Cleanup CoreML EP's code to remove COREML_ENABLE_MLPROGRAM (microsoft#23490)

### Description
Cleanup CoreML EP's code to remove the COREML_ENABLE_MLPROGRAM macro.
Also, increase MINIMUM_COREML_VERSION(first version we support) to 5 .

Author: snnn

onnxruntime/core/providers/coreml/builders/impl/activation_op_builder.cc

@@ -97,7 +97,6 @@ Status ActivationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
                                                   const logging::Logger& logger) const {
   const auto& op_type(node.OpType());
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
     // https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html#module-coremltools.converters.mil.mil.ops.defs.iOS15.activation
@@ -166,9 +165,7 @@ Status ActivationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
 
     model_builder.AddOperation(std::move(op));
 
-  } else
-#endif  // (COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     std::unique_ptr<COREML_SPEC::NeuralNetworkLayer> layer = model_builder.CreateNNLayer(node);
 
     if (op_type == "Sigmoid") {

onnxruntime/core/providers/coreml/builders/impl/argmax_op_builder.cc

@@ -32,7 +32,6 @@ Status ArgMaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   const int64_t keepdims = helper.Get("keepdims", 1);
   const bool removedim = keepdims != 1;
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
     // https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html#module-coremltools.converters.mil.mil.ops.defs.iOS15.reduction
@@ -46,9 +45,7 @@ Status ArgMaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
     // the output of ArgMax must be int32
     AddOperationOutput(*op, *node.OutputDefs()[0], output_datatype);
     model_builder.AddOperation(std::move(op));
-  } else
-#endif  // (COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     auto* coreml_argmax = layer->mutable_argmax();
     coreml_argmax->set_axis(axis);
     coreml_argmax->set_removedim(removedim);
@@ -91,11 +88,9 @@ bool ArgMaxOpBuilder::IsOpSupportedImpl(const Node& node,
     return false;
   }
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (input_params.create_mlprogram) {
     return true;
   }
-#endif
 
   // If there are multiple downstream nodes and cast (toint32) is one of them
   // not supported, exit here

onnxruntime/core/providers/coreml/builders/impl/batch_norm_op_builder.cc

@@ -57,7 +57,6 @@ Status BatchNormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_bu
   const auto eps = helper.Get("epsilon", 1e-5f);
   const auto channels = scale_tensor.dims()[0];
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
     // https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html#coremltools.converters.mil.mil.ops.defs.iOS15.normalization.batch_norm
@@ -78,9 +77,7 @@ Status BatchNormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_bu
 
     AddOperationOutput(*op, *node.OutputDefs()[0]);
     model_builder.AddOperation(std::move(op));
-  } else
-#endif  // (COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     auto* coreml_batch_norm = layer->mutable_batchnorm();
     coreml_batch_norm->set_channels(channels);
     coreml_batch_norm->set_epsilon(eps);

onnxruntime/core/providers/coreml/builders/impl/binary_op_builder.cc

@@ -56,7 +56,6 @@ bool CheckIfBothInputShapesMatch(const Node& node, const logging::Logger& logger
 }
 }  // namespace
 
-#if defined(COREML_ENABLE_MLPROGRAM)
 static std::vector<int64_t> InferOutputShape(const std::vector<int64_t>& a, const std::vector<int64_t>& b) {
   std::vector<int64_t> output_shape;
   int64_t i_a = 0, j_b = 0;
@@ -112,14 +111,12 @@ static void AddVariadicInputs(std::unique_ptr<CoreML::Specification::MILSpec::Op
   }
   *op = std::move(op_prev);
 }
-#endif
 
 Status BinaryOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
                                               const logging::Logger& logger) const {
   const auto& op_type(node.OpType());
   const auto& input_defs(node.InputDefs());
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
 
@@ -153,9 +150,7 @@ Status BinaryOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const
     }
     AddOperationOutput(*op, *node.OutputDefs()[0]);
     model_builder.AddOperation(std::move(op));
-  } else
-#endif  // defined (COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     std::unique_ptr<COREML_SPEC::NeuralNetworkLayer> layer = model_builder.CreateNNLayer(node);
 
     if (op_type == "Add") {

onnxruntime/core/providers/coreml/builders/impl/builder_utils.cc

@@ -150,7 +150,6 @@ void CreateCoreMLWeight(CoreML::Specification::WeightParams& weight, gsl::span<c
   CreateCoreMLWeightConvertingDataToFloats(weight, data);
 }
 
-#if defined(COREML_ENABLE_MLPROGRAM)
 //
 // ML Program Utils
 //
@@ -448,6 +447,5 @@ void AddPadTypeAndPads(COREML_SPEC::MILSpec::Operation& op, ModelBuilder& model_
     }
   }
 }
-#endif  // defined(COREML_ENABLE_MLPROGRAM)
 }  // namespace coreml
 }  // namespace onnxruntime

onnxruntime/core/providers/coreml/builders/impl/builder_utils.h

@@ -50,7 +50,6 @@ void CreateCoreMLWeight(CoreML::Specification::WeightParams& weight, gsl::span<c
 // Copy the int64_t array to a coreml weight
 void CreateCoreMLWeight(CoreML::Specification::WeightParams& weight, gsl::span<const int64_t> data);
 
-#if defined(COREML_ENABLE_MLPROGRAM)
 //
 // MLProgram utils
 //
@@ -174,6 +173,5 @@ void AddOperationOutput(COREML_SPEC::MILSpec::Operation& op, const NodeArg& outp
 /// <param name="num_spatial_dims">Number of spatial dims in input. Generally rank - 2 (ignore N and C dims).</param>
 void AddPadTypeAndPads(COREML_SPEC::MILSpec::Operation& op, ModelBuilder& model_builder, std::string_view op_type,
                        const NodeAttrHelper& helper, int num_spatial_dims);
-#endif  // defined(COREML_ENABLE_MLPROGRAM)
 }  // namespace coreml
 }  // namespace onnxruntime

onnxruntime/core/providers/coreml/builders/impl/cast_op_builder.cc

@@ -27,9 +27,8 @@ class CastOpBuilder : public BaseOpBuilder {
 Status CastOpBuilder::AddToModelBuilderImpl([[maybe_unused]] ModelBuilder& model_builder,
                                             [[maybe_unused]] const Node& node,
                                             [[maybe_unused]] const logging::Logger& logger) const {
-// This is a special handling case for ArgMax Op, where argmax is followed by a cast to int32 type.
-// The ArgMax is fused with the Cast node and produces an int32 output.
-#if defined(COREML_ENABLE_MLPROGRAM)
+  // This is a special handling case for ArgMax Op, where argmax is followed by a cast to int32 type.
+  // The ArgMax is fused with the Cast node and produces an int32 output.
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
     // https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html#coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary.cast
@@ -73,7 +72,6 @@ Status CastOpBuilder::AddToModelBuilderImpl([[maybe_unused]] ModelBuilder& model
     AddOperationOutput(*op, *node.OutputDefs()[0], cast_to_type);
     model_builder.AddOperation(std::move(op));
   }
-#endif
 
   return Status::OK();
 }
@@ -134,7 +132,6 @@ bool CastOpBuilder::HasSupportedInputsImpl(const Node& node, [[maybe_unused]] co
     return false;
   }
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (input_params.create_mlprogram) {
     if ((input_type == ONNX_NAMESPACE::TensorProto_DataType_INT32 ||
          input_type == ONNX_NAMESPACE::TensorProto_DataType_INT64 ||
@@ -152,7 +149,6 @@ bool CastOpBuilder::HasSupportedInputsImpl(const Node& node, [[maybe_unused]] co
       return false;
     }
   }
-#endif
 
   // only support int64 coming from ArgMax (check for ArgMax is done in IsOpSupportedImpl())
   if (input_type != ONNX_NAMESPACE::TensorProto_DataType_INT64) {

onnxruntime/core/providers/coreml/builders/impl/clip_op_builder.cc

@@ -64,7 +64,6 @@ Status ClipOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
   bool has_min = min != std::numeric_limits<float>::lowest();
   bool has_max = max != std::numeric_limits<float>::max();
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
 
@@ -121,9 +120,7 @@ Status ClipOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
 
     AddOperationOutput(*op, output);
     model_builder.AddOperation(std::move(op));
-  } else
-#endif  // defined(COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     // TODO: CoreML has a Clip layer for NeuralNetwork. Added in CoreML 4. We could potentially use that if available
     // to simplify.
     // https://apple.github.io/coremltools/mlmodel/Format/NeuralNetwork.html#cliplayerparams

onnxruntime/core/providers/coreml/builders/impl/concat_op_builder.cc

@@ -26,7 +26,6 @@ class ConcatOpBuilder : public BaseOpBuilder {
 Status ConcatOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
                                               const Node& node,
                                               const logging::Logger& logger) const {
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;  // NOLINT
 
@@ -45,7 +44,6 @@ Status ConcatOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
     AddOperationOutput(*op, *node.OutputDefs()[0]);
     model_builder.AddOperation(std::move(op));
   } else  // NOLINT
-#endif    // defined(COREML_ENABLE_MLPROGRAM)
   {
     std::unique_ptr<COREML_SPEC::NeuralNetworkLayer> layer = model_builder.CreateNNLayer(node);
 

onnxruntime/core/providers/coreml/builders/impl/conv_op_builder.cc

@@ -52,7 +52,6 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
 
   NodeAttrHelper helper(node);
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (model_builder.CreateMLProgram()) {
     using namespace CoreML::Specification::MILSpec;
 
@@ -89,9 +88,7 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
     AddOperationOutput(*conv_op, *node.OutputDefs()[0]);
 
     model_builder.AddOperation(std::move(conv_op));
-  } else
-#endif  // defined(COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     std::unique_ptr<COREML_SPEC::NeuralNetworkLayer> layer = model_builder.CreateNNLayer(node);
 
     auto strides = helper.Get("strides", std::vector<int64_t>{1, 1});
@@ -225,14 +222,11 @@ bool ConvOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputPara
   const auto& weight_name = input_defs[1]->Name();
   const auto* weight = input_params.graph_viewer.GetConstantInitializer(weight_name);
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   if (input_params.create_mlprogram) {
     // ML Program supports non-const weight, 1D, 2D and 3D.
     // keep to 1D and 2D for consistency with the NeuralNetwork implementation for now.
     // add 3D support as/when needed.
-  } else
-#endif  // defined (COREML_ENABLE_MLPROGRAM)
-  {
+  } else {
     if (!weight) {
       LOGS(logger, VERBOSE) << "The weight of Conv [" << name << "] must be a constant initializer";
       return false;
@@ -257,7 +251,6 @@ bool ConvOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputPara
 
   NodeAttrHelper helper(node);
 
-#if defined(COREML_ENABLE_MLPROGRAM)
   // spec says same_lower is supported in CoreML 5. it lies. CoreML 6 is required otherwise you get
   //   `Unexpected value for parameter pad_type[0] "same_lower" not in ("custom", "same", "valid").`
   // We _could_ manually calculate the pads, but not implementing that until we have a real use case to justify
@@ -269,7 +262,6 @@ bool ConvOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputPara
       return false;
     }
   }
-#endif
 
   // there's no equivalent to allow a manual kernel shape in CoreML.
   // it's OK if a specified kernel_shape matches kH and kW dims of the weight input.