[Pten] Adjust the params of creation kernel for inference (#39573)

* remove manual_api

* change sig map of full and empty

* fix fill_any_like_xpu_op

* fix fill_any_like_xpu_op

* fix problem of fill_any_like_xpu_op

* fix conflict

* polish code

paddle/fluid/eager/api/utils/tensor_utils.cc

@@ -43,7 +43,7 @@ paddle::experimental::Tensor CreateTensorWithValue(
     bool is_leaf) {
   paddle::experimental::Tensor out = paddle::experimental::full(
       paddle::framework::vectorize(ddim), paddle::experimental::Scalar(value),
-      dtype, pten::TransToPtenBackend(place), layout);
+      dtype, pten::TransToPtenBackend(place));
 
   auto meta = EagerUtils::autograd_meta(&out);
   if (is_leaf) {

paddle/fluid/operators/fill_any_like_op.h

@@ -33,6 +33,7 @@ class FillAnyLikeKernel : public framework::OpKernel<T> {
                                 float, T>::type>::type;
 
   void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<framework::Tensor>("X");
     auto* out = context.Output<framework::Tensor>("Out");
     out->mutable_data<T>(context.GetPlace());
 
@@ -65,7 +66,7 @@ class FillAnyLikeKernel : public framework::OpKernel<T> {
     pten::FullLikeKernel<T>(
         static_cast<const typename paddle::framework::ConvertToPtenContext<
             DeviceContext>::TYPE&>(dev_ctx),
-        value, out);
+        *x, value, pten::DataType::UNDEFINED, out);
   }
 };
 

paddle/fluid/operators/fill_any_like_op_xpu.cc

@@ -31,6 +31,7 @@ class FillAnyLikeXPUKernel : public framework::OpKernel<T> {
   using XPUInTDType = typename XPUTypeTrait<T>::Type;
 
   void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<framework::Tensor>("X");
     auto* out = context.Output<framework::Tensor>("Out");
     out->mutable_data<T>(context.GetPlace());
 
@@ -63,7 +64,7 @@ class FillAnyLikeXPUKernel : public framework::OpKernel<T> {
     pten::FullLikeKernel<T>(
         static_cast<const typename paddle::framework::ConvertToPtenContext<
             paddle::platform::XPUDeviceContext>::TYPE&>(dev_ctx),
-        value, out);
+        *x, value, pten::DataType::UNDEFINED, out);
   }
 };
 

paddle/pten/infermeta/nullary.cc

@@ -28,9 +28,8 @@ void CreateInferMetaBase(const std::vector<int64_t>& shape,
 
 void CreateInferMeta(const ScalarArray& shape,
                      DataType dtype,
-                     DataLayout layout,
                      MetaTensor* out) {
-  CreateInferMetaBase(shape.GetData(), dtype, layout, out);
+  CreateInferMetaBase(shape.GetData(), dtype, DataLayout::NCHW, out);
 }
 
 }  // namespace pten

paddle/pten/infermeta/nullary.h

@@ -33,9 +33,6 @@ void CreateInferMetaBase(const std::vector<int64_t>& shape,
                          DataLayout layout,
                          MetaTensor* out);
 
-void CreateInferMeta(const ScalarArray& shape,
-                     DataType dtype,
-                     DataLayout layout,
-                     MetaTensor* out);
+void CreateInferMeta(const ScalarArray& shape, DataType dtype, MetaTensor* out);
 
 }  // namespace pten

paddle/pten/infermeta/unary.cc

@@ -79,13 +79,10 @@ void CastInferMeta(const MetaTensor& x, DataType out_dtype, MetaTensor* out) {
   out->set_layout(x.layout());
 }
 
-void CreateLikeInferMeta(const MetaTensor& x,
-                         DataType dtype,
-                         DataLayout layout,
-                         MetaTensor* out) {
+void CreateLikeInferMeta(const MetaTensor& x, DataType dtype, MetaTensor* out) {
   out->set_dims(x.dims());
   out->set_dtype(dtype == DataType::UNDEFINED ? x.dtype() : dtype);
-  out->set_layout(layout == DataLayout::UNDEFINED ? x.layout() : layout);
+  out->set_layout(x.layout());
 }
 
 static pten::framework::DDim ValidateShape(

paddle/pten/infermeta/unary.h

@@ -41,10 +41,7 @@ void FlattenInferMeta(const MetaTensor& x,
 
 void CastInferMeta(const MetaTensor& x, DataType out_dtype, MetaTensor* out);
 
-void CreateLikeInferMeta(const MetaTensor& x,
-                         DataType dtype,
-                         DataLayout layout,
-                         MetaTensor* out);
+void CreateLikeInferMeta(const MetaTensor& x, DataType dtype, MetaTensor* out);
 
 void InferMetaFromVecValue(const MetaTensor& x,
                            const std::vector<int64_t>& shape,

paddle/pten/kernels/cpu/full_kernel.cc

@@ -16,7 +16,62 @@ limitations under the License. */
 
 #include "paddle/pten/backends/cpu/cpu_context.h"
 #include "paddle/pten/core/kernel_registry.h"
-#include "paddle/pten/kernels/impl/full_kernel_impl.h"
+
+#include "paddle/pten/kernels/funcs/eigen/common.h"
+#include "paddle/pten/kernels/funcs/eigen/eigen_function.h"
+
+namespace pten {
+
+template <typename T, typename Context, typename VType>
+void FullValue(const Context& dev_ctx, DenseTensor* tensor, VType val) {
+  dev_ctx.template Alloc<T>(tensor);
+  auto t = pten::EigenVector<T>::Flatten(*tensor);
+  t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(val));
+}
+
+template <typename T, typename Context>
+void FullKernel(const Context& dev_ctx,
+                const ScalarArray& shape,
+                const Scalar& val,
+                DataType dtype,
+                DenseTensor* out) {
+  out->ResizeAndAllocate(pten::framework::make_ddim(shape.GetData()));
+  FullValue<T>(dev_ctx, out, val.to<T>());
+}
+
+template <typename T, typename Context>
+void FullLikeKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const Scalar& val,
+                    DataType dtype,
+                    DenseTensor* out) {
+  auto value = val.to<float>();
+  using CommonType = typename std::common_type<
+      float,
+      typename std::conditional<std::is_same<T, pten::dtype::float16>::value,
+                                float,
+                                T>::type>::type;
+
+  auto common_type_value = static_cast<CommonType>(value);
+
+  PADDLE_ENFORCE_EQ(
+      (common_type_value >=
+       static_cast<CommonType>(std::numeric_limits<T>::lowest())) &&
+          (common_type_value <=
+           static_cast<CommonType>(std::numeric_limits<T>::max())),
+      true,
+      pten::errors::InvalidArgument(
+          "The filled value is out of range for target type, "
+          "current kernel type is %s, the range should between %f "
+          "and %f, but now value is %f.",
+          typeid(T).name(),
+          static_cast<CommonType>(std::numeric_limits<T>::lowest()),
+          static_cast<CommonType>(std::numeric_limits<T>::max()),
+          static_cast<float>(value)));
+  FullValue<T>(dev_ctx, out, value);
+}
+
+}  // namespace pten
 
 PT_REGISTER_KERNEL(full,
                    CPU,

paddle/pten/kernels/empty_kernel.cc

@@ -23,12 +23,16 @@ namespace pten {
 template <typename T, typename Context>
 void EmptyKernel(const Context& dev_ctx,
                  const ScalarArray& shape,
+                 DataType dtype,
                  DenseTensor* out) {
   out->ResizeAndAllocate(pten::framework::make_ddim(shape.GetData()));
 }
 
 template <typename T, typename Context>
-void EmptyLikeKernel(const Context& dev_ctx, DenseTensor* out) {
+void EmptyLikeKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     DataType dtype,
+                     DenseTensor* out) {
   dev_ctx.template Alloc<T>(out);
 }
 

paddle/pten/kernels/empty_kernel.h

@@ -25,10 +25,14 @@ namespace pten {
 template <typename T, typename Context>
 void EmptyKernel(const Context& dev_ctx,
                  const ScalarArray& shape,
+                 DataType dtype,
                  DenseTensor* out);
 
 template <typename T, typename Context>
-void EmptyLikeKernel(const Context& dev_ctx, DenseTensor* out);
+void EmptyLikeKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     DataType dtype,
+                     DenseTensor* out);
 
 // TODO(chenweihang): the tensor creation method need to be replaced later,
 // all kernel api call Empty here instead of making tensor self
@@ -52,27 +56,22 @@ DenseTensor Empty(const Context& dev_ctx) {
 template <typename T, typename Context>
 DenseTensor Empty(const Context& dev_ctx,
                   const ScalarArray& shape,
-                  DataType dtype = DataType::FLOAT32,
-                  Backend backend = Backend::CPU,  // Is backend needed here?
-                  DataLayout layout = DataLayout::NCHW) {
+                  DataType dtype = DataType::FLOAT32) {
   auto dense_out = Empty<T, Context>(dev_ctx);
   MetaTensor meta_out(&dense_out);
-  CreateInferMeta(shape, dtype, layout, &meta_out);
-  EmptyKernel<T, Context>(dev_ctx, shape, &dense_out);
+  CreateInferMeta(shape, dtype, &meta_out);
+  EmptyKernel<T, Context>(dev_ctx, shape, dtype, &dense_out);
   return dense_out;
 }
 
 template <typename T, typename Context>
-DenseTensor EmptyLike(
-    const Context& dev_ctx,
+DenseTensor EmptyLike(const Context& dev_ctx,
                       const DenseTensor& x,
-    DataType dtype = DataType::UNDEFINED,
-    Backend backend = Backend::UNDEFINED,  // Is backend needed here?
-    DataLayout layout = DataLayout::UNDEFINED) {
+                      DataType dtype = DataType::UNDEFINED) {
   auto dense_out = Empty<T, Context>(dev_ctx);
   MetaTensor meta_out(&dense_out);
-  CreateLikeInferMeta(x, dtype, layout, &meta_out);
-  EmptyLikeKernel<T, Context>(dev_ctx, &dense_out);
+  CreateLikeInferMeta(x, dtype, &meta_out);
+  EmptyLikeKernel<T, Context>(dev_ctx, x, dtype, &dense_out);
   return dense_out;
 }
 

paddle/pten/kernels/full_kernel.h

@@ -27,39 +27,37 @@ template <typename T, typename Context>
 void FullKernel(const Context& dev_ctx,
                 const ScalarArray& shape,
                 const Scalar& val,
+                DataType dtype,
                 DenseTensor* out);
 
 template <typename T, typename Context>
 void FullLikeKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
                     const Scalar& val,
+                    DataType dtype,
                     DenseTensor* out);
 
 template <typename T, typename Context>
 DenseTensor Full(const Context& dev_ctx,
                  const ScalarArray& shape,
                  const Scalar& val,
-                 DataType dtype = DataType::FLOAT32,
-                 Backend backend = Backend::CPU,  // Is backend needed here?
-                 DataLayout layout = DataLayout::NCHW) {
+                 DataType dtype = DataType::FLOAT32) {
   auto dense_out = Empty<T, Context>(dev_ctx);
   MetaTensor meta_out(&dense_out);
-  CreateInferMeta(shape, dtype, layout, &meta_out);
-  FullKernel<T, Context>(dev_ctx, shape, val, &dense_out);
+  CreateInferMeta(shape, dtype, &meta_out);
+  FullKernel<T, Context>(dev_ctx, shape, val, dtype, &dense_out);
   return dense_out;
 }
 
 template <typename T, typename Context>
-DenseTensor FullLike(
-    const Context& dev_ctx,
+DenseTensor FullLike(const Context& dev_ctx,
                      const DenseTensor& x,
                      const Scalar& val,
-    DataType dtype = DataType::UNDEFINED,
-    Backend backend = Backend::UNDEFINED,  // Is backend needed here?
-    DataLayout layout = DataLayout::UNDEFINED) {
+                     DataType dtype = DataType::UNDEFINED) {
   auto dense_out = Empty<T, Context>(dev_ctx);
   MetaTensor meta_out(&dense_out);
-  CreateLikeInferMeta(x, dtype, layout, &meta_out);
-  FullLikeKernel<T, Context>(dev_ctx, val, &dense_out);
+  CreateLikeInferMeta(x, dtype, &meta_out);
+  FullLikeKernel<T, Context>(dev_ctx, x, val, dtype, &dense_out);
   return dense_out;
 }
 

paddle/pten/kernels/gpu/full_kernel.cu

@@ -33,10 +33,11 @@ struct FullFuctor {
   }
 };
 
-template <typename T, typename ContextT>
-void FullKernel(const ContextT& dev_ctx,
+template <typename T, typename Context>
+void FullKernel(const Context& dev_ctx,
                 const ScalarArray& shape,
                 const Scalar& val,
+                DataType dtype,
                 DenseTensor* out) {
   out->Resize(paddle::framework::make_ddim(shape.GetData()));
   int numel = out->numel();
@@ -53,9 +54,11 @@ void FullKernel(const ContextT& dev_ctx,
   }
 }
 
-template <typename T, typename ContextT>
-void FullLikeKernel(const ContextT& dev_ctx,
+template <typename T, typename Context>
+void FullLikeKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
                     const Scalar& val,
+                    DataType dtype,
                     DenseTensor* out) {
   auto value = val.to<float>();
   using CommonType = typename std::common_type<

paddle/pten/kernels/xpu/full_kernel.cc

@@ -57,6 +57,7 @@ template <typename T, typename Context>
 void FullKernel(const Context& dev_ctx,
                 const ScalarArray& shape,
                 const Scalar& val,
+                DataType dtype,
                 DenseTensor* out) {
   out->ResizeAndAllocate(pten::framework::make_ddim(shape.GetData()));
   FullValueXPU<T>(dev_ctx, out, val.to<T>());
@@ -64,7 +65,9 @@ void FullKernel(const Context& dev_ctx,
 
 template <typename T, typename Context>
 void FullLikeKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
                     const Scalar& val,
+                    DataType dtype,
                     DenseTensor* out) {
   auto value = val.to<float>();
   using XPUInTDType = typename XPUTypeTrait<T>::Type;

paddle/pten/ops/compat/empty_sig.cc

@@ -18,11 +18,11 @@ namespace pten {
 
 KernelSignature EmptyOpArgumentMapping(const ArgumentMappingContext& ctx) {
   if (ctx.HasInput("ShapeTensor")) {
-    return KernelSignature("empty", {}, {"ShapeTensor"}, {"Out"});
+    return KernelSignature("empty", {}, {"ShapeTensor", "dtype"}, {"Out"});
   } else if (ctx.InputSize("ShapeTensorList") > 0) {
-    return KernelSignature("empty", {}, {"ShapeTensorList"}, {"Out"});
+    return KernelSignature("empty", {}, {"ShapeTensorList", "dtype"}, {"Out"});
   } else {
-    return KernelSignature("empty", {}, {"shape"}, {"Out"});
+    return KernelSignature("empty", {}, {"shape", "dtype"}, {"Out"});
   }
 }
 

paddle/pten/ops/compat/fill_any_like_sig.cc

@@ -18,7 +18,7 @@ namespace pten {
 
 KernelSignature FillAnyLikeOpArgumentMapping(
     const ArgumentMappingContext& ctx) {
-  return KernelSignature("full_like", {}, {"value"}, {"Out"});
+  return KernelSignature("full_like", {"X"}, {"value", "dtype"}, {"Out"});
 }
 
 }  // namespace pten

paddle/pten/ops/compat/fill_constant_sig.cc

@@ -23,42 +23,46 @@ KernelSignature FillConstantOpArgumentMapping(
     if (ctx.HasInput("ShapeTensor")) {
       if (ctx.HasInput("ValueTensor")) {
         return KernelSignature(
-            "full", {}, {"ShapeTensor", "ValueTensor"}, {"Out"});
+            "full", {}, {"ShapeTensor", "ValueTensor", "dtype"}, {"Out"});
       } else {
         const auto& str_value =
             paddle::any_cast<std::string>(ctx.Attr("str_value"));
         if (str_value.empty()) {
-          return KernelSignature("full", {}, {"ShapeTensor", "value"}, {"Out"});
+          return KernelSignature(
+              "full", {}, {"ShapeTensor", "value", "dtype"}, {"Out"});
         } else {
           return KernelSignature(
-              "full", {}, {"ShapeTensor", "str_value"}, {"Out"});
+              "full", {}, {"ShapeTensor", "str_value", "dtype"}, {"Out"});
         }
       }
     } else if (ctx.InputSize("ShapeTensorList") > 0) {
       if (ctx.HasInput("ValueTensor")) {
         return KernelSignature(
-            "full", {}, {"ShapeTensorList", "ValueTensor"}, {"Out"});
+            "full", {}, {"ShapeTensorList", "ValueTensor", "dtype"}, {"Out"});
       } else {
         const auto& str_value =
             paddle::any_cast<std::string>(ctx.Attr("str_value"));
         if (str_value.empty()) {
           return KernelSignature(
-              "full", {}, {"ShapeTensorList", "value"}, {"Out"});
+              "full", {}, {"ShapeTensorList", "value", "dtype"}, {"Out"});
         } else {
           return KernelSignature(
-              "full", {}, {"ShapeTensorList", "str_value"}, {"Out"});
+              "full", {}, {"ShapeTensorList", "str_value", "dtype"}, {"Out"});
         }
       }
     } else {
       if (ctx.HasInput("ValueTensor")) {
-        return KernelSignature("full", {}, {"shape", "ValueTensor"}, {"Out"});
+        return KernelSignature(
+            "full", {}, {"shape", "ValueTensor", "dtype"}, {"Out"});
       } else {
         const auto& str_value =
             paddle::any_cast<std::string>(ctx.Attr("str_value"));
         if (str_value.empty()) {
-          return KernelSignature("full", {}, {"shape", "value"}, {"Out"});
+          return KernelSignature(
+              "full", {}, {"shape", "value", "dtype"}, {"Out"});
         } else {
-          return KernelSignature("full", {}, {"shape", "str_value"}, {"Out"});
+          return KernelSignature(
+              "full", {}, {"shape", "str_value", "dtype"}, {"Out"});
         }
       }
     }

python/paddle/utils/code_gen/api.yaml

@@ -51,30 +51,28 @@
     func : dot
 
 - api : empty
-  args : (ScalarArray shape, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
+  args : (ScalarArray shape, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU)
   output: Tensor
   infer_meta :
     func : CreateInferMeta
-    param : [shape, dtype, layout]
+    param : [shape, dtype]
   kernel :
     func : empty
-    param : [shape]
+    param : [shape, dtype]
     data_type : dtype
     backend : place
-    layout : layout
 
 - api : empty_like
-  args : (Tensor x, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
+  args : (Tensor x, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED)
   output: Tensor
   infer_meta :
     func : CreateLikeInferMeta
-    param : [x, dtype, layout]
+    param : [x, dtype]
   kernel :
     func : empty_like
-    param : []
+    param : [x, dtype]
     data_type : dtype > x
     backend : place > x
-    layout : layout > x
 
 - api : flatten
   args : (Tensor x, int start_axis, int stop_axis)
@@ -85,30 +83,28 @@
     func : flatten
 
 - api : full
-  args : (ScalarArray shape, Scalar value, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU, DataLayout layout=DataLayout::NCHW)
+  args : (ScalarArray shape, Scalar value, DataType dtype=DataType::FLOAT32, Backend place=Backend::CPU)
   output: Tensor
   infer_meta :
     func : CreateInferMeta
-    param : [shape, dtype, layout]
+    param : [shape, dtype]
   kernel :
     func : full
-    param : [shape, value]
+    param : [shape, value, dtype]
     data_type : dtype
     backend : place
-    layout : layout
 
 - api : full_like
-  args : (Tensor x, Scalar value, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED, DataLayout layout = DataLayout::UNDEFINED)
+  args : (Tensor x, Scalar value, DataType dtype = DataType::UNDEFINED, Backend place = Backend::UNDEFINED)
   output: Tensor
   infer_meta :
     func : CreateLikeInferMeta
-    param : [x, dtype, layout]
+    param : [x, dtype]
   kernel :
     func : full_like
-    param : [value]
+    param : [x, value, dtype]
     data_type : dtype > x
     backend : place > x
-    layout : layout > x
 
 - api : matmul
   args : (Tensor x, Tensor y, bool transpose_x = false, bool transpose_y = false)
@@ -136,9 +132,9 @@
     func : multiply
 
 - api : ones_like
-  args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
+  args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED)
   output : Tensor
-  invoke : full_like(x, 1, dtype, place, layout)
+  invoke : full_like(x, 1, dtype, place)
 
 - api : reshape
   args : (Tensor x, ScalarArray shape)
@@ -185,6 +181,6 @@
     data_type : x
 
 - api : zeros_like
-  args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED, DataLayout layout=DataLayout::UNDEFINED)
+  args : (Tensor x, DataType dtype=DataType::UNDEFINED, Backend place=Backend::UNDEFINED)
   output : Tensor
-  invoke : full_like(x, 0, dtype, place, layout)
+  invoke : full_like(x, 0, dtype, place)

python/paddle/utils/code_gen/api_base.py

@@ -358,8 +358,8 @@ PADDLE_API {self.outputs['return_type']} {self.get_api_func_name() + '_'}({self.
 """
 
         if len(input_names) == 0:
-            assert attr_backend_count > 0 and attr_layout_count > 0 and attr_data_type_count > 0, \
-                f"{api} api: When there is no input tensor, the args must have 'Backend', 'DataLayout' and 'DataType'."
+            assert attr_backend_count > 0 and attr_data_type_count > 0, \
+                f"{api} api: When there is no input tensor, the args must have 'Backend' and 'DataType'."
 
         kernel_select_args = ""
         for input_name in input_names:

python/paddle/utils/code_gen/wrapped_infermeta_gen.py

@@ -29,30 +29,36 @@ def gene_wrapped_infermeta_and_register(api):
 PT_REGISTER_INFER_META_FN({api.kernel['func'][0]}, pten::{api.infer_meta['func']});"""
 
         if api.infer_meta['param'] is not None:
+            kernel_params = api.kernel['param']
+            if kernel_params is None:
+                kernel_params = api.inputs['names'] + api.attrs['names']
+            if kernel_params == api.infer_meta['param']:
+                return '', '', register_code
+
+            assert len(api.infer_meta['param']) <= len(kernel_params), \
+                 f"{api.api} api: Parameters error. The params of infer_meta should be a subset of kernel params."
+
             tensor_type_map = {
                 'const Tensor&': 'const MetaTensor&',
                 'const std::vector<Tensor>&': 'const std::vector<MetaTensor>&',
                 'Tensor': 'MetaTensor*',
                 'std::vector<Tensor>': 'std::vector<MetaTensor>*',
             }
+
             wrapped_infermeta_name = get_wrapped_infermeta_name(api.api)
             args = []
-            check_args = []
             for input_name in api.inputs['names']:
+                if input_name in kernel_params:
                     args.append(tensor_type_map[api.inputs['input_info'][
                         input_name]] + ' ' + input_name)
-                check_args.append(input_name)
             for attr_name in api.attrs['names']:
+                if attr_name in kernel_params:
                     args.append(api.attrs['attr_info'][attr_name][0] + ' ' +
                                 attr_name)
-                check_args.append(attr_name)
             for i, out_type in enumerate(api.outputs['types']):
                 args.append(tensor_type_map[out_type] + ' ' + api.outputs[
                     'names'][i])
 
-            if check_args == api.infer_meta['param']:
-                return '', '', register_code
-
             invoke_param = api.infer_meta['param']
             invoke_param.extend(api.outputs['names'])