Applying autotest module on existing complex operators

[MarioLulab]

Original requirements

Autotest: We found that the previous testing of operators supporting complex tensor was not complete. We decided to reuse the real tensor operator tests to ensure completeness. Since complex tensor tests are supported in the autotest module from pr (#10027) , in this pr we applied the autotest module to the tests of complex tensor operators already available in Oneflow

Fix: In addition, the autograd rules for some previous operators of complex numbers might not conform to the convention of "Conjugate Wirtinger Derivative". We have fixed these bugs in this pr at the same time.

Main Works

Applying autotest module on existing operators that have already support complex tensor:

Complex and Real Behave the Same Way: means we don't need to add conjugate operation in op grad. Because regardless of whether the input data type involved in the operation is real or complex, the gradient result using the winterger derivative is the same as the real derivative rule,

Grad Not Supported in OF: means the grad of this op is not supported in oneflow

• broadcast_elementwise_binary

  Op	complex type	Backend	Using autotest	conjugate Wirtinger derivative
  Add	cp64, cp128	CPU, CUDA	DONE	Complex and Real Behave the Same Way
  Mul	cp64, cp128	CPU, CUDA	DONE	DONE
  Sub	cp64, cp128	CPU, CUDA	DONE	Complex and Real Behave the Same Way
  Equal	cp64, cp128	CPU, CUDA	DONE	Complex and Real Behave the Same Way
  NotEqual	cp64, cp128	CPU, CUDA	DONE	Grad Not Supported in OF

• broadcast_elementwise_unary

  Op	complex type	Backend	Using autotest	conjugate Wirtinger derivative
  Cast	cp64, cp128	CPU, CUDA	DONE	Complex and Real Behave the Same Way

• other exisiting operations

  Op	complex type	Backend	Using autotest	conjugate Wirtinger derivative
  constant_pad	cp64, cp128	CPU, CUDA	Done	Complex and Real Behave the Same Way
  reduce_sum	cp64, cp128	CPU, CUDA	TO-DO	Complex and Real Behave the Same Way

注意：

复数基础设施建设系列 pr:

1. 使用 primitive 来实现 conj, real 等常见复数算子： use primitive to implement "conj_physical", "real" and "imag" op #10281
2. 将现有支持复数数据类型的算子测例迁移到 autotest 模块中，以使复数算子复用实数算子的测试用例：Applying autotest module on existing complex operators #10284
3. 继续拓展支持复数数据类型的算子，比如 matmul, sqrt, div 等：add mm, sqrt, div operators for complex tensor to support OneScience #10269
   依赖关系：
   本 pr 基于：pr1，需要在 merge pr1 后，再 Merge 本 pr

[levi131]

LGTM