Enable tensorflow 2.8.0 in CI pipeline.

ci_build/azure_pipelines/onnxruntime_nightly_test.yml

@@ -68,7 +68,7 @@ stages:
       parameters:
         platforms: ['linux']
         python_versions: ['3.9']
-        tf_versions: ['2.5.0']
+        tf_versions: ['2.8.0']
         onnx_opsets: ['']
         onnx_backends: {onnxruntime: ['nightly']}
         job:

ci_build/azure_pipelines/pretrained_model_test-matrix.yml

@@ -28,20 +28,29 @@ jobs:
       steps:
       - template: 'pretrained_model_test.yml'
 
+- template: 'templates/job_generator.yml'
+  parameters:
+    platforms: ['linux', 'windows']
+    python_versions: ['3.9']
+    tf_versions: ['2.6.2']
+    job:
+      steps:
+      - template: 'pretrained_model_test.yml'
+
 - template: 'templates/job_generator.yml'
   parameters:
     platforms: ['linux', 'windows']
     python_versions: ['3.8']
-    tf_versions: ['2.5.0']
+    tf_versions: ['2.7.0']
     job:
       steps:
       - template: 'pretrained_model_test.yml'
 
 - template: 'templates/job_generator.yml'
   parameters:
     platforms: ['linux', 'windows']
-    python_versions: ['3.9']
-    tf_versions: ['2.6.2']
+    python_versions: ['3.8']
+    tf_versions: ['2.8.0']
     job:
       steps:
       - template: 'pretrained_model_test.yml'

ci_build/azure_pipelines/pretrained_model_test.yml

@@ -15,18 +15,18 @@ jobs:
 
 - template: 'templates/job_generator.yml'
   parameters:
-    # 2.6, tf
+    # 2.7, tf
     python_versions: ['3.7']
-    tf_versions: ['1.15.5','2.6.2']
+    tf_versions: ['1.15.5','2.7.0']
     job:
       steps:
       - template: 'pretrained_model_test.yml'
 
 - template: 'templates/job_generator.yml'
   parameters:
-    # 2.7, tf
+    # 2.8, tf
     python_versions: ['3.9']
-    tf_versions: ['2.7.0']
+    tf_versions: ['2.8.0']
     job:
       steps:
       - template: 'pretrained_model_test.yml'

ci_build/azure_pipelines/templates/keras2onnx_application_tests.yml

@@ -20,7 +20,6 @@ steps:
       conda config --set always_yes yes --set changeps1 no
       pip install $(ONNX_PATH)
       pip install h5py==2.9.0
-      pip install numpy==1.19
       pip install parameterized
       $(INSTALL_TENSORFLOW)
       $(INSTALL_KERAS)
@@ -82,7 +81,6 @@ steps:
       pip uninstall -y protobuf
       pip install protobuf
       pip install h5py==2.9.0
-      pip install numpy==1.19
       pip install parameterized
       %INSTALL_TENSORFLOW%
       %INSTALL_KERAS%

ci_build/azure_pipelines/templates/keras2onnx_unit_test.yml

@@ -20,7 +20,6 @@ steps:
       conda config --set always_yes yes --set changeps1 no
       pip install $(ONNX_PATH)
       pip install h5py==2.9.0
-      pip install numpy==1.19
       pip install parameterized
       pip install $(TENSORFLOW_PATH)
       if [[ ! -z $KERAS ]];

ci_build/azure_pipelines/templates/setup.yml

@@ -49,6 +49,10 @@ steps:
       then
         pip install "tensorflow-text>=2.7,<2.8"
       fi
+      if [[ $CI_TF_VERSION == 2.8* ]] ;
+      then
+        pip install "tensorflow-text>=2.8,<2.9"
+      fi
     fi
 
     python setup.py install

ci_build/azure_pipelines/trimmed_keras2onnx_unit_test.yml

@@ -54,7 +54,7 @@ jobs:
         TENSORFLOW_PATH: tensorflow==1.15.0
         INSTALL_ORT: pip install onnxruntime==1.9.0
 
-      Python37-tf2.3:
+      Python37-tf2.8:
         python.version: '3.7'
         ONNX_PATH: onnx==1.10.2
         TENSORFLOW_PATH: tensorflow-cpu==2.3.0

ci_build/azure_pipelines/unit_test-matrix.yml

@@ -58,4 +58,15 @@ stages:
           - template: 'unit_test.yml'
         report_coverage: 'True'
 
+    - template: 'templates/job_generator.yml'
+      parameters:
+        platforms: ['linux', 'windows']
+        python_versions: ['3.8']
+        tf_versions: ['2.8.0']
+        onnx_opsets: ['']
+        job:
+          steps:
+          - template: 'unit_test.yml'
+        report_coverage: 'True'
+
   - template: 'templates/combine_test_coverage.yml'

ci_build/azure_pipelines/unit_test.yml

@@ -3,6 +3,43 @@
 stages:
   - stage:
     jobs:
+    - template: 'templates/job_generator.yml'
+      parameters:
+        # TFJS tf 2.8
+        python_versions: ['3.9']
+        tf_versions: ['2.8.0']
+        onnx_opsets: ['']
+        skip_tfjs_tests: 'False'
+        skip_tf_tests: 'True'
+        job:
+          steps:
+          - template: 'unit_test.yml'
+        report_coverage: 'True'
+
+    - template: 'templates/job_generator.yml'
+      parameters:
+        # TFLite tf 2.8
+        python_versions: ['3.8']
+        tf_versions: ['2.8.0']
+        onnx_opsets: ['']
+        skip_tflite_tests: 'False'
+        skip_tf_tests: 'True'
+        job:
+          steps:
+          - template: 'unit_test.yml'
+        report_coverage: 'True'
+
+    - template: 'templates/job_generator.yml'
+      parameters:
+        # tf 2.8
+        python_versions: ['3.8']
+        tf_versions: ['2.8.0']
+        onnx_opsets: ['']
+        job:
+          steps:
+          - template: 'unit_test.yml'
+        report_coverage: 'True'
+
     - template: 'templates/job_generator.yml'
       parameters:
         # TFJS tf 2.7

tests/test_backend.py

@@ -305,7 +305,7 @@ def func(x):
     def test_multinomial(self):
         x_val = np.array([[10., 10.]], dtype=np.float32)
         def func(x):
-            op = multinomial(tf.math.log(x), 5, output_dtype=tf.int64)
+            op = multinomial(tf.math.log(x), 5, output_dtype=tf.int32)
             return tf.identity(op, name=_TFOUTPUT)
 
         # since returned indexes are random we can only check type and shape
@@ -318,7 +318,7 @@ def test_multinomial1(self):
         shape = [2, 10]
         x_val = np.ones(np.prod(shape)).astype("float32").reshape(shape)
         def func(x):
-            op = multinomial(x, 2, output_dtype=tf.int64)
+            op = multinomial(x, 2, output_dtype=tf.int32)
             return tf.identity(op, name=_TFOUTPUT)
         # since returned indexes are random we can only check type and shape
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False,

tf2onnx/tflite/BucketizeOptions.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# automatically generated by the FlatBuffers compiler, do not modify
+
+# namespace: tflite
+
+import flatbuffers
+from flatbuffers.compat import import_numpy
+np = import_numpy()
+
+class BucketizeOptions(object):
+    __slots__ = ['_tab']
+
+    @classmethod
+    def GetRootAs(cls, buf, offset=0):
+        n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+        x = BucketizeOptions()
+        x.Init(buf, n + offset)
+        return x
+
+    @classmethod
+    def GetRootAsBucketizeOptions(cls, buf, offset=0):
+        """This method is deprecated. Please switch to GetRootAs."""
+        return cls.GetRootAs(buf, offset)
+    @classmethod
+    def BucketizeOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+        return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+    # BucketizeOptions
+    def Init(self, buf, pos):
+        self._tab = flatbuffers.table.Table(buf, pos)
+
+    # BucketizeOptions
+    def Boundaries(self, j):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+        if o != 0:
+            a = self._tab.Vector(o)
+            return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
+        return 0
+
+    # BucketizeOptions
+    def BoundariesAsNumpy(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+        if o != 0:
+            return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o)
+        return 0
+
+    # BucketizeOptions
+    def BoundariesLength(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+        if o != 0:
+            return self._tab.VectorLen(o)
+        return 0
+
+    # BucketizeOptions
+    def BoundariesIsNone(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+        return o == 0
+
+def Start(builder): builder.StartObject(1)
+def BucketizeOptionsStart(builder):
+    """This method is deprecated. Please switch to Start."""
+    return Start(builder)
+def AddBoundaries(builder, boundaries): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(boundaries), 0)
+def BucketizeOptionsAddBoundaries(builder, boundaries):
+    """This method is deprecated. Please switch to AddBoundaries."""
+    return AddBoundaries(builder, boundaries)
+def StartBoundariesVector(builder, numElems): return builder.StartVector(4, numElems, 4)
+def BucketizeOptionsStartBoundariesVector(builder, numElems):
+    """This method is deprecated. Please switch to Start."""
+    return StartBoundariesVector(builder, numElems)
+def End(builder): return builder.EndObject()
+def BucketizeOptionsEnd(builder):
+    """This method is deprecated. Please switch to End."""
+    return End(builder)

tf2onnx/tflite/BuiltinOperator.py

@@ -151,4 +151,10 @@ class BuiltinOperator(object):
     READ_VARIABLE = 143
     ASSIGN_VARIABLE = 144
     BROADCAST_ARGS = 145
+    RANDOM_STANDARD_NORMAL = 146
+    BUCKETIZE = 147
+    RANDOM_UNIFORM = 148
+    MULTINOMIAL = 149
+    GELU = 150
+    DYNAMIC_UPDATE_SLICE = 151
 

tf2onnx/tflite/BuiltinOptions.py

@@ -119,4 +119,8 @@ class BuiltinOptions(object):
     VarHandleOptions = 111
     ReadVariableOptions = 112
     AssignVariableOptions = 113
+    RandomOptions = 114
+    BucketizeOptions = 115
+    GeluOptions = 116
+    DynamicUpdateSliceOptions = 117
 

tf2onnx/tflite/DynamicUpdateSliceOptions.py

@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# automatically generated by the FlatBuffers compiler, do not modify
+
+# namespace: tflite
+
+import flatbuffers
+from flatbuffers.compat import import_numpy
+np = import_numpy()
+
+class DynamicUpdateSliceOptions(object):
+    __slots__ = ['_tab']
+
+    @classmethod
+    def GetRootAs(cls, buf, offset=0):
+        n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+        x = DynamicUpdateSliceOptions()
+        x.Init(buf, n + offset)
+        return x
+
+    @classmethod
+    def GetRootAsDynamicUpdateSliceOptions(cls, buf, offset=0):
+        """This method is deprecated. Please switch to GetRootAs."""
+        return cls.GetRootAs(buf, offset)
+    @classmethod
+    def DynamicUpdateSliceOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+        return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+    # DynamicUpdateSliceOptions
+    def Init(self, buf, pos):
+        self._tab = flatbuffers.table.Table(buf, pos)
+
+def Start(builder): builder.StartObject(0)
+def DynamicUpdateSliceOptionsStart(builder):
+    """This method is deprecated. Please switch to Start."""
+    return Start(builder)
+def End(builder): return builder.EndObject()
+def DynamicUpdateSliceOptionsEnd(builder):
+    """This method is deprecated. Please switch to End."""
+    return End(builder)

tf2onnx/tflite/GeluOptions.py

@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# automatically generated by the FlatBuffers compiler, do not modify
+
+# namespace: tflite
+
+import flatbuffers
+from flatbuffers.compat import import_numpy
+np = import_numpy()
+
+class GeluOptions(object):
+    __slots__ = ['_tab']
+
+    @classmethod
+    def GetRootAs(cls, buf, offset=0):
+        n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
+        x = GeluOptions()
+        x.Init(buf, n + offset)
+        return x
+
+    @classmethod
+    def GetRootAsGeluOptions(cls, buf, offset=0):
+        """This method is deprecated. Please switch to GetRootAs."""
+        return cls.GetRootAs(buf, offset)
+    @classmethod
+    def GeluOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
+        return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
+
+    # GeluOptions
+    def Init(self, buf, pos):
+        self._tab = flatbuffers.table.Table(buf, pos)
+
+    # GeluOptions
+    def Approximate(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
+        if o != 0:
+            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+        return False
+
+def Start(builder): builder.StartObject(1)
+def GeluOptionsStart(builder):
+    """This method is deprecated. Please switch to Start."""
+    return Start(builder)
+def AddApproximate(builder, approximate): builder.PrependBoolSlot(0, approximate, 0)
+def GeluOptionsAddApproximate(builder, approximate):
+    """This method is deprecated. Please switch to AddApproximate."""
+    return AddApproximate(builder, approximate)
+def End(builder): return builder.EndObject()
+def GeluOptionsEnd(builder):
+    """This method is deprecated. Please switch to End."""
+    return End(builder)