Support for QCDQ to QOp conversion

src/qonnx/custom_op/qop/averagepool_op.py

@@ -0,0 +1,48 @@
+########################################################################
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#########################################################################
+
+import onnx
+
+class AveragePool:
+
+    def __init__(self, node):
+
+        average_pool_node = node
+        a_name = average_pool_node.inputs[0].name
+
+        y_name = average_pool_node.outputs[0].name
+
+        new_average_pool_node = onnx.helper.make_node(name = average_pool_node.name, op_type = "AveragePool",
+                                                        inputs = [a_name],
+                                                        outputs = [y_name],
+                                                        ceil_mode = average_pool_node.attrs["ceil_mode"],
+                                                        kernel_shape = average_pool_node.attrs["kernel_shape"],
+                                                        pads = average_pool_node.attrs["pads"],
+                                                        strides = average_pool_node.attrs["strides"])
+
+        self.node = new_average_pool_node
+
+    def get_node(self):
+        return self.node

src/qonnx/custom_op/qop/cast_op.py

@@ -0,0 +1,44 @@
+########################################################################
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#########################################################################
+
+import onnx
+
+class Cast:
+
+    def __init__(self, node):
+
+        cast_node = node
+
+        x_name = cast_node.inputs[0].name
+        y_name = cast_node.outputs[0].name
+
+        new_cast_node = onnx.helper.make_node(name = cast_node.name, op_type = "Cast",
+                                            inputs = [x_name],
+                                            outputs = [y_name],
+                                            to = cast_node.attrs["to"])
+        self.node = new_cast_node
+
+    def get_node(self):
+        return self.node

src/qonnx/custom_op/qop/clip_op.py

@@ -0,0 +1,61 @@
+########################################################################
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#########################################################################
+
+import onnx
+from .helper import helper
+
+class Clip:
+
+    def __init__(self, node):
+
+        clip_node = node
+
+        x_name = clip_node.inputs[0].name
+
+        x2_name = clip_node.inputs[1].name
+        x2_value = clip_node.inputs[1].values
+        x2_tensor = helper.create_initializer_tensor(x2_name,x2_value,onnx.TensorProto.INT8)
+
+        x3_name = clip_node.inputs[2].name
+        x3_value = clip_node.inputs[2].values
+        x3_tensor = helper.create_initializer_tensor(x3_name,x3_value,onnx.TensorProto.INT8)
+
+        new_clip_node = onnx.helper.make_node(name = clip_node.name, op_type = "Clip",
+                                                inputs= [x_name, x2_name, x3_name],
+                                                outputs = [clip_node.outputs[0].name])
+
+        self.node = new_clip_node
+
+        intializer_list = []
+        intializer_list.append(x2_tensor)
+        intializer_list.append(x3_tensor)
+        self.intializer_list = intializer_list
+
+    def get_node(self):
+        return self.node
+
+    def get_intializers(self):
+        return self.intializer_list
+

src/qonnx/custom_op/qop/concat_op.py

@@ -0,0 +1,78 @@
+########################################################################
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#########################################################################
+
+import onnx
+from .helper import helper
+
+class Concat:
+
+    def __init__(self, node):
+
+        concat_node = node
+
+        number_of_inputs = len(concat_node.inputs)
+
+        zp_value_list = []
+        zp_name_list = []
+        scale_values_list = []
+        scale_name_list = []
+        input_tensor_names = []
+
+        intializer_list = []
+        input_names = []
+
+        for i in range(number_of_inputs):
+            input_tensor_names.append(concat_node.inputs[i].name)
+            if len(concat_node.inputs[i].inputs) == 0:
+                 c_input = helper.create_initializer_tensor(name=concat_node.inputs[i].name,
+                                                            tensor_array=concat_node.inputs[i].values,
+                                                            data_type=onnx.TensorProto.INT64)
+                 intializer_list.append(c_input)
+                 self.intializer_list = intializer_list
+
+        y_name = concat_node.outputs[0].name
+
+        for i in range(number_of_inputs):
+            input_names.append(input_tensor_names[i])
+            if len(scale_name_list)>0 and len(zp_name_list)>0:
+                input_names.append(scale_name_list[i])
+                input_names.append(zp_name_list[i])
+
+        kwargs = {}
+        kwargs["domain"] = 'com.microsoft'
+
+        new_concat_node = onnx.helper.make_node(name = concat_node.name,
+                                                    op_type = "Concat",
+                                                    inputs = input_names,
+                                                    outputs = [y_name],
+                                                    axis = concat_node.attrs["axis"])
+
+        self.node = new_concat_node
+
+    def get_node(self):
+        return self.node
+
+    def get_intializers(self):
+        return self.intializer_list

src/qonnx/custom_op/qop/dequantizelinear_op.py

@@ -0,0 +1,105 @@
+########################################################################
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#########################################################################
+
+import onnx
+from .helper import helper
+import numpy as np
+
+class DequantizeLinear:
+
+    def __init__(self, node, remove_relu):
+
+        dql_node = node
+
+        x_name = dql_node.inputs[0].name
+
+        if helper.is_parent_exist(dql_node, 0, 0):
+            if dql_node.i().op == "QuantizeLinear":
+                ql_node = dql_node.i()
+                if helper.is_parent_exist(ql_node,0, 0):
+                    if ql_node.i().op == "Relu":
+                        relu_node = ql_node.i()
+                        if remove_relu:
+                            x_name = ql_node.outputs[0].name
+                        else:
+                            x_name = relu_node.outputs[0].name
+                else:
+                    print("*************** WARNING *********************** Please check parent of QL node", ql_node.name, " ignore if pattern is correct")
+        else:
+            print("*************** WARNING *********************** Please check parent of DQL node", dql_node.name, " ignore if pattern is correct")
+        self.initializers = []
+
+        if len(dql_node.inputs[0].inputs) == 0:
+            if dql_node.inputs[0].dtype == np.uint8:
+                input_tensor = helper.create_initializer_tensor(name= dql_node.inputs[0].name,
+                                                                tensor_array=dql_node.inputs[0].values,
+                                                                data_type=onnx.TensorProto.UINT8)
+            elif dql_node.inputs[0].dtype == np.int8:
+                input_tensor = helper.create_initializer_tensor(name= dql_node.inputs[0].name,
+                                                                tensor_array=dql_node.inputs[0].values,
+                                                                data_type=onnx.TensorProto.INT8)
+            elif dql_node.inputs[0].dtype == np.int32:
+                input_tensor = helper.create_initializer_tensor(name= dql_node.inputs[0].name,
+                                                                tensor_array=dql_node.inputs[0].values,
+                                                                data_type=onnx.TensorProto.INT32)
+            self.initializers.append(input_tensor)
+
+        x_scale_name = dql_node.inputs[1].name
+        x_scale_value = dql_node.inputs[1].values
+        x_scale_tensor = helper.create_initializer_tensor(name=x_scale_name,tensor_array=x_scale_value,data_type=onnx.TensorProto.FLOAT)
+
+        x_zp_name = dql_node.inputs[2].name
+        x_zp_value = dql_node.inputs[2].values
+
+        if dql_node.inputs[2].dtype == np.uint8:
+            x_zp_tensor = helper.create_initializer_tensor(name=x_zp_name,
+                                                        tensor_array=x_zp_value,
+                                                        data_type=onnx.TensorProto.UINT8)
+        if dql_node.inputs[2].dtype == np.int32:
+            x_zp_tensor = helper.create_initializer_tensor(name=x_zp_name,
+                                                       tensor_array=x_zp_value,
+                                                       data_type=onnx.TensorProto.INT32)
+        elif dql_node.inputs[2].dtype == np.int8:
+            x_zp_tensor = helper.create_initializer_tensor(name=x_zp_name,
+                                                        tensor_array=x_zp_value,
+                                                        data_type=onnx.TensorProto.INT8)
+
+        y_name = dql_node.outputs[0].name
+
+        dequantizelinear_node = onnx.helper.make_node(name = dql_node.name,
+                                                    op_type = "DequantizeLinear",
+                                                    inputs = [x_name, x_scale_name, x_zp_name],
+                                                    outputs = [y_name])
+
+        self.node = dequantizelinear_node
+
+        self.initializers.append(x_scale_tensor)
+        self.initializers.append(x_zp_tensor)
+
+    def get_node(self):
+        return self.node
+
+    def get_intializers(self):
+        return self.initializers

src/qonnx/custom_op/qop/flatten_op.py

@@ -0,0 +1,53 @@
+########################################################################
+#
+# Copyright (c) 2023 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#########################################################################
+
+import onnx
+
+class Flatten:
+
+    def __init__(self, node):
+
+        flatten_node = node
+        x_name = flatten_node.inputs[0].name
+        y_name = flatten_node.outputs[0].name
+
+        if flatten_node.i().op == "DequantizeLinear":
+            node1 = flatten_node.i()
+            x_name = node1.inputs[0].name
+
+        if flatten_node.o().op == "QuantizeLinear":
+            node2 = flatten_node.o()
+            y_name = node2.outputs[0].name
+
+
+        new_flatten_node = onnx.helper.make_node(name = flatten_node.name, op_type = "Flatten",
+                                                        inputs = [x_name],
+                                                        outputs = [y_name])
+
+
+        self.node = new_flatten_node
+
+    def get_node(self):
+        return self.node