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/tanh.json b/testing/web-platform/tests/webnn/resources/test_data/tanh.json
new file mode 100644
index 0000000000..063c10623d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/tanh.json
@@ -0,0 +1,401 @@
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+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/transpose.json b/testing/web-platform/tests/webnn/resources/test_data/transpose.json
new file mode 100644
index 0000000000..f7e6a7d2dd
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/transpose.json
@@ -0,0 +1,746 @@
+{
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\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/where.json b/testing/web-platform/tests/webnn/resources/test_data/where.json
new file mode 100644
index 0000000000..7c55b3171a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/where.json
@@ -0,0 +1,1817 @@
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+        "type": "float32"
+      }
+    },
+    {
+      "name": "where float32 4D tensors only broadcast trueValues 4D to 4D",
+      "inputs": {
+        "condition": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            241,
+            0,
+            85,
+            0,
+            227,
+            51,
+            202,
+            0,
+            104,
+            227,
+            129,
+            129,
+            175,
+            134,
+            130,
+            140,
+            103,
+            46,
+            158,
+            17,
+            0,
+            41,
+            94,
+            0
+          ],
+          "type": "uint8"
+        },
+        "trueValues": {
+          "shape": [2, 2, 1, 1],
+          "data": [
+            -29.164173126220703,
+            32.200286865234375,
+            -6.146966457366943,
+            -24.822790145874023
+          ],
+          "type": "float32"
+        },
+        "falseValues": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            90.44255065917969,
+            66.7642593383789,
+            -39.82423782348633,
+            -21.683168411254883,
+            -67.3449478149414,
+            -46.46003341674805,
+            -55.907073974609375,
+            65.5730209350586,
+            35.14354705810547,
+            11.960981369018555,
+            76.65287780761719,
+            60.741416931152344,
+            -93.93305206298828,
+            -30.397233963012695,
+            34.315250396728516,
+            7.7088470458984375,
+            -82.2353286743164,
+            91.12223052978516,
+            22.937334060668945,
+            74.97810363769531,
+            18.877336502075195,
+            62.31299591064453,
+            48.867103576660156,
+            -75.41789245605469
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          -29.164173126220703,
+          66.7642593383789,
+          -29.164173126220703,
+          -21.683168411254883,
+          -29.164173126220703,
+          -29.164173126220703,
+          32.200286865234375,
+          65.5730209350586,
+          32.200286865234375,
+          32.200286865234375,
+          32.200286865234375,
+          32.200286865234375,
+          -6.146966457366943,
+          -6.146966457366943,
+          -6.146966457366943,
+          -6.146966457366943,
+          -6.146966457366943,
+          -6.146966457366943,
+          -24.822790145874023,
+          -24.822790145874023,
+          18.877336502075195,
+          -24.822790145874023,
+          -24.822790145874023,
+          -75.41789245605469
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "where float32 4D tensors only broadcast falseValues 3D to 4D",
+      "inputs": {
+        "condition": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            241,
+            0,
+            85,
+            0,
+            227,
+            51,
+            202,
+            0,
+            104,
+            227,
+            129,
+            129,
+            175,
+            134,
+            130,
+            140,
+            103,
+            46,
+            158,
+            17,
+            0,
+            41,
+            94,
+            0
+          ],
+          "type": "uint8"
+        },
+        "trueValues": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            70.27151489257812,
+            89.99047088623047,
+            -1.5351932048797607,
+            -83.77035522460938,
+            29.59670639038086,
+            -37.10446548461914,
+            -6.663239002227783,
+            -83.15333557128906,
+            -90.47624206542969,
+            -45.470706939697266,
+            50.38861083984375,
+            46.510799407958984,
+            47.503013610839844,
+            -21.95842933654785,
+            42.89878463745117,
+            -76.8262710571289,
+            67.0030746459961,
+            40.09717559814453,
+            -29.081029891967773,
+            -12.19106674194336,
+            -10.8577880859375,
+            -23.302522659301758,
+            30.851404190063477,
+            -58.82704544067383
+          ],
+          "type": "float32"
+        },
+        "falseValues": {
+          "shape": [2, 2, 1],
+          "data": [
+            -86.59848022460938,
+            -67.72773742675781,
+            -30.731334686279297,
+            -93.49223327636719
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          70.27151489257812,
+          -86.59848022460938,
+          -1.5351932048797607,
+          -67.72773742675781,
+          29.59670639038086,
+          -37.10446548461914,
+          -6.663239002227783,
+          -30.731334686279297,
+          -90.47624206542969,
+          -45.470706939697266,
+          50.38861083984375,
+          46.510799407958984,
+          47.503013610839844,
+          -21.95842933654785,
+          42.89878463745117,
+          -76.8262710571289,
+          67.0030746459961,
+          40.09717559814453,
+          -29.081029891967773,
+          -12.19106674194336,
+          -30.731334686279297,
+          -23.302522659301758,
+          30.851404190063477,
+          -93.49223327636719
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "where float32 4D tensors only broadcast falseValues 4D to 4D",
+      "inputs": {
+        "condition": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            241,
+            0,
+            85,
+            0,
+            227,
+            51,
+            202,
+            0,
+            104,
+            227,
+            129,
+            129,
+            175,
+            134,
+            130,
+            140,
+            103,
+            46,
+            158,
+            17,
+            0,
+            41,
+            94,
+            0
+          ],
+          "type": "uint8"
+        },
+        "trueValues": {
+          "shape": [2, 2, 2, 3],
+          "data": [
+            70.27151489257812,
+            89.99047088623047,
+            -1.5351932048797607,
+            -83.77035522460938,
+            29.59670639038086,
+            -37.10446548461914,
+            -6.663239002227783,
+            -83.15333557128906,
+            -90.47624206542969,
+            -45.470706939697266,
+            50.38861083984375,
+            46.510799407958984,
+            47.503013610839844,
+            -21.95842933654785,
+            42.89878463745117,
+            -76.8262710571289,
+            67.0030746459961,
+            40.09717559814453,
+            -29.081029891967773,
+            -12.19106674194336,
+            -10.8577880859375,
+            -23.302522659301758,
+            30.851404190063477,
+            -58.82704544067383
+          ],
+          "type": "float32"
+        },
+        "falseValues": {
+          "shape": [1, 2, 1, 3],
+          "data": [
+            17.777414321899414,
+            -1.9890096187591553,
+            -57.28207015991211,
+            -80.01390838623047,
+            66.87323760986328,
+            -31.328258514404297
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          70.27151489257812,
+          -1.9890096187591553,
+          -1.5351932048797607,
+          17.777414321899414,
+          29.59670639038086,
+          -37.10446548461914,
+          -6.663239002227783,
+          66.87323760986328,
+          -90.47624206542969,
+          -45.470706939697266,
+          50.38861083984375,
+          46.510799407958984,
+          47.503013610839844,
+          -21.95842933654785,
+          42.89878463745117,
+          -76.8262710571289,
+          67.0030746459961,
+          40.09717559814453,
+          -29.081029891967773,
+          -12.19106674194336,
+          -31.328258514404297,
+          -23.302522659301758,
+          30.851404190063477,
+          -31.328258514404297
+        ],
+        "type": "float32"
+      }
+    },
+    {
+      "name": "where float32 4D tensors all broadcast 4D",
+      "inputs": {
+        "condition": {
+          "shape": [2, 1, 1, 1],
+          "data": [
+            58,
+            217
+          ],
+          "type": "uint8"
+        },
+        "trueValues": {
+          "shape": [2, 3],
+          "data": [
+            38.77214050292969,
+            18.20477294921875,
+            -11.542049407958984,
+            -82.74693298339844,
+            -18.61202621459961,
+            -75.77534484863281
+          ],
+          "type": "float32"
+        },
+        "falseValues": {
+          "shape": [2, 2, 1],
+          "data":[
+            -86.59848022460938,
+            -67.72773742675781,
+            -30.731334686279297,
+            -93.49223327636719
+          ],
+          "type": "float32"
+        }
+      },
+      "expected": {
+        "name": "output",
+        "shape": [2, 2, 2, 3],
+        "data": [
+          38.77214050292969,
+          18.20477294921875,
+          -11.542049407958984,
+          -82.74693298339844,
+          -18.61202621459961,
+          -75.77534484863281,
+          38.77214050292969,
+          18.20477294921875,
+          -11.542049407958984,
+          -82.74693298339844,
+          -18.61202621459961,
+          -75.77534484863281,
+          38.77214050292969,
+          18.20477294921875,
+          -11.542049407958984,
+          -82.74693298339844,
+          -18.61202621459961,
+          -75.77534484863281,
+          38.77214050292969,
+          18.20477294921875,
+          -11.542049407958984,
+          -82.74693298339844,
+          -18.61202621459961,
+          -75.77534484863281
+        ],
+        "type": "float32"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/utils.js b/testing/web-platform/tests/webnn/resources/utils.js
new file mode 100644
index 0000000000..f91d6622d3
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/utils.js
@@ -0,0 +1,929 @@
+'use strict';
+
+const ExecutionArray = ['sync', 'async'];
+
+// https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
+const TypedArrayDict = {
+  // workaround use Uint16 for Float16
+  float16: Uint16Array,
+
+  float32: Float32Array,
+  int32: Int32Array,
+  uint32: Uint32Array,
+  int8: Int8Array,
+  uint8: Uint8Array,
+  int64: BigInt64Array,
+};
+
+const getTypedArrayData = (type, data) => {
+  let outData;
+  if (type === 'float16') {
+    // workaround to convert Float16 to Uint16
+    outData = new TypedArrayDict[type](data.length);
+    for (let i = 0; i < data.length; i++) {
+      outData[i] = toHalf(data[i]);
+    }
+  } else if (type === 'int64') {
+    outData = new TypedArrayDict[type](data.length);
+    for (let i = 0; i < data.length; i++) {
+      outData[i] = BigInt(data[i]);
+    }
+  } else {
+    outData = new TypedArrayDict[type](data);
+  }
+  return outData;
+};
+
+const sizeOfShape = (array) => {
+  return array.reduce((accumulator, currentValue) => accumulator * currentValue, 1);
+};
+
+/**
+ * Get tests resources from test data JSON file of specified operation name.
+ * @param {String} operationName - An operation name
+ * @returns {Object} Tests resources
+ */
+const loadTests = (operationName) => {
+  const loadJSON = (file) => {
+    let xmlhttp = new XMLHttpRequest();
+    xmlhttp.open("GET", file, false);
+    xmlhttp.overrideMimeType("application/json");
+    xmlhttp.send();
+    if (xmlhttp.status == 200 && xmlhttp.readyState == 4) {
+      return xmlhttp.responseText;
+    } else {
+      throw new Error(`Failed to load ${file}`);
+    }
+  };
+
+  const capitalLetterMatches = operationName.match(/[A-Z]/g);
+  if (capitalLetterMatches !== null) {
+    // for example: the test data JSON file for leakyRelu is leaky_relu.json and for reduceLogSum is reduce_log_sum.json
+    capitalLetterMatches.forEach(
+      capitalLetter => operationName = operationName.replace(capitalLetter, `_${capitalLetter.toLowerCase()}`)
+    )
+  }
+  const json = loadJSON(`/webnn/resources/test_data/${operationName}.json`);
+  const resources = JSON.parse(json.replace(/\\"|"(?:\\"|[^"])*"|(\/\/.*|\/\*[\s\S]*?\*\/)/g, (m, g) => g ? "" : m));
+  return resources.tests;
+};
+
+/**
+ * Get exptected data and data type from given resources with output name.
+ * @param {Array} resources - An array of expected resources
+ * @param {String} outputName - An output name
+ * @returns {Array.<[Number[], String]>} An array of expected data array and data type
+ */
+const getExpectedDataAndType = (resources, outputName) => {
+  let ret;
+  for (let subResources of resources) {
+    if (subResources.name === outputName) {
+      ret = [subResources.data, subResources.type];
+      break;
+    }
+  }
+  if (ret === undefined) {
+    throw new Error(`Failed to get expected data sources and type by ${outputName}`);
+  }
+  return ret;
+};
+
+/**
+ * Get ULP tolerance of conv2d/convTranspose2d operation.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getConv2dPrecisionTolerance = (resources, operationName) => {
+  // number of reduced input elements multiplied by filter and summed (a sliding dot product like pooling)
+  const inputNameArray = Object.keys(resources.inputs);
+  const inputShape = resources.inputs[inputNameArray[0]].shape;
+  const filterShape = resources.inputs[inputNameArray[1]].shape;
+  const options = resources.options;
+  let groups = 1;
+  let inputChannels = inputShape[1]; // default nchw inputLayout
+  // default oihw filterLayout for conv2d or default iohw filterLayout for convTranspose2d
+  let filterWidth = filterShape[3];
+  let filterHeight = filterShape[2];
+  if (options) {
+    if (options.groups) {
+      groups = options.groups;
+    }
+    if (options.inputLayout) {
+      if (!['nchw', 'nhwc'].includes(options.inputLayout)) {
+        throw new Error(`Unsupported inputLayout ${options.inputLayout}`);
+      }
+      inputChannels = options.inputLayout === 'nchw' ? inputChannels : inputShape[3];
+    }
+    if (options.filterLayout) {
+      let filterLayouts = ['oihw', 'hwio', 'ohwi', 'ihwo']; // default for conv2d
+      if (operationName === 'convTranspose2d') {
+        filterLayouts = ['iohw', 'hwoi', 'ohwi'];
+      }
+      if (!filterLayouts.includes(options.filterLayout)) {
+        throw new Error(`Unsupported filterLayout ${options.filterLayout}`);
+      }
+      switch (options.filterLayout) {
+        case 'oihw':
+        case 'iohw':
+          // Just use the existing filterWidth and filterHeight above.
+          break;
+        case 'hwio':
+        case 'hwoi':
+          filterWidth = filterShape[1];
+          filterHeight = filterShape[0];
+          break;
+        case 'ohwi':
+        case 'ihwo':
+          filterWidth = filterShape[2];
+          filterHeight = filterShape[1];
+          break;
+        default:
+          break;
+      }
+    }
+  }
+  const tolerance = filterWidth * filterHeight * (inputChannels / groups) * 2;
+  return tolerance;
+};
+
+/**
+ * Get ULP tolerance of gemm operation.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getGemmPrecisionTolerance = (resources, operationName) => {
+  // GEMM : alpha * (A x B) + beta * C
+  // An upper bound for the worst serial ordering is bounded by
+  // the number of lossy operations, where matrix multiplication
+  // is a dot product (mul and add times the number of elements)
+  // plus bias operations.
+  const shapeA = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+  const options = {...resources.options};
+  const width = options.aTranspose ? shapeA[0] : shapeA[1];
+  let tolerance = width * 2;
+  // default options.alpha is 1.0
+  if (options.alpha !== undefined && options.alpha !== 1.0) {
+    tolerance++;
+  }
+  if (options.c && options.beta !== 0.0) {
+    // default options.beta is 1.0
+    if (options.beta !== undefined && options.beta !== 1.0) {
+      tolerance++;
+    }
+    tolerance++;
+  }
+  return tolerance;
+};
+
+/**
+ * Get ULP tolerance of matmul operation.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getMatmulPrecisionTolerance = (resources, operationName) => {
+  // Matmul : Compute the matrix product of two input tensors.
+  // If a is 1-D, WebNN converts it to a 2-D tensor by prepending a 1 to its dimensions, [n] -> [1, n].
+  // So we can just always check the last dimension here.
+  const shapeA = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+  const tolerance = shapeA[shapeA.length - 1] * 2;
+  return tolerance;
+};
+
+/**
+ * Get ULP tolerance of averagePool2d operation.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getAveragePool2dPrecisionTolerance = (resources, operationName) => {
+  const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+  let height;
+  let width;
+  const options = {...resources.options};
+  if (options.windowDimensions) {
+    height = options.windowDimensions[0];
+    width = options.windowDimensions[1];
+  } else {
+    // If not present, the window dimensions are assumed to be the height and width dimensions of the input shape
+    if (options.layout && options.layout === 'nhwc') {
+      height = inputShape[1];
+      width = inputShape[2];
+    } else {
+      // nhwc layout of input
+      height = inputShape[2];
+      width = inputShape[3];
+    }
+  }
+
+  const tolerance = height * width + 2;
+  return tolerance;
+};
+
+/**
+ * Get ULP tolerance of softmax operation.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getSoftmaxPrecisionTolerance = (resources, operationName) => {
+  // Compute the softmax values of the 2-D input tensor along axis 1.
+  const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+  const tolerance = inputShape[1] * 3 + 3;
+  return tolerance;
+};
+
+/**
+ * Get ULP tolerance of reduction operations.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getReductionPrecisionTolerance = (resources, operationName) => {
+  const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+  const rank = inputShape.length;
+  const options = {...resources.options};
+  let sizes;
+  if (options && options.axes) {
+    sizes = options.axes.map(
+                (axis) => axis < 0 ? inputShape[axis + rank] : inputShape[axis]
+    );
+  } else {
+    sizes = inputShape;
+  }
+  const reducedElementCount = sizes.reduce(
+                                  (accumulator, currentValue) => accumulator * currentValue
+  );
+  let tolerance;
+  switch (operationName) {
+    case 'reduceL1':
+    case 'reduceProduct':
+    case 'reduceSum':
+      tolerance = reducedElementCount;
+      break;
+    case 'reduceL2':
+      tolerance = reducedElementCount * 2 + 1;
+      break;
+    case 'reduceMean':
+      tolerance = reducedElementCount + 2;
+      break;
+    case 'reduceLogSum':
+      tolerance = reducedElementCount + 18;
+      break;
+    case 'reduceLogSumExp':
+      tolerance = reducedElementCount * 2 + 18;
+      break;
+    case 'reduceSumSquare':
+      tolerance = reducedElementCount * 2;
+      break;
+    default:
+      break;
+  }
+  return tolerance;
+};
+
+// Refer to precision metrics on https://github.com/webmachinelearning/webnn/issues/265#issuecomment-1256242643
+const PrecisionMetrics = {
+  argMax: {ULP: {int64: 0}},
+  argMin: {ULP: {int64: 0}},
+  batchNormalization: {ULP: {float32: 6, float16: 6}},
+  cast: {ULP: {float32: 1, float16: 1, int32: 0, uint32: 0, int64: 0, int8: 0, uint8: 0}},
+  clamp: {ULP: {float32: 0, float16: 0}},
+  concat: {ULP: {float32: 0, float16: 0}},
+  conv2d: {ULP: {float32: getConv2dPrecisionTolerance, float16: getConv2dPrecisionTolerance}},
+  convTranspose2d: {ULP: {float32: getConv2dPrecisionTolerance, float16: getConv2dPrecisionTolerance}},
+  // Begin Element-wise binary operations
+  add: {ULP: {float32: 1, float16: 1}},
+  sub: {ULP: {float32: 1, float16: 1}},
+  mul: {ULP: {float32: 1, float16: 1}},
+  div: {ULP: {float32: 2, float16: 2}},
+  max: {ULP: {float32: 0, float16: 0}},
+  min: {ULP: {float32: 0, float16: 0}},
+  pow: {ULP: {float32: 32, float16: 2}},
+  // End Element-wise binary operations
+  // Begin Element-wise logical operations
+  equal: {ULP: {uint8: 0}},
+  greater: {ULP: {uint8: 0}},
+  greaterOrEqual: {ULP: {uint8: 0}},
+  lesser: {ULP: {uint8: 0}},
+  lesserOrEqual: {ULP: {uint8: 0}},
+  logicalNot: {ULP: {uint8: 0}},
+  // End Element-wise logical operations
+  // Begin Element-wise unary operations
+  abs: {ULP: {float32: 0, float16: 0}},
+  ceil: {ULP: {float32: 0, float16: 0}},
+  cos: {ATOL: {float32: 1/1024, float16: 1/512}},
+  erf: {ATOL: {float32: 1/1024, float16: 1/512}},
+  exp: {ULP: {float32: 32, float16: 1}},
+  floor: {ULP: {float32: 0, float16: 0}},
+  identity: {ULP: {float32: 0, float16: 0}},
+  log: {ATOL: {float32: 1/1024, float16:  1/1024}},
+  neg: {ULP: {float32: 0, float16: 0}},
+  reciprocal: {ULP: {float32: 2, float16: 2}},
+  sin: {ATOL: {float32: 1/1024, float16: 1/512}},
+  sqrt: {ULP: {float32: 1, float16: 1}},
+  tan: {ATOL: {float32: 1/1024, float16: 1/512}},
+  // End Element-wise unary operations
+  elu: {ULP: {float32: 18, float16: 18}},
+  expand: {ULP: {float32: 0, float16: 0}},
+  gather: {ULP: {float32: 0, float16: 0}},
+  gemm: {ULP: {float32: getGemmPrecisionTolerance, float16: getGemmPrecisionTolerance}},
+  instanceNormalization: {ULP: {float32: 840, float16: 8400}},
+  hardSigmoid: {ULP: {float32: 2, float16: 2}},
+  hardSwish: {ULP: {float32: 4, float16: 4}},
+  layerNormalization: {ATOL: {float32: 1/1024, float16: 1/512}},
+  leakyRelu: {ULP: {float32: 1, float16: 1}},
+  linear: {ULP: {float32: 2, float16: 2}},
+  matmul: {ULP: {float32: getMatmulPrecisionTolerance, float16: getMatmulPrecisionTolerance}},
+  pad: {ULP: {float32: 0, float16: 0}},
+  // Begin Pooling operations
+  averagePool2d: {ULP: {float32: getAveragePool2dPrecisionTolerance, float16: getAveragePool2dPrecisionTolerance}},
+  maxPool2d: {ULP: {float32: 0, float16: 0}},
+  // End Pooling operations
+  prelu: {ULP: {float32: 1, float16: 1}},
+  // Begin Reduction operations
+  reduceL1: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceL2: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceLogSum: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceLogSumExp: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceMax: {ULP: {float32: 0, float16: 0}},
+  reduceMean: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceMin: {ULP: {float32: 0, float16: 0}},
+  reduceProduct: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceSum: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  reduceSumSquare: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
+  // End Reduction operations
+  relu: {ULP: {float32: 0, float16: 0}},
+  reshape: {ULP: {float32: 0, float16: 0}},
+  sigmoid: {ULP: {float32: 32+2, float16: 3}}, // float32 (leaving a few ULP for roundoff)
+  slice: {ULP: {float32: 0, float16: 0}},
+  softmax: {ULP: {float32: getSoftmaxPrecisionTolerance, float16: getSoftmaxPrecisionTolerance}},
+  softplus: {ULP: {float32: 18, float16: 18}},
+  softsign: {ULP: {float32: 3, float16: 3}},
+  split: {ULP: {float32: 0, float16: 0}},
+  tanh: {ATOL: {float32: 1/1024, float16: 1/512}},
+  transpose: {ULP: {float32: 0, float16: 0}},
+  where: {ULP: {float32: 0, float16: 0}},
+};
+
+/**
+ * Get precison tolerance value.
+ * @param {String} operationName - An operation name
+ * @param {String} metricType - Value: 'ULP', 'ATOL'
+ * @param {Object} resources - Resources used for building a graph
+ * @returns {Number} A tolerance number
+ */
+const getPrecisonTolerance = (operationName, metricType, resources) => {
+  // the outputs by split or gru is a sequence
+  const precisionType = Array.isArray(resources.expected) ? resources.expected[0].type : resources.expected.type;
+  let tolerance = PrecisionMetrics[operationName][metricType][precisionType];
+  // If the tolerance is dynamic, then evaluate the function to get the value.
+  if (tolerance instanceof Function) {
+    tolerance = tolerance(resources, operationName);
+  }
+  return tolerance;
+};
+
+/**
+ * Get bitwise of the given value.
+ * @param {Number} value
+ * @param {String} dataType - A data type string, like "float32", "float16",
+ *     more types, please see:
+ *     https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
+ * @return {Number} A 64-bit signed integer.
+ */
+const getBitwise = (value, dataType) => {
+  const buffer = new ArrayBuffer(8);
+  const int64Array = new BigInt64Array(buffer);
+  int64Array[0] = value < 0 ? ~BigInt(0) : BigInt(0);
+  let typedArray;
+  if (dataType === "float32") {
+    typedArray = new Float32Array(buffer);
+  } else {
+    throw new AssertionError(`Data type ${dataType} is not supported`);
+  }
+  typedArray[0] = value;
+  return int64Array[0];
+};
+
+/**
+ * Assert that each array property in ``actual`` is a number being close enough to the corresponding
+ * property in ``expected`` by the acceptable ULP distance ``nulp`` with given ``dataType`` data type.
+ *
+ * @param {Array} actual - Array of test values.
+ * @param {Array} expected - Array of values expected to be close to the values in ``actual``.
+ * @param {Number} nulp - A BigInt value indicates acceptable ULP distance.
+ * @param {String} dataType - A data type string, value: "float32",
+ *     more types, please see:
+ *     https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
+ * @param {String} description - Description of the condition being tested.
+ */
+const assert_array_approx_equals_ulp = (actual, expected, nulp, dataType, description) => {
+  /*
+    * Test if two primitive arrays are equal within acceptable ULP distance
+    */
+  assert_true(actual.length === expected.length,
+              `assert_array_approx_equals_ulp: ${description} lengths differ, expected ${expected.length} but got ${actual.length}`);
+  let actualBitwise, expectedBitwise, distance;
+  for (let i = 0; i < actual.length; i++) {
+    if (actual[i] === expected[i]) {
+      continue;
+    } else {
+      // measure the ULP distance
+      if (dataType === 'float32') {
+        actualBitwise = getBitwise(actual[i], dataType);
+        expectedBitwise = getBitwise(expected[i], dataType);
+      } else if (dataType === 'float16') {
+        actualBitwise = actual[i];
+        // convert expected data of Float16 to Uint16
+        expectedBitwise = toHalf(expected[i]);
+      } else if (dataType === 'int64') {
+        actualBitwise = actual[i];
+        expectedBitwise = BigInt(expected[i]);
+      }
+      distance = actualBitwise - expectedBitwise;
+      distance = distance >= 0 ? distance : -distance;
+      assert_true(distance <= nulp,
+                  `assert_array_approx_equals_ulp: ${description} actual ${actual[i]} should be close enough to expected ${expected[i]} by the acceptable ${nulp} ULP distance, but they have ${distance} ULP distance`);
+    }
+  }
+};
+
+/**
+ * Assert actual results with expected results.
+ * @param {String} operationName - An operation name
+ * @param {(Number[]|Number)} actual
+ * @param {(Number[]|Number)} expected
+ * @param {Number} tolerance
+ * @param {String} operandType  - An operand type string, value: "float32",
+ *     more types, please see:
+ *     https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
+ * @param {String} metricType - Value: 'ULP', 'ATOL'
+ */
+const doAssert = (operationName, actual, expected, tolerance, operandType, metricType) => {
+  const description = `test ${operationName} ${operandType}`;
+  if (typeof expected === 'number') {
+    // for checking a scalar output by matmul 1D x 1D
+    expected = [expected];
+    actual = [actual];
+  }
+  if (metricType === 'ULP') {
+    assert_array_approx_equals_ulp(actual, expected, tolerance, operandType, description);
+  } else if (metricType === 'ATOL') {
+    assert_array_approx_equals(actual, expected, tolerance, description);
+  }
+};
+
+/**
+ * Check computed results with expected data.
+ * @param {String} operationName - An operation name
+ * @param {Object.<String, MLOperand>} namedOutputOperands
+ * @param {Object.<MLNamedArrayBufferViews>} outputs - The resources of required outputs
+ * @param {Object} resources - Resources used for building a graph
+ */
+const checkResults = (operationName, namedOutputOperands, outputs, resources) => {
+  const metricType = Object.keys(PrecisionMetrics[operationName])[0];
+  const expected = resources.expected;
+  let tolerance;
+  let operandType;
+  let outputData;
+  let expectedData;
+  if (Array.isArray(expected)) {
+    // the outputs of split() or gru() is a sequence
+    for (let operandName in namedOutputOperands) {
+      outputData = outputs[operandName];
+      // for some operations which may have multi outputs of different types
+      [expectedData, operandType] = getExpectedDataAndType(expected, operandName);
+      tolerance = getPrecisonTolerance(operationName, metricType, resources);
+      doAssert(operationName, outputData, expectedData, tolerance, operandType, metricType)
+    }
+  } else {
+    outputData = outputs[expected.name];
+    expectedData = expected.data;
+    operandType = expected.type;
+    tolerance = getPrecisonTolerance(operationName, metricType, resources);
+    doAssert(operationName, outputData, expectedData, tolerance, operandType, metricType)
+  }
+};
+
+/**
+ * Create a constant operand
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for constant operand
+ * @returns {MLOperand} A constant operand
+ */
+const createConstantOperand = (builder, resources) => {
+  const bufferView = new TypedArrayDict[resources.type](resources.data);
+  return builder.constant({dataType: resources.type, type: resources.type, dimensions: resources.shape}, bufferView);
+};
+
+/**
+ * Create single input operands for a graph.
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} [inputOperandName] - An inputOperand name
+ * @returns {MLOperand} An input operand
+ */
+const createSingleInputOperand = (builder, resources, inputOperandName) => {
+  inputOperandName = inputOperandName ? inputOperandName : Object.keys(resources.inputs)[0];
+  const inputResources = resources.inputs[inputOperandName];
+  let operand;
+  if (resources.inputs[inputOperandName].hasOwnProperty('constant') && resources.inputs[inputOperandName]['constant']) {
+    operand = createConstantOperand(builder, resources.inputs[inputOperandName]);
+  } else {
+    operand = builder.input(inputOperandName, {dataType: inputResources.type, type: inputResources.type, dimensions: inputResources.shape});
+  }
+  return operand;
+};
+
+/**
+ * Create multi input operands for a graph.
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @returns {MLOperand[]} Input operands array
+ */
+const createMultiInputOperands = (builder, resources) => {
+  let inputOperands = [];
+  const inputOperandNameArray = Object.keys(resources.inputs);
+  inputOperandNameArray.forEach(inputOperandName => {
+    const operand = createSingleInputOperand(builder, resources, inputOperandName);
+    inputOperands.push(operand);
+  });
+  return inputOperands;
+};
+
+/**
+ * Build an operation which has a single input.
+ * @param {String} operationName - An operation name
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @returns {MLNamedOperands}
+ */
+const buildOperationWithSingleInput = (operationName, builder, resources) => {
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  const outputOperand = resources.options ?
+      builder[operationName](inputOperand, resources.options) : builder[operationName](inputOperand);
+  namedOutputOperand[resources.expected.name] = outputOperand;
+  return namedOutputOperand;
+};
+
+/**
+ * Build an operation which has two inputs.
+ * @param {String} operationName - An operation name
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @returns {MLNamedOperands}
+ */
+const buildOperationWithTwoInputs = (operationName, builder, resources) => {
+  // For example: MLOperand matmul(MLOperand a, MLOperand b);
+  const namedOutputOperand = {};
+  const [inputOperandA, inputOperandB] = createMultiInputOperands(builder, resources);
+  const outputOperand = resources.options ?
+      builder[operationName](inputOperandA, inputOperandB, resources.options) : builder[operationName](inputOperandA, inputOperandB);
+  namedOutputOperand[resources.expected.name] = outputOperand;
+  return namedOutputOperand;
+};
+
+const buildBatchNorm = (operationName, builder, resources) => {
+  // MLOperand batchNormalization(MLOperand input, MLOperand mean, MLOperand variance,
+  //                              optional MLBatchNormalizationOptions options = {});
+  const namedOutputOperand = {};
+  const [inputOperand, meanOperand, varianceOperand] = createMultiInputOperands(builder, resources);
+  const batchNormOptions = {...resources.options};
+  if (batchNormOptions.scale) {
+    batchNormOptions.scale = createConstantOperand(builder, batchNormOptions.scale);
+  }
+  if (batchNormOptions.bias) {
+    batchNormOptions.bias = createConstantOperand(builder, batchNormOptions.bias);
+  }
+  if (batchNormOptions.activation) {
+    batchNormOptions.activation = builder[batchNormOptions.activation]();
+  }
+  // invoke builder.batchNormalization()
+  namedOutputOperand[resources.expected.name] =
+      builder[operationName](inputOperand, meanOperand, varianceOperand, batchNormOptions);
+  return namedOutputOperand;
+};
+
+const buildCast = (operationName, builder, resources) => {
+  // MLOperand cast(MLOperand input, MLOperandDataType type);
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  // invoke builder.cast()
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, resources.type);
+  return namedOutputOperand;
+};
+
+const buildConcat = (operationName, builder, resources) => {
+  // MLOperand concat(sequence<MLOperand> inputs, unsigned long axis);
+  const namedOutputOperand = {};
+  const inputOperands = [];
+  let operand;
+  for (let input of resources.inputs) {
+    if (input.hasOwnProperty('constant') && input['constant']) {
+      operand = createConstantOperand(builder, input);
+    } else {
+      operand = builder.input(input.name, {dataType: input.type, type: input.type, dimensions: input.shape});
+    }
+    inputOperands.push(operand);
+  }
+  // invoke builder.concat()
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperands, resources.axis);
+  return namedOutputOperand;
+};
+
+const buildConvTranspose2d = (operationName, builder, resources) => {
+  // MLOperand convTranspose2d(MLOperand input, MLOperand filter, optional MLConvTranspose2dOptions options = {});
+  const namedOutputOperand = {};
+  const [inputOperand, filterOperand] = createMultiInputOperands(builder, resources);
+  let convTranspose2dOptions = {...resources.options};
+  if (convTranspose2dOptions.bias) {
+    convTranspose2dOptions.bias = createConstantOperand(builder, convTranspose2dOptions.bias);
+  }
+  if (convTranspose2dOptions.activation) {
+    convTranspose2dOptions.activation = builder[convTranspose2dOptions.activation]();
+  }
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, filterOperand, convTranspose2dOptions);
+  return namedOutputOperand;
+};
+
+const buildConv2d = (operationName, builder, resources) => {
+  // MLOperand conv2d(MLOperand input, MLOperand filter, optional MLConv2dOptions options = {});
+  const namedOutputOperand = {};
+  const [inputOperand, filterOperand] = createMultiInputOperands(builder, resources);
+  let conv2dOptions = {...resources.options};
+  if (conv2dOptions.bias) {
+    conv2dOptions.bias = createConstantOperand(builder, conv2dOptions.bias);
+  }
+  if (conv2dOptions.activation) {
+    conv2dOptions.activation = builder[conv2dOptions.activation]();
+  }
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, filterOperand, conv2dOptions);
+  return namedOutputOperand;
+};
+
+const buildGemm = (operationName, builder, resources) => {
+  // MLOperand gemm(MLOperand a, MLOperand b, optional MLGemmOptions options = {});
+  const namedOutputOperand = {};
+  const [inputOperandA, inputOperandB] = createMultiInputOperands(builder, resources);
+  let gemmOptions = {...resources.options};
+  if (gemmOptions.c) {
+    if (gemmOptions.c.shape) {
+      gemmOptions.c = createConstantOperand(builder, gemmOptions.c);
+    } else {
+      // MLOperand c;
+      // Create a single-value operand when c is a scalar
+      gemmOptions.c = builder.constant({dataType: 'float32', type: 'float32', dimensions: [1]}, new Float32Array([gemmOptions.c]));
+    }
+  }
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperandA, inputOperandB, gemmOptions);
+  return namedOutputOperand;
+};
+
+const buildLayerNorm = (operationName, builder, resources) => {
+  // MLOperand layerNormalization(MLOperand input, optional MLLayerNormalizationOptions options = {});
+  // MLOperand instanceNormalization(MLOperand input, optional MLInstanceNormalizationOptions options = {});
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  const layerNormOptions = {...resources.options};
+  if (layerNormOptions.scale) {
+    layerNormOptions.scale = createConstantOperand(builder, layerNormOptions.scale);
+  }
+  if (layerNormOptions.bias) {
+    layerNormOptions.bias = createConstantOperand(builder, layerNormOptions.bias);
+  }
+  // invoke builder.layerNormalization() or builder.instanceNormalization()
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, layerNormOptions);
+  return namedOutputOperand;
+};
+
+const buildPad = (operationName, builder, resources) => {
+  // MLOperand pad(MLOperand input, sequence<unsigned long> beginningPadding, sequence<unsigned long> endingPadding, optional MLPadOptions options = {});
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  // invoke builder.pad()
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, resources.beginningPadding, resources.endingPadding, resources.options);
+  return namedOutputOperand;
+};
+
+const buildReshape = (operationName, builder, resources) => {
+  // MLOperand reshape(MLOperand input, sequence<unsigned long> newShape);
+  // MLOperand expand(MLOperand input, sequence<unsigned long> newShape);
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  // invoke builder.reshape() or builder.expand()
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, resources.newShape);
+  return namedOutputOperand;
+};
+
+const buildSlice = (operationName, builder, resources) => {
+  // MLOperand slice(MLOperand input, sequence<unsigned long> starts, sequence<unsigned long> sizes);
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  // invoke builder.slice()
+  namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, resources.starts, resources.sizes);
+  return namedOutputOperand;
+};
+
+const buildSplit = (operationName, builder, resources) => {
+  // sequence<MLOperand> split(MLOperand input,
+  //                           (unsigned long or sequence<unsigned long>) splits,
+  //                           optional MLSplitOptions options = {});
+  const namedOutputOperand = {};
+  const inputOperand = createSingleInputOperand(builder, resources);
+  // invoke builder.split()
+  const outputOperands = builder[operationName](inputOperand, resources.splits, resources.options);
+  resources.expected.forEach((resourceDict, index) => {
+    namedOutputOperand[resourceDict.name] = outputOperands[index];
+  });
+  return namedOutputOperand;
+};
+
+const buildWhere = (operationName, builder, resources) => {
+  // MLOperand where(MLOperand condition, MLOperand trueValues, MLOperand falseValues);
+  const namedOutputOperand = {};
+  const [conditionOperand, trueValuesOperand, falseValuesOperand] = createMultiInputOperands(builder, resources);
+  // invoke builder.where()
+  namedOutputOperand[resources.expected.name] = builder[operationName](conditionOperand, trueValuesOperand, falseValuesOperand);
+  return namedOutputOperand;
+};
+
+/**
+ * Build a graph.
+ * @param {String} operationName - An operation name
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {Function} buildFunc - A build function for an operation
+ * @returns [namedOperands, inputs, outputs]
+ */
+const buildGraph = (operationName, builder, resources, buildFunc) => {
+  const namedOperands = buildFunc(operationName, builder, resources);
+  let inputs = {};
+  if (Array.isArray(resources.inputs)) {
+    // the inputs of concat() is a sequence
+    for (let subInput of resources.inputs) {
+      if (!subInput.hasOwnProperty('constant') || !subInput.constant) {
+        inputs[subInput.name] = getTypedArrayData(subInput.type, subInput.data);
+      }
+    }
+  } else {
+    for (let inputName in resources.inputs) {
+      const subTestByName = resources.inputs[inputName];
+      if (!subTestByName.hasOwnProperty('constant') || !subTestByName.constant) {
+        inputs[inputName] = getTypedArrayData(subTestByName.type, subTestByName.data);
+      }
+    }
+  }
+  let outputs = {};
+  if (Array.isArray(resources.expected)) {
+    // the outputs of split() or gru() is a sequence
+    for (let i = 0; i < resources.expected.length; i++) {
+      const subExpected = resources.expected[i];
+      outputs[subExpected.name] = new TypedArrayDict[subExpected.type](sizeOfShape(subExpected.shape));
+    }
+  } else {
+    // matmul 1D with 1D produces a scalar which doesn't have its shape
+    const shape = resources.expected.shape ? resources.expected.shape : [1];
+    outputs[resources.expected.name] = new TypedArrayDict[resources.expected.type](sizeOfShape(shape));
+  }
+  return [namedOperands, inputs, outputs];
+};
+
+/**
+ * Build a graph, synchronously compile graph and execute, then check computed results.
+ * @param {String} operationName - An operation name
+ * @param {MLContext} context - A ML context
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {Function} buildFunc - A build function for an operation
+ */
+const runSync = (operationName, context, builder, resources, buildFunc) => {
+  // build a graph
+  const [namedOutputOperands, inputs, outputs] = buildGraph(operationName, builder, resources, buildFunc);
+  // synchronously compile the graph up to the output operand
+  const graph = builder.buildSync(namedOutputOperands);
+  // synchronously execute the compiled graph.
+  context.computeSync(graph, inputs, outputs);
+  checkResults(operationName, namedOutputOperands, outputs, resources);
+};
+
+/**
+ * Build a graph, asynchronously compile graph and execute, then check computed results.
+ * @param {String} operationName - An operation name
+ * @param {MLContext} context - A ML context
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {Function} buildFunc - A build function for an operation
+ */
+const run = async (operationName, context, builder, resources, buildFunc) => {
+  // build a graph
+  const [namedOutputOperands, inputs, outputs] = buildGraph(operationName, builder, resources, buildFunc);
+  // asynchronously compile the graph up to the output operand
+  const graph = await builder.build(namedOutputOperands);
+  // asynchronously execute the compiled graph
+  const result = await context.compute(graph, inputs, outputs);
+  checkResults(operationName, namedOutputOperands, result.outputs, resources);
+};
+
+/**
+ * Run WebNN operation tests.
+ * @param {(String[]|String)} operationName - An operation name array or an operation name
+ * @param {Function} buildFunc - A build function for an operation
+ * @param {String} deviceType - The execution device type for this test
+ */
+const testWebNNOperation = (operationName, buildFunc, deviceType = 'cpu') => {
+  let operationNameArray;
+  if (typeof operationName === 'string') {
+    operationNameArray = [operationName];
+  } else if (Array.isArray(operationName)) {
+    operationNameArray = operationName;
+  }
+
+  ExecutionArray.forEach(executionType => {
+    const isSync = executionType === 'sync';
+    if (self.GLOBAL.isWindow() && isSync) {
+      return;
+    }
+    let context;
+    let builder;
+    if (isSync) {
+      // test sync
+      operationNameArray.forEach((subOperationName) => {
+        const tests = loadTests(subOperationName);
+        setup(() => {
+          context = navigator.ml.createContextSync({deviceType});
+          builder = new MLGraphBuilder(context);
+        });
+        for (const subTest of tests) {
+          test(() => {
+            runSync(subOperationName, context, builder, subTest, buildFunc);
+          }, `${subTest.name} / ${executionType}`);
+        }
+      });
+    } else {
+      // test async
+      operationNameArray.forEach((subOperationName) => {
+        const tests = loadTests(subOperationName);
+        promise_setup(async () => {
+          context = await navigator.ml.createContext({deviceType});
+          builder = new MLGraphBuilder(context);
+        });
+        for (const subTest of tests) {
+          promise_test(async () => {
+            await run(subOperationName, context, builder, subTest, buildFunc);
+          }, `${subTest.name} / ${executionType}`);
+        }
+      });
+    }
+  });
+};
+
+// ref: http://stackoverflow.com/questions/32633585/how-do-you-convert-to-half-floats-in-javascript
+const toHalf = (value) => {
+  let floatView = new Float32Array(1);
+  let int32View = new Int32Array(floatView.buffer);
+
+  /* This method is faster than the OpenEXR implementation (very often
+   * used, eg. in Ogre), with the additional benefit of rounding, inspired
+   * by James Tursa's half-precision code. */
+
+  floatView[0] = value;
+  let x = int32View[0];
+
+  let bits = (x >> 16) & 0x8000; /* Get the sign */
+  let m = (x >> 12) & 0x07ff; /* Keep one extra bit for rounding */
+  let e = (x >> 23) & 0xff; /* Using int is faster here */
+
+  /* If zero, or denormal, or exponent underflows too much for a denormal
+    * half, return signed zero. */
+  if (e < 103) {
+    return bits;
+  }
+
+  /* If NaN, return NaN. If Inf or exponent overflow, return Inf. */
+  if (e > 142) {
+    bits |= 0x7c00;
+    /* If exponent was 0xff and one mantissa bit was set, it means NaN,
+      * not Inf, so make sure we set one mantissa bit too. */
+    bits |= ((e == 255) ? 0 : 1) && (x & 0x007fffff);
+    return bits;
+  }
+
+  /* If exponent underflows but not too much, return a denormal */
+  if (e < 113) {
+    m |= 0x0800;
+    /* Extra rounding may overflow and set mantissa to 0 and exponent
+      * to 1, which is OK. */
+    bits |= (m >> (114 - e)) + ((m >> (113 - e)) & 1);
+    return bits;
+  }
+
+  bits |= ((e - 112) << 10) | (m >> 1);
+  /* Extra rounding. An overflow will set mantissa to 0 and increment
+    * the exponent, which is OK. */
+  bits += m & 1;
+  return bits;
+};
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/sigmoid.https.any.js b/testing/web-platform/tests/webnn/sigmoid.https.any.js
new file mode 100644
index 0000000000..e904d8dfa7
--- /dev/null
+++ b/testing/web-platform/tests/webnn/sigmoid.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API sigmoid operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-sigmoid
+
+testWebNNOperation('sigmoid', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/slice.https.any.js b/testing/web-platform/tests/webnn/slice.https.any.js
new file mode 100644
index 0000000000..cb7acefc05
--- /dev/null
+++ b/testing/web-platform/tests/webnn/slice.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API slice operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-slice
+
+testWebNNOperation('slice', buildSlice);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/softmax.https.any.js b/testing/web-platform/tests/webnn/softmax.https.any.js
new file mode 100644
index 0000000000..8e5342bd75
--- /dev/null
+++ b/testing/web-platform/tests/webnn/softmax.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softmax operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softmax
+
+testWebNNOperation('softmax', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/softplus.https.any.js b/testing/web-platform/tests/webnn/softplus.https.any.js
new file mode 100644
index 0000000000..7096f64a04
--- /dev/null
+++ b/testing/web-platform/tests/webnn/softplus.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softplus operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softplus
+
+testWebNNOperation('softplus', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/softsign.https.any.js b/testing/web-platform/tests/webnn/softsign.https.any.js
new file mode 100644
index 0000000000..61a7d5365f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/softsign.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softsign operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softsign
+
+testWebNNOperation('softsign', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/split.https.any.js b/testing/web-platform/tests/webnn/split.https.any.js
new file mode 100644
index 0000000000..b6fc5b4d98
--- /dev/null
+++ b/testing/web-platform/tests/webnn/split.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API split operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-split
+
+testWebNNOperation('split', buildSplit);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/tanh.https.any.js b/testing/web-platform/tests/webnn/tanh.https.any.js
new file mode 100644
index 0000000000..d0d45e754b
--- /dev/null
+++ b/testing/web-platform/tests/webnn/tanh.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API tanh operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-tanh
+
+testWebNNOperation('tanh', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/transpose.https.any.js b/testing/web-platform/tests/webnn/transpose.https.any.js
new file mode 100644
index 0000000000..63a123342a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/transpose.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API transpose operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-transpose
+
+testWebNNOperation('transpose', buildOperationWithSingleInput);
\ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/where.https.any.js b/testing/web-platform/tests/webnn/where.https.any.js
new file mode 100644
index 0000000000..306128a814
--- /dev/null
+++ b/testing/web-platform/tests/webnn/where.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API where operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-where
+
+testWebNNOperation('where', buildWhere);
\ No newline at end of file