# Python Operator 
To facilitate Python coders on model developing, onnxruntime provides a way to invoke operators implemented in Python.

## Implemenation
The feature is implemented under onnxruntime/core/language_interop_ops.
All Python C API dependent code are compiled into a dynamic linked library named pywrapper.
Before calling into Python script, pywrapper will convert onnxruntime tensor(s) to numpy(s), which get converted back when done.
<p>Here is a chart illustrating the calling sequence:
<pre>
onnxruntime                          pywrapper                          script
     |                                  |                                 |
     | ------------------------------>  |                                 |
     |       call with tensor(s)        | ------------------------------> |
     |                                  |         call with numpy(s)      | 
     |                                  |                                 | compute
     |                                  |  <----------------------------- |
     | <------------------------------  |           return numpys(s)      |
     |         return tensor(s)         |                                 |
</pre>

## Usage
Step 1, build onnxruntime with“--config Release --enable_language_interop_ops --build_shared_lib” and override existing onnxruntime binary with the latest, then copy onnxruntime_pywrapper.dll or libonnxruntime_pywrapper.so or libonnxruntime_pywrapper.dylib to the path where onnxruntime binary is placed.
Note:
* It is suggested to compile within the Python environment where inferencing will happen. For example, if inferencing will happen in a conda env named myconda1, please compile the binary within that environment as well;
* If "--numpy_version=..." is specified, Python operator will build with that version.

Step 2, create an onnx model containing Python operator nodes:
```python
ad1_node = helper.make_node('Add', ['A','B'], ['S'])
mul_node = helper.make_node('Mul', ['C','D'], ['P'])
py1_node = helper.make_node(op_type = 'PyOp', #required, must be 'PyOp'
                            inputs = ['S','P'], #required
                            outputs = ['L','M','N'], #required
                            domain = 'pyopmulti_1', #required, must be unique
                            input_types = [TensorProto.FLOAT, TensorProto.FLOAT], #required
                            output_types = [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT], #required
                            module = 'mymodule', #required
                            class_name = 'Multi_1', #required
                            compute = 'compute', #optional, 'compute' by default
                            W1 = '5', W2 = '7', W3 = '9') #optional, must all be strings
ad2_node = helper.make_node('Add', ['L','M'], ['H'])
py2_node = helper.make_node('PyOp',['H','N','E'],['O','W'], domain = 'pyopmulti_2',
                            input_types = [TensorProto.FLOAT, TensorProto.FLOAT, TensorProto.FLOAT],
                            output_types = [TensorProto.FLOAT, TensorProto.FLOAT],
                            module = 'mymodule', class_name = 'Multi_2')
sub_node = helper.make_node('Sub', ['O','W'], ['F'])
graph = helper.make_graph([ad1_node,mul_node,py1_node,ad2_node,py2_node,sub_node], 'multi_pyop_graph', [A,B,C,D,E], [F])
model = helper.make_model(graph, producer_name = 'pyop_model')
onnx.save(model, './model.onnx')
```
Step 3, implement mymodule.py:
```python
class Multi_1:
    def __init__(self, W1, W2, W3):
        self.W1 = int(W1)
        self.W2 = int(W2)
        self.W3 = int(W3)
    def compute(self, S, P):
        ret = S + P
        return ret + self.W1, ret + self.W2, ret + self.W3
class Multi_2:
    def compute(self, H, N, E):
        r1, r2 = H + N, N + E
        return r1, r2
```
Step 4, copy mymodule.py into Python sys.path, then reference with onnxruntime. On Windows, please set PYTHONHOME beforehand. It should point to directory where the python is installed, such as C:\Python37 or C:\ProgramData\Anaconda3\envs\myconda1 if it is in conda.

## Supported Data Types
* TensorProto.BOOL,
* TensorProto.UINT8,
* TensorProto.UINT16,
* TensorProto.UINT32,
* TensorProto.INT16,
* TensorProto.INT32,
* TensorProto.FLOAT,
* TensorProto.DOUBLE

## Limitations
* On Windows,  "--config Debug" has known issues,  build with "--config RelWithDebInfo" if need debugging symbols;
* Due to python C API restrictions, multi-threading is disabled, meaning Python operators will run sequentially.

## Test
The operator has been tested on multiple platforms, with or without conda:

Platform | Python 3.5 | Python 3.6 | Python 3.7
----------- | ------------| -----------  | -----------
Windows | (conda) passed | (conda) passed | passed
Linux | (conda) passed | (conda) passed | passed
Mac |  (conda) passed | (conda) passed | (conda) passed