[WebNN] Add limit to QDQ ops (#23076)

WebNN requires the `scale_shape` to be a subsample of the `input_shape`.

Author: Honry

js/web/docs/webnn-operators.md

@@ -27,7 +27,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | Cos | ai.onnx(7+) | cos | ✓ | ✓ | |
 | CumSum | ai.onnx(11-13, 14+) | cumulativeSum | ✓ | ✓ | 'axis' input should be a constant |
 | Div | ai.onnx(7-12, 13, 14+) | div | ✓ | ✓ | |
-| DequantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | dequantizeLinear | ✗ | ✓ | |
+| DequantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | dequantizeLinear | ✓ | ✓ | The shape of x_scale should be a subsample of the shape of input |
 | Dropout | ai.onnx(7-9, 10-11, 12, 13-21, 22+) | identity | ✓ | ✓ | Only supports test mode |
 | Einsum | ai.onnx(12+) | reshape, transpose, matmul, reduceSum, mul, triangular | ✓ | ✓ | |
 | Elu | ai.onnx(7+) | elu | ✓ | ✓ | WebNN CPU backend only supports 'alpha' value is 1.0 |
@@ -71,7 +71,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | Pad | ai.onnx(7-10, 11-12, 13-17, 18, 19-20, 21+) | pad | ✓ | ✓ | modes == 'wrap' is not supported |
 | Pow | ai.onnx(7-11, 12, 13-14, 15+) | pow | ✓ | ✓ | |
 | PRelu | ai.onnx(7-8, 9-15, 16+) | prelu | ✓ | ✓ | WebNN CPU backend restricts the last dimension of input and slope to be same (Chromium issue: https://issues.chromium.org/issues/335517470) |
-| QuantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | quantizeLinear | ✗ | ✓ | |
+| QuantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | quantizeLinear | ✓ | ✓ | The shape of x_scale should be a subsample of the shape of input |
 | Reciprocal | ai.onnx(7-12, 13+) | reciprocal | ✓ | ✓ | |
 | ReduceL1 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL1 | ✓ | ✓ | Input 'axes' if present should be a constant |
 | ReduceL2 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL2 | ✓ | ✓ | Input 'axes' if present should be a constant |

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

@@ -22,6 +22,8 @@ class QDQOpBuilder : public BaseOpBuilder {
                                const logging::Logger& logger) const override ORT_MUST_USE_RESULT;
 
   // Operator support related.
+  bool IsOpSupportedImpl(const InitializedTensorSet& /* initializers */, const Node& node,
+                         const WebnnDeviceType /* device_type */, const logging::Logger& logger) const override;
   bool HasSupportedInputsImpl(const InitializedTensorSet& /* initializers */, const Node& node,
                               const emscripten::val& wnn_limits, const logging::Logger& logger) const override;
 };
@@ -118,6 +120,38 @@ Status QDQOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   return Status::OK();
 }
 
+// Operator support related.
+bool QDQOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& /* initializers */,
+                                     const Node& node,
+                                     const WebnnDeviceType /* device_type */,
+                                     const logging::Logger& logger) const {
+  const auto& input_defs = node.InputDefs();
+
+  std::vector<int64_t> input_shape;
+  std::vector<int64_t> scale_shape;
+
+  if (!GetShape(*input_defs[0], input_shape, logger) || !GetShape(*input_defs[1], scale_shape, logger)) {
+    return false;
+  }
+
+  // WebNN requires the scale_shape to be a subsample of the input_shape.
+  if (scale_shape.size() > input_shape.size()) {
+    LOGS(logger, VERBOSE) << "The rank of scale is larger than the rank of input";
+    return false;
+  }
+
+  for (size_t i = 0; i < scale_shape.size(); ++i) {
+    auto scale_dim = scale_shape[scale_shape.size() - i - 1];
+    auto input_dim = input_shape[input_shape.size() - i - 1];
+    if (input_dim % scale_dim != 0) {
+      LOGS(logger, VERBOSE) << "The shape of scale is not a subsample of the shape of input";
+      return false;
+    }
+  }
+
+  return true;
+}
+
 bool QDQOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* initializers */, const Node& node,
                                           const emscripten::val& wnn_limits, const logging::Logger& logger) const {
   const auto& input_defs = node.InputDefs();