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use primitive to implement "conj_physical", "real" and "imag" op #10281

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merged 7 commits into from
Jun 5, 2023

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@MarioLulab MarioLulab commented May 22, 2023

Original requirements

现有的 conj kernel, real kernel, imag kernel 使用 KernelUtil 来进行组织,在 kernel 层面复数数据类型和实数数据类型的调用 conj 无法实现统一。故移除原有 KernelUtil 的实现,使用 ElementwiseUnary 的 primitive 进行实现

  • 新增 kConj 这一 UnaryOp,使用 ElementwiseUnary 的 Primitive 实现 conj,并移除原有 conj 的 KernelUtil
  • 新增 kRealkRealGrad UnaryOp,使用 ElementwiseUnary 的 Primitive 实现 real 和 real_grad,并移除原有 real 和 real_grad 的 KernelUtil
  • 新增 kImagkImagGrad UnaryOp,使用 ElementwiseUnary 的 Primitive 实现 imag 和 imag_grad,并移除原有 imag 和 imag_grad 的 KernelUtil

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:pr2pr3 的基础,请优先 merge 此 pr

@MarioLulab MarioLulab marked this pull request as draft May 22, 2023 07:20
@MarioLulab MarioLulab changed the title use primitive to implement "conj_physical" op use primitive to implement "conj_physical", "real" and "imag" op May 22, 2023
@levi131 levi131 self-assigned this May 22, 2023
@levi131 levi131 marked this pull request as ready for review May 23, 2023 03:18
@levi131 levi131 requested a review from hjchen2 as a code owner May 23, 2023 03:18
@MarioLulab MarioLulab requested review from oneflow-ci-bot and removed request for oneflow-ci-bot May 23, 2023 03:47
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LGTM

@levi131 levi131 merged commit e4d79e8 into Oneflow-Inc:master Jun 5, 2023
L-Xiafeng pushed a commit that referenced this pull request Jun 26, 2023
### 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"](https://en.wikipedia.org/wiki/Wirtinger_derivatives). 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:
#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:#10284
3. 继续拓展支持复数数据类型的算子,比如 matmul, sqrt, div
等:#10269
依赖关系:
本 pr 基于:[pr1](https://github.com/Oneflow-Inc/oneflow/pull/10281),需要在
merge [pr1](#10281) 后,再 Merge
本 pr
L-Xiafeng added a commit that referenced this pull request Jun 30, 2023
…10269)

### Original requirements
AI for Science models FNO use a series of operators such as sqrt, mm,
div, etc. for complex tensor. So we need to support complex tensor for
these ops.


#### Main Works
**Support existing ops for complex tensor in this pr as below:**

- broadcast_elementwise_binary
  | Op   |      complex type      |  Backend  |  Progress  |
  |:-----:|:-------------:|:------:|:------:|
  | scalar_div |  cp64, cp128 | CPU, CUDA | Done |
  | broadcast_div |  cp64, cp128 | CPU, CUDA | Done |

- broadcast_elementwise_unary
  | Op   |      complex type      |  Backend  |  Progress   |
  |----------|:-------------:|:------:|:------:|
  | sqrt |  cp64, cp128 | CPU, CUDA | Done |
  | negative | cp64, cp128 | CPU, CUDA | Done |


- other exisiting operations
  | Op   |      complex type      |  Backend  |  Progress   |
  |----------|:-------------:|:------:|:------:|
  | matmul |  cp64, cp128 | CPU, CUDA | Done |
  | batch_matmul |    cp64, cp128   |  CPU, CUDA | Done |
  | broadcast_matmul | cp64, cp128 | CPU, CUDA | Done |
  | matrix_vector_product | cp64, cp128 | CPU, CUDA | Done |
  | reduce_sum_like | cp64, cp128 | CPU, CUDA | Done |

## 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:
#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:#10284
3. 继续拓展支持复数数据类型的算子,比如 matmul, sqrt, div
等:#10269
依赖关系:
本 pr 基于:[pr1](#10281) 和
[pr2](#10284) ,需要在 merge
[pr1](#10281) 和
[pr2](#10284) 后,再 Merge 本 pr

---------

Co-authored-by: levi131 <limaolin@oneflow.org>
Co-authored-by: levi131 <83750468+levi131@users.noreply.github.com>
Co-authored-by: lijunlin <70465472+L-Xiafeng@users.noreply.github.com>
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3 participants