Advanced Usage of aot-type Custom Operators

Overview

aot-type custom operators use a pre-compilation approach, which requires developers to write the source code files for the corresponding function based on a specific interface, and compile the source code files in advance into a dynamic link library. Then, during network runtime, the framework will automatically call and execute the function in the dynamic link library. aot-type custom operators support CUDA language for GPU platforms and C and C++ languages for CPU platforms. For basic knowledge of developing aot-type custom operators, please refer to basic tutorial.

In this tutorial, we will demonstrate advanced features of aot-type custom operators, including:

auto-compilation of aot type custom operators;
Attributes and intermediate variables of aot-type custom operators;
Dynamic shape support for aot-type custom operators.

For the complete source code of the example, check here in the MindSpore source code.

The Introduction to the Advanced Usage Features of aot-type Custom Operators

Auto-compilation of aot-type Custom Operators

When the user’s aot type custom operator file is a single file and does not require custom compilation options during compilation, users can use the automatic compilation feature. In this way, users will provide the source file for the implementation of the custom operator, and MindSpore will automatically compile the source file into a binary library. Currently, this function supports C++ file compilation based on GCC and CUDA file compilation based on NVCC. When using the automatic compilation function, there are several points to note:

MindSpore recognizes the method of automatic compilation as a file name suffix. In order to use the auto compilation feature, please use a source file with a suffix of cpp, cc, or cu. In other cases, MindSpore will process as a binary library path.
The result of automatic compilation is in the folder akg_kernel_meta.
The default compilation options are:
- C++: g++ -std=c++17 --shared -fPIC -D_GLIBCXX_USE_CXX11_ABI=0 -I./ -o $object_path, $source_path
- CUDA 10: nvcc --shared -Xcompiler -fPIC -O3 -gencode arch=compute_70, code=sm_70 --use_fast_math --expt-relaxed-constexpr -D_GLIBCXX_USE_CXX11_ABI=0 -I./ -o $object_path, $source_path
- CUDA 11(or higher version): nvcc --shared -Xcompiler -fPIC -O3 -gencode arch=compute_80, code=sm_80 --use_fast_math --expt-relaxed-constexpr -D_GLIBCXX_USE_CXX11_ABI=0 -I./ -o $object_path, $source_path
MindSpore requires the compilation option of -D_ GLIBCXX_ USE_ CXX11_ ABI = 0, so please avoid using a CUDA software stack with a version lower than 10.1.168 on GPU platforms.

Attributes and Intermediate Variables of aot-type Custom Operators

Many commonly used operators have attributes, such as the kernel size, padding, and strides of the convlution operator. Operators with different attribute values have the same computational logic, with the only difference being the values of the attributes during initialization. In addition, during the calculation process of the operator, some additional memory spaces may be needed to store the intermediate variables. The following calculation is an example. If we consider the input_1 and input_2 to calculate output as follows:

tmp = Add(input_1, input_2)
output = ReduceSum(tmp, axis, keep_dims)

Here, we need to add the following intermediate variables and attributes to the operator in the computation function, including:

tmp as an intermediate variable to record the intermediate result of addition;
axis as an attribute of type int, and keep_dims as an attribute of type bool.

aot-type custom operators provide functionality to add attributes, and then we can define a class of custom operators with a single source code. These operators have the same computational logic but achieve different computational effects by assigning values to the attributes during operator initialization. Additionally, to allow MindSpore to manage memory allocation and release, aot-type custom operators provide interfaces to specify the size of intermediate variables, allowing MindSpore to allocate memory for computation.

Dynamic Shape Support for aot-type Custom Operators

Dynamic Shape refers to that the shapes of inputs or outputs of an operator depends on the specific operation and cannot be calculated in advance at compile time. Specifically, there are two cases: the shapes of the operator’s inputs are unknown at compile time, and the shapes of the operator’s outputs depend on the specific input values. The case that the shapes of the operator’s inputs are unknown at compile time is more common. Any operator, regardless of their own calculation logic, needs to support this case if it is used in a network that supports dynamic shape inputs.

Currently, the aot type custom operators support the dynamic shape scenario when the shape of the operator’s input is unknown at compile time. This is achieved by defining a C++ version of the shape derivation function to support type derivation for custom operators in this scenario.

It should be noted that custom operators do not yet support dynamic shape scenarios where the shape of the operator output depends on the value of a specific input.

The Introduction aot-type Custom Operator Advanced Usage Interface

Main Function

In the source code file, the main function of the operator implementation function must follow the following specifications:

extern "C" int FuncName(int nparam, void **params, int *ndims, int64_t **shapes, const char **dtypes, void *stream, void *extra);

The function name FuncName can be replaced with any valid function name. The return value is of type int, with 0 indicating normal exit and non-zero indicating an exception. The meaning of the parameter list is as follows:

nparam (int): The total number of inputs, outputs, and intermediate variables. For example, if the operator has 2 inputs, 1 output, and 1 intermediate variable, then nparam is 4.
params (void **): An array of pointers to inputs, outputs, and intermediate variables. For example, if the operator has 2 inputs, 1 output, and 1 intermediate variable, then params[0] points to the memory of the first input data, params[1] points to the memory of the second input data, params[2] points to the memory of the output data, and params[3] points to the memory of the intermediate variable.
ndims (int *): An array of dimensions for inputs, output,s and intermediate variables. For example, if params[i] is a tensor with shape [1024, 1024], then ndims[i] is 2.
shapes (int64_t **): An array of shapes for inputs, outputs, and intermediate variables. For example, if params[i] is a tensor with shape [1024, 1024], then shapes[i][0] is 1024 and shapes[i][1] is 1024.
dtypes (const char **): An array of data types for inputs, outputs, and intermediate variables. The elements in dtypes can take values among the list “float32”, “float16”, “float”, “float64”, “int”, “int8”, “int16”, “int32”, “int64”, “uint”, “uint8”, “uint16”, “uint32”, “uint64”, and “bool”.
stream (void *): The pointer to a CUDA stream, only required for GPU operator implementation.
extra_void (void *): The pointer to a data structure related to attributes.

Initialization Function

To support operator attributes and intermediate variables, we need to define an operator initialization function. The definition of the operator initialization function must follow the following specifications:

extern "C" int FuncNameInit(int *ndims, int64_t **shapes, const char **dtypes, AotExtra *extra);

The function name FuncName is the name of the operator main function. The return value is of type int, with 0 indicating normal exit and non-zero indicating an exception. The meaning of the parameter list is as follows:

ndims (int *): Array of dimensions for input and output shapes.
shapes (int64_t **): Array of shapes for inputs and outputs.
dtypes (const char **): Array of data types for inputs and outputs.
extra (AotExtra *): Custom operator extensions with attributes. The AotExtra type is defined in the header file custom_aot_extra.h provided by MindSpore.

Shape Inference Function

To support dynamic shape, a C++ version of the shape inference function needs to be added to the custom operator of Aot type. The definition of the operator shape inference function must meet the following specifications:

extern "C" std::vector<int64_t> FuncNameInferShape(int *ndims, int64_t **shapes, AotExtra *extra)

The function name FuncName is the name of the operator main function. The return value is of type std::vector<int64_t> and represents the output shape. The meaning of the parameter list is as follows:

ndims (int *): Array of dimensions for input shapes.
shapes (int64_t **): Array of shapes for inputs.
extra (AotExtra *): Pointer to an extension for attribute-bearing custom operators. The AotExtra type is defined in the header file custom_aot_extra.h provided by MindSpore.

Operator Attribute Registration (Python)

The initialization of operator attributes is implemented through the operator registration function. For each attribute, we create an attr for the operator registration file, setting the attribute name and value. The registration function is as follows:

def attr(self, name=None, param_type=None, value_type=None, default_value=None, **kwargs)

Please refer to the CustomRegOp interface documentation for the meaning of each parameter. When registering a custom operator of Aot type, we set the following four parameters:

name: the name of the attribute of the aot-type custom operator;
param_type: the parameter type of the attribute. For attributes of aot-type custom operators, this input is fixed to be “required”, which means it is a required parameter;
value_type: the numerical type of the attribute. For attributes of aot-type custom operators, this input can be a specific numerical type or “all”, which means no restrictions on the type;
The last input needs to specify the input name as value=, and the input value is the value of the attribute.

Advanced Usage Example of aot-type Custom Operator

Now we introduce the advanced usage of custom Aot operators using an example of a fused Add and ReduceSum operator. The operator first adds two inputs, and then performs sum operation along a certain axis. The basic calculation logic is as follows:

tmp = Add(input_1, input_2)
output = ReduceSum(tmp, axis, keep_dims)

Here, we need to add the following intermediate variables and attributes in the computation function, including:

tmp is an intermediate variable that records the intermediate result of the addition;
axis is a property of type int, and keep_dims is a property of type bool.

Operator Implementation File (C++/CUDA): kernel.cc

To implement the operator, we create a source file named kernel.cc, which includes an operator attribute class add_reduce_kernel_attr and three functions: CustomKernelInit, CustomKernelInferShape, and CustomKernel.

Operator Attribute Class

First, we define a data structure to store operator attributes, which inherits from AotKernelData. AotKernelData is the base class for custom operator attribute data structures. By downloading the header file custom_aot_extra.h provided by MindSpore and placing it in the same directory as the source file, we can use the related interfaces by including it with #include "custom_aot_extra.h" at the beginning of the file.

#include <vector>
#include "custom_aot_extra.h"
class add_reduce_kernel_attr : public AotKernelData {
 public:
  int64_t axis;
  bool keep_dim;
};

Here, we define the following variables in the attribute class add_kernel:

axis : member variable, type is int64_t;
keep_dim : member variable, type is bool.

Operator Initialization Function

After defining the operator attribute class, we define the operator initialization function. Notice that the initialization function name here is CustomKernelInit, and the corresponding prefix for the following functions should be CustomKernel.

extern "C" int CustomKernelInit(int *ndims, int64_t **shapes, const char **dtypes, AotExtra *extra) {
  size_t workspace_size = 1;
  for (size_t i = 0; i < ndims[0]; i++) {
    workspace_size *= shapes[0][i];
  }

  std::vector<size_t> workspace = {workspace_size * sizeof(float)};
  extra->SetWorkSpace(workspace);

  add_reduce_kernel_attr *kernel_data_ptr = new add_reduce_kernel_attr;
  kernel_data_ptr->axis = extra->Attr<int64_t>("axis");
  kernel_data_ptr->keep_dim = extra->Attr<bool>("keep_dim");
  extra->SetKernelData(kernel_data_ptr);
  return 0;
}

Here, we need a intermediate variable workspace to record the intermediate result of addition. The method is as follows:

Calculate the memory size required for workspace: Since the size of workspace is the same as that of the first input, we multiply the size of each dimension of shapes[0] to calculate the number of elements in workspace, and then multiply it by sizeof(float) to get the memory size (assuming the element type is float by default).
Store all the memory sizes of intermediate variables in a std::vector<size_t> object: std::vector<size_t> workspace = {workspace_size * sizeof(float)};. Here, since there is only one intermediate variable, the vector has only one element.
Set the memory size of the intermediate variable using the SetWorkSpace function of AotExtra *extra: extra->SetWorkSpace(workspace).

In addition, we need to obtain the values of two attributes, axis and keep_dim, as follows:

Create a pointer to an add_reduce_kernel_attr object: add_reduce_kernel_attr *kernel_ptr = new add_reduce_kernel_attr.
Retrieve the attribute values from extra and store them in the member variables of kernel_ptr: kernel_data_ptr->axis = extra->Attr<int64_t>("axis"); kernel_data_ptr->keep_dim = extra->Attr<bool>("keep_dim");. Here, reduce_axis and keep_dim are of type int and bool respectively. We use the corresponding template function of extra->Attr<T>(std::string name) to obtain the value of the attribute with the given type.
- The supported types for T in step 2 are bool, string, int64_t, float, std::vector<int64_t>, std::vector<float>, std::vector<std::vector<int64_t>>, and std::vector<std::vector<float>>.
Store kernel_ptr in extra for use during operator calculation: extra->SetKernelData(kernel_ptr).

Operator Shape Inference Function

To define a dynamic shape scene, we define a C++ version of the operator shape inference function as follows. Notice that the operator shape inference function name is CustomKernelInferShape, and shares the same prefix CustomKernel with the initialization function name CustomKernelInit.

#include <vector>
#include "custom_aot_extra.h"

extern "C" std::vector<int64_t> CustomKernelInferShape(int *ndims, int64_t **shapes, AotExtra *extra) {
  const int64_t kDynRankSize = -2;

  if (shapes[0][0] == kDynRankSize) {
    return std::vector<int64_t>{shapes[0][0]};
  }
  int64_t axis = extra->Attr<int64_t>("axis");
  bool keep_dim = extra->Attr<bool>("keep_dim");
  if (keep_dim) {
    if (axis == 0) {
      return std::vector<int64_t>{1, shapes[0][1]};
    } else {
      return std::vector<int64_t>{shapes[0][0], 1};
    }
  } else {
    return std::vector<int64_t>{shapes[0][1 - axis]};
  }
}

In the above example, we need to note the following:

According to the MindSpore specifications, dynamic shape inputs includes two cases: the dynamic shape case and the dynamic rank case, with corresponding shape inputs as follows:
- the dynamic shape case: If the size of a certain dimension of the input is unknown, it is represented by -1. For example, the shape of the input is [1024, -1, 1024], which indicates that the input is a three-dimensional tensor with dimensions of 1024 and -1 for the second dimension;
- the dynamic rank case: The number of dimensions of the input is unknown, and the shape of the input is fixed as [-2, ].
To support C++ shape inference functions, we need to handle cases when inputs are either dynamic shape or dynamic rank. For example, in the above example, if the input is of dynamic rank, the output will also be of dynamic rank. Therefore, when we find that the input is [-2, ], we directly return [-2, ].
For scenarios where the output shape depends on attributes, you can use the extra->Attr<T>(std::string name) template interface to obtain attributes.

Operator Computation Function (Main Function)

The interface specification of the operator computation function is the same as that of a custom operator without attributes. It is worth noting that the operator main function name CustomKernel needs to be the same as the prefix of the initialization function name CustomKernelInit and the operator shape inference function name CustomKernelInferShape mentioned above. The main function, together with the above two functions, forms the source file kernel.cc.

extern "C" int CustomKernel(int nparam, void **params, int *ndims, int64_t **shapes, const char **dtypes, void *stream,
                         void *extra_void) {
  constexpr int OUTPUT_INDEX = 2;

  float *input_1 = static_cast<float *>(params[0]);
  float *input_2 = static_cast<float *>(params[1]);
  float *output = static_cast<float *>(params[2]);
  float *tmp = static_cast<float *>(params[3]);

  // Add
  int in_size = 1;
  for (int i = 0; i < ndims[OUTPUT_INDEX]; i++) {
    in_size *= shapes[OUTPUT_INDEX][i];
  }

  for (int i = 0; i < in_size; i++) {
    tmp[i] = input_1[i] + input_2[i];
  }

  // ReduceSum
  AotExtra *extra = static_cast<AotExtra *>(extra_void);
  auto kernel_ptr = static_cast<add_reduce_kernel_attr *>(extra->KernelData());
  bool keep_dim = kernel_ptr->keep_dim;
  int64_t axis = kernel_ptr->axis;
  int64_t input_dim_1 = shapes[0][1];
  int size;
  if (keep_dim) {
    size = shapes[1][0] * shapes[1][1];
  } else {
    size = shapes[1][0];
  }

  int ext = shapes[0][axis];
  for (int i = 0; i < size; i++) {
    output[i] = 0;
    for (int j = 0; j < ext; j++) {
      int idx = input_dim_1 * (i * axis + j * (1 - axis)) + i * (1 - axis) + j * axis;
      output[i] = output[i] + tmp[idx];
    }
  }
  return 0;
}

In the computation of Add, we used the intermediate variable of the operator, and the method is as follows:

Convert the pointers in the params array to float * one by one. According to the introduction of the interface above, the elements in the array are: two input address pointers (input_1 and input_2), an output address pointer (output), and an intermediate variable address pointer (tmp);
Store the result of adding the two inputs into the intermediate variable: tmp[i] = input_1[i] + input_2[i].

In the computation of ReduceSum, we used the attribute value of the operator, and the method is as follows:

Convert the extra_void type to a AotExtra type pointer: AotExtra *extra = static_cast<AotExtra *>(extra_void).
Get the kernel_ptr object pointer created in the initialization function from extra: auto kernel_ptr = static_cast<add_reduce_kernel_attr *>(extra->KernelData()). Here, extra->KernelData() obtains a void object pointer, which needs to be further converted to the kernel_ptr object pointer.
Use the attribute values stored in kernel_ptr for calculation: bool keep_dim = kernel_ptr->keep_dim; int64_t axis = kernel_ptr->axis;. Here, we obtain the variables keep_dim and axis from kernel_ptr for computation.

Operator Definition File: test_custom_aot.py

To add aot-type custom operator to a MindSpore network using the above functions, we create the file test_custom_aot.py.

import numpy as np
from mindspore import Tensor
from mindspore import dtype as mstype
from mindspore.nn import Cell
import mindspore as ms
import mindspore.ops as ops
from mindspore.ops import DataType, CustomRegOp

class ReduceDynNet(Cell):
    def __init__(self, out_types, axis, keep_dim):
        super(ReduceDynNet, self).__init__()
        reduce_cpu_info = CustomRegOp("reduce_kernel_cpu") \
            .input(0, "x1") \
            .input(0, "x2") \
            .output(0, "y") \
            .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
            .attr("axis", "required", "all", value=axis) \
            .attr("keep_dim", "required", "all", value=keep_dim) \
            .target("CPU") \
            .get_op_info()
        # As the shape inference function of C++ version is defined above, the ouptut_shape can be 'None'
        self.program = ops.Custom("./kernel.cc:CustomKernel", None, out_types, "aot", reg_info=reduce_cpu_info)

    def construct(self, x, y):
        return self.program(x, y)

The ReduceDynNet in this file includes two parts: the operator registration function and the operator definition class.

Operator Registration

The assignment of operator attributes during initialization is implemented through the operator registration function. For the function of custom operator registration, please refer to the relevant documentation of CustomRegOp. For each attribute, we create an attr for the operator registration file reduce_cpu_info, setting the attribute name and value.

Each attr item here has four inputs: the first is the name, such as "axis" or "keep_dim"; the middle two are "required" and "all"; the last input needs to specify the input name as value=, and the input value is the value of the attribute, for example, value=axis and value=keep_dim here. We determine the values of these two parameters from the network input, and these values should match the types used in the extra->Attr<T> template interface in the initialization function and shape inference function above.

In addition, if we need to define multiple operator registration files, we need to use different operator file names, which is the argument of CustomRegOp, here it is "add_with_attr_kernel_cpu". If we want to define another operator with the same prototype but different attribute values, the name cannot be duplicated.

Operator Definition

In the Python file above, a custom operator of type aot is defined using the interface Custom of MindSpore: self.program = ops.Custom("./kernel.cc:CustomKernel", None, out_types, "aot", reg_info=reduce_cpu_info). Since we defined the C++ version of the shape inference function earlier, ouptut_shape can be set to None.

Notice that in the operator definition, we directly use the source file name ./kernel.cc, so we are utilizing the automatic compilation feature provided by MindSpore. Make sure that the corresponding compiler (g++ in this case, and nvcc for GPU environment) is available in the environment.

Operator Call

As a test, we add the __main__ function to the test_custom_aot.py file:

if __name__ == "__main__":
    shape = (4, 5)
    axis = 1
    keep_dim = False
    ms.set_context(device_target="CPU")

    input_x = np.ones(shape).astype(np.float32)
    input_y = np.ones(shape).astype(np.float32)

    test = ReduceDynNet(mstype.float32, axis, keep_dim)
    dyn_x = Tensor(shape=[4, None], dtype=mstype.float32)
    # set the net to dynamic shape
    test.set_inputs(dyn_x, dyn_x)
    output = test(Tensor(input_x),Tensor(input_y))
    print(output)

Execute the file to call the operator:

python test_custom_aot.py

Execution result is as follows:

[10. 10. 10. 10.]

Introduction to Multi-Output Custom Operators of the AOT Type

Custom operators of the AOT type support multiple outputs (outputs as tuples). The definition of the operator file for a custom operator with multiple outputs is similar to that of a single-output operator, but corresponding modifications need to be made based on the multi-output scenario, including:

Operator inference function: The output of the infer function needs to be written in the form of a tuple;
Operator registration file: The names and data type information of multiple outputs need to be listed;
Operator computation function: It needs to identify the pointers corresponding to multiple outputs.

Below, we demonstrate the method of defining a custom operator of the AOT type with multiple outputs using an example. For specific file usage, please refer to here.

Operator Inference Function

In the case of multiple outputs, the operator inference function should be written in the form of a tuple. Taking the case where the output shapes are constants as an example, the out_shapes in the custom operator below is ([3], [3], [3]), and out_dtypes is (mstype.float32, mstype.float32, mstype.float32), which correspond to the shapes and data types of the three outputs, respectively.

self.program = ops.Custom(func, ([3], [3], [3]), (mstype.float32, mstype.float32, mstype.float32), "aot", bprop, reg)

Operator Registering Function

When defining multiple outputs, we need to clearly specify the names of the inputs and outputs in sequence, and indicate the corresponding data formats for both inputs and outputs in the dtype_format section. For example:

multioutput_gpu_info = CustomRegOp() \
    .input(0, "x1") \
    .input(1, "x2") \
    .output(0, "y1") \
    .output(1, "y2") \
    .output(2, "y3") \
    .dtype_format(DataType.F32_Default, DataType.F32_Default,
                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
    .target("GPU") \
    .get_op_info()

Here, we define a registration file for an operator with two inputs and three outputs. Therefore, we add two input items and three output items in the registration file. Additionally, the five data formats defined in dtype_format correspond to the data format requirements for the two inputs and three outputs in sequence.

Operator Computation Function

The following CustomAddMulDiv function is the computation function of the custom op.

constexpr int THREADS = 1024;

__global__ void CustomAddMulDivKernel(float *input1, float *input2, float *output1, float *output2, float *output3,
                                      size_t size) {
  auto idx = blockIdx.x * THREADS + threadIdx.x;
  if (idx < size) {
    output1[idx] = input1[idx] + input2[idx];
    output2[idx] = input1[idx] * input2[idx];
    output3[idx] = input1[idx] / input2[idx];
  }
}

extern "C" int CustomAddMulDiv(int nparam, void **params, int *ndims, int64_t **shapes, const char **dtypes,
                               void *stream, void *extra) {
  cudaStream_t custream = static_cast<cudaStream_t>(stream);

  constexpr int OUTPUT_INDEX = 2;
  constexpr int TOTAL_PARAM_NUM = 5;

  // There are two inputs and three outputs, so the nparam should be 5.
  if (nparam != TOTAL_PARAM_NUM) {
    return 1;
  }

  // This is to check if the type of parameters the same as what the user wants.
  for (int i = 0; i < nparam; i++) {
    if (strcmp(dtypes[i], "float32") != 0) {
      return 2;
    }
  }

  // input1's index is 0, input2's index is 1, output1's index is 2, output2's index is 3 and output3's index is 4
  void *input1 = params[0];
  void *input2 = params[1];
  void *output1 = params[2];
  void *output2 = params[3];
  void *output3 = params[4];
  size_t size = 1;

  // Cumprod of output's shape to compute elements' num
  for (int i = 0; i < ndims[OUTPUT_INDEX]; i++) {
    size *= shapes[OUTPUT_INDEX][i];
  }
  int n = size / THREADS;

  // Do the computation
  CustomAddMulDivKernel<<<n + 1, THREADS, 0, custream>>>(static_cast<float *>(input1), static_cast<float *>(input2),
                                                         static_cast<float *>(output1), static_cast<float *>(output2),
                                                         static_cast<float *>(output3), size);
  // When return 0, MindSpore will continue to run if this kernel could launch successfully.
  return 0;
}

Please note that since the operator has two inputs and three outputs, nparam should be 5, and the five pointers in the params array should correspond to the two inputs and three outputs in sequence. Therefore, in the above code, we obtain the inputs and outputs as follows:

void *input1 = params[0];
void *input2 = params[1];
void *output1 = params[2];
void *output2 = params[3];
void *output3 = params[4];

For the complete operator computation file, please refer to here.

Operator in Scripts

When a custom operator with multiple outputs is involved in a net, the results can be used as a normal tuple, for example:

class AOTMultiOutputNet(Cell):
    def __init__(self, func, out_shapes, out_types, bprop=None, reg=None):
        super(AOTMultiOutputNet, self).__init__()

        self.program = ops.Custom(func, out_shapes, out_types, "aot", bprop, reg)
        self.add = ops.Add()
        self.mul = ops.Mul()

    def construct(self, x, y):
        aot = self.program(x, y)
        add_res = self.add(aot[0], aot[1])
        mul_res = self.mul(add_res, aot[2])
        return mul_res

if __name__ == "__main__":
  x = np.array([1.0, 1.0, 1.0]).astype(np.float32)
  y = np.array([1.0, 1.0, 1.0]).astype(np.float32)
  net = AOTMultiOutputNet("./add_mul_div.cu:CustomAddMulDiv", ([3], [3], [3]),
                          (mstype.float32, mstype.float32, mstype.float32), reg=multioutput_gpu_info)
  output = test(Tensor(input_x),Tensor(input_y))
  print(output)

Here aot as the output of the multi-output custom operator can be used as a tuple. Execution result is as follows:

[3. 3. 3.]