[WebNN EP] Support more Normalization ops for TFLite backend (#21151)

Following Normalization ops have been supported in Chromium for TFLite
backend:
- batchNormalization:
https://chromium-review.googlesource.com/c/chromium/src/+/5532745
- layerNormalization:
https://chromium-review.googlesource.com/c/chromium/src/+/5573326
- instanceNormalization:
https://chromium-review.googlesource.com/c/chromium/src/+/5532750

Author: Honry

js/web/docs/webnn-operators.md

@@ -16,7 +16,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | ArgMax | ai.onnx(7-10, 11, 12, 13+) | argMax | ✓ | ✓ | WebNN CPU backend only supports 'select_last_index' value is 0 |
 | ArgMin | ai.onnx(7-10, 11, 12, 13+) | argMin | ✓ | ✓ | WebNN CPU backend only supports 'select_last_index' value is 0 |
 | AveragePool | ai.onnx(7-9, 10, 11, 12-18, 19+) | averagePool2d | ✓ | ✓ | Only supports 4-D input, 2-D 'kernel_shape', 'count_include_pad' value is 0 |
-| BatchNormalization | ai.onnx(7-8, 9-13, 14, 15+) | batchNormalization | ✗ | ✓ | Only supports 'training_mode' value is 0, one output |
+| BatchNormalization | ai.onnx(7-8, 9-13, 14, 15+) | batchNormalization | ✓ | ✓ | Only supports 'training_mode' value is 0, one output |
 | Cast | ai.onnx(7-8, 9-12, 13-18, 19-20, 21+) | cast | ✓ | ✓ | WebNN CPU backend doesn't support casting to uint64 data type |
 | Ceil | ai.onnx(7-12, 13+) | ceil | ✓ | ✓ | |
 | Clip | ai.onnx(7-10, 11, 12, 13+) | clamp | ✓ | ✓ | WebNN CPU backend only supports 3 specific ranges: [0.0, infinity], [-1.0, 1.0], [0.0, 6.0] (Chromium issue: https://issues.chromium.org/issues/326156496) |
@@ -43,8 +43,8 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | HardSigmoid | ai.onnx(7+) | hardSigmoid | ✓ | ✓ | |
 | HardSwish | ai.onnx(14+) | hardSwish | ✓ | ✓ | |
 | Identity | ai.onnx(7-13, 14-15, 16-18, 19-20, 21+) | identity | ✓ | ✓ | |
-| InstanceNormalization | ai.onnx(7+) | instanceNormalization | ✗ | ✓ | |
-| LayerNormalization | ai.onnx(7-16, 17+) | layerNormalization | ✗ | ✓ | |
+| InstanceNormalization | ai.onnx(7+) | instanceNormalization | ✓ | ✓ | |
+| LayerNormalization | ai.onnx(7-16, 17+) | layerNormalization | ✓ | ✓ | |
 | LeakyRelu | ai.onnx(7-15, 16+) | leakyRelu | ✓ | ✓ | |
 | Less | ai.onnx(7-8, 9-12, 13+) | lesser | ✓ | ✓ | |
 | LessOrEqual | ai.onnx(12-15, 16+) | lesserOrEqual | ✓ | ✓ | |

onnxruntime/core/providers/webnn/builders/helper.h

@@ -160,7 +160,7 @@ static const InlinedHashMap<std::string, WebnnOpInfo> op_map = {
     {"ArgMax", {"argMax", true}},
     {"ArgMin", {"argMin", true}},
     {"AveragePool", {"averagePool2d", true}},
-    {"BatchNormalization", {"batchNormalization", false}},
+    {"BatchNormalization", {"batchNormalization", true}},
     {"Cast", {"cast", true}},
     {"Ceil", {"ceil", true}},
     {"Clip", {"clamp", true}},
@@ -190,8 +190,8 @@ static const InlinedHashMap<std::string, WebnnOpInfo> op_map = {
     {"HardSigmoid", {"hardSigmoid", true}},
     {"HardSwish", {"hardSwish", true}},
     {"Identity", {"identity", true}},
-    {"InstanceNormalization", {"instanceNormalization", false}},
-    {"LayerNormalization", {"layerNormalization", false}},
+    {"InstanceNormalization", {"instanceNormalization", true}},
+    {"LayerNormalization", {"layerNormalization", true}},
     {"LeakyRelu", {"leakyRelu", true}},
     {"Less", {"lesser", true}},
     {"LessOrEqual", {"lesserOrEqual", true}},

onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc

@@ -87,11 +87,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
     int64_t axis = helper.Get("axis", -1);
     axis = HandleNegativeAxis(axis, rank);
     std::vector<uint32_t> axes(rank - SafeInt<uint32_t>(axis));
-    if (model_builder.GetPreferredLayout() == DataLayout::NHWC && axis > 1) {
-      std::iota(axes.begin(), axes.end(), axis - 1);
-    } else {
-      std::iota(axes.begin(), axes.end(), axis);
-    }
+    std::iota(axes.begin(), axes.end(), axis);
+
     options.set("axes", emscripten::val::array(axes));
     output = model_builder.GetBuilder().call<emscripten::val>("layerNormalization", input, options);
   } else if (op_type == "InstanceNormalization") {