diff --git a/onnxruntime/contrib_ops/cpu/image_scaler.h b/onnxruntime/contrib_ops/cpu/image_scaler.h
index 9e9d9908ab188..865bca51f1e85 100644
--- a/onnxruntime/contrib_ops/cpu/image_scaler.h
+++ b/onnxruntime/contrib_ops/cpu/image_scaler.h
@@ -16,8 +16,8 @@ template <typename T>
 class ImageScaler final : public OpKernel {
  public:
   ImageScaler(const OpKernelInfo& info) : OpKernel(info) {
-    ORT_ENFORCE(info.GetAttr<float>("scale", &scale_).IsOK());
-    ORT_ENFORCE(info.GetAttrs<float>("bias", bias_).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttr<float>("scale", &scale_));
+    ORT_THROW_IF_ERROR(info.GetAttrs<float>("bias", bias_));
   }
 
   Status Compute(OpKernelContext* context) const override {
diff --git a/onnxruntime/contrib_ops/cuda/collective/sharding.cc b/onnxruntime/contrib_ops/cuda/collective/sharding.cc
index b6b509023a1a9..1b4cc4502cff8 100644
--- a/onnxruntime/contrib_ops/cuda/collective/sharding.cc
+++ b/onnxruntime/contrib_ops/cuda/collective/sharding.cc
@@ -244,7 +244,7 @@ DistributedKernel::DistributedKernel(const OpKernelInfo& info) : NcclKernel(info
   // stored on a 1-D mesh with 2 devices and the second input on another 1-D
   // mesh with 1 device.
   std::vector<std::string> attr_input_device_mesh_shapes;
-  ORT_ENFORCE(info.GetAttrs<std::string>("input_device_mesh_shapes", attr_input_device_mesh_shapes).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("input_device_mesh_shapes", attr_input_device_mesh_shapes));
 
   // input_device_mesh_elements[i] is the flattened device mesh for the i-th input.
   // Note that its actual shape is input_device_mesh_shapes[i].
@@ -255,12 +255,12 @@ DistributedKernel::DistributedKernel(const OpKernelInfo& info) : NcclKernel(info
   //  Then the first input is stored on a 1-D mesh with 2 devices and the second
   //  input on another 1-D mesh with 1 device.
   std::vector<std::string> attr_input_device_mesh_elements;
-  ORT_ENFORCE(info.GetAttrs<std::string>("input_device_mesh_elements", attr_input_device_mesh_elements).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("input_device_mesh_elements", attr_input_device_mesh_elements));
 
   // input_shard_specs[i] is the sharding spec of the i-th input; e.g.,
   // "RR" if the i-th input is not sharded.
   std::vector<std::string> input_shard_specs;
-  ORT_ENFORCE(info.GetAttrs<std::string>("input_shard_specs", input_shard_specs).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("input_shard_specs", input_shard_specs));
 
   ORT_ENFORCE(attr_input_device_mesh_shapes.size() == attr_input_device_mesh_elements.size());
   ORT_ENFORCE(attr_input_device_mesh_shapes.size() == input_shard_specs.size());
@@ -274,13 +274,13 @@ DistributedKernel::DistributedKernel(const OpKernelInfo& info) : NcclKernel(info
   }
 
   std::vector<std::string> attr_output_device_mesh_shapes;
-  ORT_ENFORCE(info.GetAttrs<std::string>("output_device_mesh_shapes", attr_output_device_mesh_shapes).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("output_device_mesh_shapes", attr_output_device_mesh_shapes));
 
   std::vector<std::string> attr_output_device_mesh_elements;
-  ORT_ENFORCE(info.GetAttrs<std::string>("output_device_mesh_elements", attr_output_device_mesh_elements).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("output_device_mesh_elements", attr_output_device_mesh_elements));
 
   std::vector<std::string> output_shard_specs;
-  ORT_ENFORCE(info.GetAttrs<std::string>("output_shard_specs", output_shard_specs).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("output_shard_specs", output_shard_specs));
 
   ORT_ENFORCE(attr_output_device_mesh_shapes.size() == attr_output_device_mesh_elements.size());
   ORT_ENFORCE(attr_output_device_mesh_shapes.size() == output_shard_specs.size());
diff --git a/onnxruntime/contrib_ops/cuda/tensor/image_scaler.cc b/onnxruntime/contrib_ops/cuda/tensor/image_scaler.cc
index a2169b29dc8f5..befad5661c43f 100644
--- a/onnxruntime/contrib_ops/cuda/tensor/image_scaler.cc
+++ b/onnxruntime/contrib_ops/cuda/tensor/image_scaler.cc
@@ -26,8 +26,8 @@ REGISTER_KERNEL_TYPED(MLFloat16)
 
 template <typename T>
 ImageScaler<T>::ImageScaler(const OpKernelInfo& info) : CudaKernel(info) {
-  ORT_ENFORCE(info.GetAttr<float>("scale", &scale_).IsOK());
-  ORT_ENFORCE(info.GetAttrs<float>("bias", bias_).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttr<float>("scale", &scale_));
+  ORT_THROW_IF_ERROR(info.GetAttrs<float>("bias", bias_));
 
   b_data_ = GetScratchBuffer<float>(bias_.size(), nullptr);
   // the transfer in kernel construction need to be sync on default stream.
diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc b/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc
index c3a9e5950acce..19545d1554405 100644
--- a/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc
+++ b/onnxruntime/core/codegen/passes/op_ir_creator/nn/conv.cc
@@ -29,9 +29,9 @@ Status GENERIC_OP_IR_CREATOR_CLASS(Conv)::Evaluate(
   info.GetAttrOrDefault<int64_t>("group", &group, 1);
   info.GetAttrOrDefault<std::string>("auto_pad", &auto_pad, "NOTSET");
 
-  ORT_ENFORCE(info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<int64_t>("kernel_shape", kernel_shape));
   ORT_ENFORCE(kernel_shape.size() <= 2, "Only support 1D/2D convolution currently!");
-  ORT_ENFORCE(info.GetAttrs<int64_t>("strides", strides).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<int64_t>("strides", strides));
 
   dilations = info.GetAttrs<int64_t>("dilations", dilations).IsOK() ? dilations : std::vector<int64_t>(kernel_shape.size(), 1);
   ORT_ENFORCE(dilations == std::vector<int64_t>(kernel_shape.size(), 1), "Only support dilation is 1 currently");
diff --git a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc
index ecff2c7b73847..e9e20e8a43998 100644
--- a/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc
+++ b/onnxruntime/core/codegen/passes/op_ir_creator/tensor/pad.cc
@@ -23,9 +23,9 @@ Status GENERIC_OP_IR_CREATOR_CLASS(Pad)::Evaluate(
   std::vector<int64_t> pads;
   float value;
 
-  ORT_ENFORCE(attrs.GetAttr<std::string>("mode", &mode).IsOK());
-  ORT_ENFORCE(attrs.GetAttrs<int64_t>("pads", pads).IsOK());
-  ORT_ENFORCE(attrs.GetAttr<float>("value", &value).IsOK());
+  ORT_THROW_IF_ERROR(attrs.GetAttr<std::string>("mode", &mode));
+  ORT_THROW_IF_ERROR(attrs.GetAttrs<int64_t>("pads", pads));
+  ORT_THROW_IF_ERROR(attrs.GetAttr<float>("value", &value));
 
   if (mode != "constant" && mode != "edge" && mode != "reflect")
     return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Pad: Unsupported padding mode!");
diff --git a/onnxruntime/core/providers/cpu/ml/category_mapper.h b/onnxruntime/core/providers/cpu/ml/category_mapper.h
index 62432a0ef00ff..481cc8cebdcd9 100644
--- a/onnxruntime/core/providers/cpu/ml/category_mapper.h
+++ b/onnxruntime/core/providers/cpu/ml/category_mapper.h
@@ -16,11 +16,11 @@ class CategoryMapper final : public OpKernel {
     std::vector<std::string> string_categories;
     std::vector<int64_t> int_categories;
 
-    ORT_ENFORCE(info.GetAttrs<std::string>("cats_strings", string_categories).IsOK());
-    ORT_ENFORCE(info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("cats_strings", string_categories));
+    ORT_THROW_IF_ERROR(info.GetAttrs<int64_t>("cats_int64s", int_categories));
 
-    ORT_ENFORCE(info.GetAttr<std::string>("default_string", &default_string_).IsOK());
-    ORT_ENFORCE(info.GetAttr<int64_t>("default_int64", &default_int_).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttr<std::string>("default_string", &default_string_));
+    ORT_THROW_IF_ERROR(info.GetAttr<int64_t>("default_int64", &default_int_));
 
     auto num_entries = string_categories.size();
 
diff --git a/onnxruntime/core/providers/cpu/ml/label_encoder.h b/onnxruntime/core/providers/cpu/ml/label_encoder.h
index a935fd64d5da4..1b4fa01900ae9 100644
--- a/onnxruntime/core/providers/cpu/ml/label_encoder.h
+++ b/onnxruntime/core/providers/cpu/ml/label_encoder.h
@@ -15,7 +15,7 @@ class LabelEncoder final : public OpKernel {
   LabelEncoder(const OpKernelInfo& info) : OpKernel(info) {
     std::vector<std::string> string_classes;
 
-    ORT_ENFORCE(info.GetAttrs<std::string>("classes_strings", string_classes).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttrs<std::string>("classes_strings", string_classes));
 
     ORT_ENFORCE(info.GetAttr<std::string>("default_string", &default_string_).IsOK());
     ORT_ENFORCE(info.GetAttr<int64_t>("default_int64", &default_int_).IsOK());
@@ -53,8 +53,8 @@ class LabelEncoder_2 final : public OpKernel {
     std::vector<TKey> keys;
     std::vector<TValue> values;
 
-    ORT_ENFORCE(info.GetAttrs<TKey>(_key_field_name, keys).IsOK());
-    ORT_ENFORCE(info.GetAttrs<TValue>(_value_field_name, values).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttrs<TKey>(_key_field_name, keys));
+    ORT_THROW_IF_ERROR(info.GetAttrs<TValue>(_value_field_name, values));
 
     auto num_keys = keys.size();
     auto num_values = values.size();
diff --git a/onnxruntime/core/providers/cpu/ml/linearregressor.cc b/onnxruntime/core/providers/cpu/ml/linearregressor.cc
index 6ed5545e7063f..4df7081b17b6e 100644
--- a/onnxruntime/core/providers/cpu/ml/linearregressor.cc
+++ b/onnxruntime/core/providers/cpu/ml/linearregressor.cc
@@ -21,8 +21,8 @@ LinearRegressor::LinearRegressor(const OpKernelInfo& info)
     : OpKernel(info),
       intercepts_(info.GetAttrsOrDefault<float>("intercepts")),
       post_transform_(MakeTransform(info.GetAttrOrDefault<std::string>("post_transform", "NONE"))) {
-  ORT_ENFORCE(info.GetAttr<int64_t>("targets", &num_targets_).IsOK());
-  ORT_ENFORCE(info.GetAttrs<float>("coefficients", coefficients_).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttr<int64_t>("targets", &num_targets_));
+  ORT_THROW_IF_ERROR(info.GetAttrs<float>("coefficients", coefficients_));
 
   // use the intercepts_ if they're valid
   use_intercepts_ = intercepts_.size() == static_cast<size_t>(num_targets_);
diff --git a/onnxruntime/core/providers/cpu/ml/svmclassifier.cc b/onnxruntime/core/providers/cpu/ml/svmclassifier.cc
index 8c356b4c62023..4bfb0f673404a 100644
--- a/onnxruntime/core/providers/cpu/ml/svmclassifier.cc
+++ b/onnxruntime/core/providers/cpu/ml/svmclassifier.cc
@@ -32,8 +32,8 @@ SVMClassifier::SVMClassifier(const OpKernelInfo& info)
       probb_(info.GetAttrsOrDefault<float>("prob_b")),
       support_vectors_(info.GetAttrsOrDefault<float>("support_vectors")),
       post_transform_(MakeTransform(info.GetAttrOrDefault<std::string>("post_transform", "NONE"))) {
-  ORT_ENFORCE(info.GetAttrs<float>("rho", rho_).IsOK());
-  ORT_ENFORCE(info.GetAttrs<float>("coefficients", coefficients_).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<float>("rho", rho_));
+  ORT_THROW_IF_ERROR(info.GetAttrs<float>("coefficients", coefficients_));
 
   // prob_a and prob_b are optional for Z output
   ORT_ENFORCE(proba_.size() == probb_.size());
diff --git a/onnxruntime/core/providers/cpu/ml/svmclassifier.h b/onnxruntime/core/providers/cpu/ml/svmclassifier.h
index e2ba20e08e30e..e0303c10f670e 100644
--- a/onnxruntime/core/providers/cpu/ml/svmclassifier.h
+++ b/onnxruntime/core/providers/cpu/ml/svmclassifier.h
@@ -18,7 +18,7 @@ class SVMCommon {
   SVMCommon(const OpKernelInfo& info)
       : kernel_type_(MakeKernel(info.GetAttrOrDefault<std::string>("kernel_type", "LINEAR"))) {
     std::vector<float> kernel_params;
-    ORT_ENFORCE(info.GetAttrs<float>("kernel_params", kernel_params).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttrs<float>("kernel_params", kernel_params));
 
     if (!kernel_params.empty()) {
       gamma_ = kernel_params[0];
diff --git a/onnxruntime/core/providers/cpu/ml/svmregressor.cc b/onnxruntime/core/providers/cpu/ml/svmregressor.cc
index 68367470a6176..48792be5ffdbd 100644
--- a/onnxruntime/core/providers/cpu/ml/svmregressor.cc
+++ b/onnxruntime/core/providers/cpu/ml/svmregressor.cc
@@ -19,10 +19,10 @@ SVMRegressor<T>::SVMRegressor(const OpKernelInfo& info)
       support_vectors_(info.GetAttrsOrDefault<float>("support_vectors")),
       post_transform_(MakeTransform(info.GetAttrOrDefault<std::string>("post_transform", "NONE"))) {
   int64_t vector_count = 0;
-  ORT_ENFORCE(info.GetAttr<int64_t>("n_supports", &vector_count).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttr<int64_t>("n_supports", &vector_count));
   vector_count_ = narrow<ptrdiff_t>(vector_count);
-  ORT_ENFORCE(info.GetAttrs<float>("rho", rho_).IsOK());
-  ORT_ENFORCE(info.GetAttrs<float>("coefficients", coefficients_).IsOK());
+  ORT_THROW_IF_ERROR(info.GetAttrs<float>("rho", rho_));
+  ORT_THROW_IF_ERROR(info.GetAttrs<float>("coefficients", coefficients_));
   ORT_ENFORCE(!coefficients_.empty());
 
   auto onec = info.GetAttrOrDefault<int64_t>("one_class", 0);
diff --git a/onnxruntime/core/providers/cpu/nn/roi_pool.h b/onnxruntime/core/providers/cpu/nn/roi_pool.h
index c916d0b05c3e9..1719ee5055ed7 100644
--- a/onnxruntime/core/providers/cpu/nn/roi_pool.h
+++ b/onnxruntime/core/providers/cpu/nn/roi_pool.h
@@ -14,7 +14,7 @@ class RoiPool : public OpKernel {
  public:
   RoiPool(const OpKernelInfo& info) : OpKernel(info) {
     std::vector<int64_t> pooled_shape;
-    ORT_ENFORCE(info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK());
+    ORT_THROW_IF_ERROR(info.GetAttrs<int64_t>("pooled_shape", pooled_shape));
     ORT_ENFORCE(pooled_shape.size() == 2);
 
     pooled_height_ = pooled_shape[0];
diff --git a/onnxruntime/core/providers/cpu/nn/unpool.h b/onnxruntime/core/providers/cpu/nn/unpool.h
index 81733449c664d..b51241870b549 100644
--- a/onnxruntime/core/providers/cpu/nn/unpool.h
+++ b/onnxruntime/core/providers/cpu/nn/unpool.h
@@ -13,8 +13,7 @@ namespace onnxruntime {
 class MaxUnpool : public OpKernel {
  public:
   MaxUnpool(const OpKernelInfo& info) : OpKernel(info) {
-    ORT_ENFORCE(info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK(),
-                "No kernel shape is set.");
+    ORT_THROW_IF_ERROR(info.GetAttrs<int64_t>("kernel_shape", kernel_shape_));
 
     num_inputs_ = OpKernel::Node().InputDefs().size();
 
diff --git a/onnxruntime/core/providers/cpu/tensor/upsamplebase.h b/onnxruntime/core/providers/cpu/tensor/upsamplebase.h
index 0b3ce6f477843..a0e7ca1084fef 100644
--- a/onnxruntime/core/providers/cpu/tensor/upsamplebase.h
+++ b/onnxruntime/core/providers/cpu/tensor/upsamplebase.h
@@ -77,7 +77,7 @@ class UpsampleBase {
 
     auto input_count = info.GetInputCount();
     if (input_count == 1) {  // opset < 10
-      ORT_ENFORCE(info.GetAttrs<float>("scales", scales_).IsOK());
+      ORT_THROW_IF_ERROR(info.GetAttrs<float>("scales", scales_));
       ORT_THROW_IF_ERROR(ScalesValidation(scales_, mode_));
       scales_cached_ = true;
     }
diff --git a/onnxruntime/core/providers/js/operators/conv.h b/onnxruntime/core/providers/js/operators/conv.h
index 3a01a4aa46be4..8f438a319f138 100644
--- a/onnxruntime/core/providers/js/operators/conv.h
+++ b/onnxruntime/core/providers/js/operators/conv.h
@@ -30,7 +30,7 @@ class ConvBase : public JsKernel {
     }
     if (is_fused_conv) {
       ORT_THROW_IF_ERROR(info.GetAttr<std::string>("activation", &conv_attrs_.activation));
-      ORT_ENFORCE(info.GetAttrs<float>("activation_params", activation_params).IsOK());
+      ORT_THROW_IF_ERROR(info.GetAttrs<float>("activation_params", activation_params));
     } else {
       conv_attrs_.activation = info.GetAttrOrDefault<std::string>("activation", "");
       activation_params = info.GetAttrsOrDefault<float>("activation_params", activation_params);