Update CI with onnxruntime==1.14.0 (#967)

* update CI

Signed-off-by: xadupre <[email protected]>

* fix unit test

Signed-off-by: xadupre <[email protected]>

* remove unnecessary print

Signed-off-by: xadupre <[email protected]>

* fix normalizer

Signed-off-by: xadupre <[email protected]>

---------

Signed-off-by: xadupre <[email protected]>

Author: xadupre

.azure-pipelines/linux-conda-CI.yml

@@ -14,28 +14,41 @@ jobs:
   strategy:
     matrix:
 
-      Py310-Onnx130-Rt131-Skl12:
+      Py310-Onnx130-Rt140-Skl121:
+        do.bench: '0'
+        python.version: '3.10'
+        numpy.version: '>=1.21.1'
+        scipy.version: '>=1.7.0'
+        onnx.version: 'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.13.0rc1'
+        onnx.target_opset: ''
+        onnxrt.version: 'onnxruntime==1.14.0' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003
+        sklearn.version: '>=1.2.1'
+        lgbm.version: ''
+        onnxcc.version: '>=1.8.1'  # git
+        run.example: '1'
+
+      Py310-Onnx130-Rt131-Skl120:
         do.bench: '0'
         python.version: '3.10'
         numpy.version: '>=1.21.0'
         scipy.version: '>=1.7.0'
         onnx.version: 'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.13.0rc1'
         onnx.target_opset: ''
         onnxrt.version: 'onnxruntime==1.13.1' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003
-        sklearn.version: '>=1.2'
+        sklearn.version: '==1.2.0'
         lgbm.version: ''
         onnxcc.version: '>=1.8.1'  # git
-        run.example: '1'
+        run.example: '0'
 
-      Py310-Onnx120-Rt131-Skl12:
+      Py310-Onnx120-Rt131-Skl120:
         do.bench: '0'
         python.version: '3.10'
         numpy.version: '>=1.21.0'
         scipy.version: '>=1.7.0'
         onnx.version: 'onnx==1.12.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4'
         onnx.target_opset: ''
         onnxrt.version: 'onnxruntime==1.13.1' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003
-        sklearn.version: '>=1.2'
+        sklearn.version: '==1.2.0'
         lgbm.version: ''
         onnxcc.version: '>=1.8.1'  # git
         run.example: '1'

.azure-pipelines/win32-conda-CI.yml

@@ -13,24 +13,33 @@ jobs:
     vmImage: 'windows-latest'
   strategy:
     matrix:
-      Py310-Onnx130-Rt131-Skl12:
+      Py310-Onnx130-Rt140-Skl121:
+        python.version: '3.10'
+        onnx.version:  'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4'
+        onnx.target_opset: ''
+        numpy.version: 'numpy>=1.22.3'
+        scipy.version: 'scipy'
+        onnxrt.version: 'onnxruntime==1.14.0' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003
+        onnxcc.version: 'onnxconverter-common>=1.8.1'  # git+https://github.com/microsoft/onnxconverter-common.git
+        sklearn.version: '>=1.2.1'
+      Py310-Onnx130-Rt131-Skl120:
         python.version: '3.10'
         onnx.version:  'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4'
         onnx.target_opset: ''
         numpy.version: 'numpy>=1.22.3'
         scipy.version: 'scipy'
         onnxrt.version: 'onnxruntime==1.13.1' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003
         onnxcc.version: 'onnxconverter-common>=1.8.1'  # git+https://github.com/microsoft/onnxconverter-common.git
-        sklearn.version: '>=1.2'
-      Py310-Onnx120-Rt131-Skl12:
+        sklearn.version: '==1.2.0'
+      Py310-Onnx120-Rt131-Skl120:
         python.version: '3.10'
         onnx.version:  'onnx==1.12.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4'
         onnx.target_opset: ''
         numpy.version: 'numpy>=1.22.3'
         scipy.version: 'scipy'
         onnxrt.version: 'onnxruntime==1.13.1' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003
         onnxcc.version: 'onnxconverter-common>=1.8.1'  # git+https://github.com/microsoft/onnxconverter-common.git
-        sklearn.version: '>=1.2'
+        sklearn.version: '==1.2.0'
       Py310-Onnx120-Rt120-Skl11:
         python.version: '3.10'
         onnx.version:  'onnx==1.12.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4'

skl2onnx/__init__.py

@@ -3,7 +3,7 @@
 """
 Main entry point to the converter from the *scikit-learn* to *onnx*.
 """
-__version__ = "1.13.1"
+__version__ = "1.14.0"
 __author__ = "Microsoft"
 __producer__ = "skl2onnx"
 __producer_version__ = __version__

skl2onnx/algebra/onnx_ops.py

@@ -363,9 +363,16 @@ def OnnxSplitApi18(*x, axis=0, split=None, num_outputs=None,
                         "split or num_outputs or output_names "
                         "must be specified since opset 18.")
                 num_outputs = len(output_names)
+            if num_outputs is None:
+                raise AttributeError(
+                    "num_outputs cannot be None for Split-18.")
             return OnnxSplit_18(  # noqa
                 *x, axis=axis, op_version=op_version,
                 num_outputs=num_outputs, output_names=output_names)
+        if num_outputs is None:
+            return OnnxSplit_18(  # noqa
+                *x, np.array(split, dtype=np.int64), axis=axis,
+                op_version=op_version, output_names=output_names)
         return OnnxSplit_18(  # noqa
             *x, np.array(split, dtype=np.int64), axis=axis,
             num_outputs=num_outputs, op_version=op_version,

skl2onnx/common/_apply_operation.py

@@ -56,9 +56,17 @@ def apply_normalizer(scope, inputs, outputs, container,
         elif norm == 'L2':
             norm = scope.get_unique_variable_name('norm')
             norm2 = scope.get_unique_variable_name('norm2')
-            container.add_node(
-                'ReduceSumSquare', input, norm, axes=[1], keepdims=1,
-                name=scope.get_unique_operator_name('ReduceSumSquare'))
+            if container.target_opset < 18:
+                container.add_node(
+                    'ReduceSumSquare', input, norm, axes=[1], keepdims=1,
+                    name=scope.get_unique_operator_name('ReduceSumSquare'))
+            else:
+                axis_name = scope.get_unique_variable_name('axis')
+                container.add_initializer(
+                    axis_name, onnx_proto.TensorProto.INT64, [1], [1])
+                container.add_node(
+                    'ReduceSumSquare', [input, axis_name], norm, keepdims=1,
+                    name=scope.get_unique_operator_name('ReduceSumSquare'))
             container.add_node(
                 'Sqrt', [norm], norm2,
                 name=scope.get_unique_operator_name('Sqrt'))