afe.ir.build_node

Functions for building and initializing AwesomeNodes for specific operators. These functions provide a simpler way to create an instance of a chosen operator and initialize all the data structure fields. These functions are not used for transformations on arbitrary operators.

Functions

create_tuple_output_node(→ afe.ir.node.AwesomeNode)	Creates a Tuple AwesomeNode. Used to accommodate AwesomeNet with multiple outputs.
create_tuple_get_item_nodes(...)	Creates a TupleGetItem node that is used to split the Tuple output node.
create_placeholder_node(→ afe.ir.node.AwesomeNode)	Creates a Placeholder AwesomeNode. Each AwesomeNet must have PlaceholderNodes to
create_quantization_node(→ afe.ir.node.AwesomeNode)	Create a quantization node to quantize from float to integer (int8, int16)
create_dequantization_node(→ afe.ir.node.AwesomeNode)	Create a dequantization node to dequantize from integer (int8, int16, int32) to float.
create_requantization_node(→ afe.ir.node.AwesomeNode)	Create requantization node that will be used for converting data from int32 to int16 or int8 type.
create_cast_node(→ afe.ir.node.AwesomeNode)	Create a node that casts tensors from one scalar type to another.

Module Contents

afe.ir.build_node.create_tuple_output_node(tuple_inputs: List[afe.ir.node.AwesomeNode], prefix: str) → afe.ir.node.AwesomeNode

Creates a Tuple AwesomeNode. Used to accommodate AwesomeNet with multiple outputs.

Parameters:

tuple_inputs – List of AwesomeNodes that serve as input nodes to resulting Tuple AwesomeNode.

Returns:

The created Tuple AwesomeNode.

afe.ir.build_node.create_tuple_get_item_nodes(input_node_name: afe.ir.defines.NodeName, output_types: List[afe.ir.tensor_type.TensorType], prefix: str, input_quant: afe.ir.defines.TupleValue[afe.ir.attributes.QuantResultTensorType] | None = None) → List[afe.ir.node.AwesomeNode]

Creates a TupleGetItem node that is used to split the Tuple output node.

Parameters:

• input_node_name – The name of the input Tuple node.

• output_types – List of types corresponding to the output types of the Tuple node.

• prefix – The prefix string that is prepended to the TupleGetItem node name.

• input_quant – Quantization parameters of the input nodes.

Returns:

The list of TupleGtItem AwesomeNodes.

afe.ir.build_node.create_placeholder_node(node_name: str, input_type: afe.ir.tensor_type.TensorType) → afe.ir.node.AwesomeNode

Creates a Placeholder AwesomeNode. Each AwesomeNet must have PlaceholderNodes to hold the input data.

Parameters:

• node_name – The name of the placeholder node to be created.

• input_type – The input type for the placeholder node.

Returns:

Placeholder node that is created.

afe.ir.build_node.create_quantization_node(input_name: afe.ir.defines.NodeName, name_counter: int, cast: afe.ir.defines.QuantCast, backend: afe.backends.Backend = Backend.EV) → afe.ir.node.AwesomeNode

Create a quantization node to quantize from float to integer (int8, int16)

Parameters:

• input_name – Input node name

• name_counter – Node index

• cast – Cast to perform quantization

• backend – Backend on which node will be executed

Returns:

AwesomeNode

afe.ir.build_node.create_dequantization_node(input_name: afe.ir.defines.NodeName, name_counter: int, cast: afe.ir.defines.DequantCast, backend: afe.backends.Backend = Backend.EV) → afe.ir.node.AwesomeNode

Create a dequantization node to dequantize from integer (int8, int16, int32) to float.

Parameters:

• input_name – Input node name

• name_counter – Node index

• cast – Cast to perform dequantization

• backend – Backend on which node will be executed

Returns:

AwesomeNode

afe.ir.build_node.create_requantization_node(input_name: afe.ir.defines.NodeName, name_counter: int, input_type: afe.ir.tensor_type.TensorType, input_quant: afe.ir.defines.Quantization, output_quant: afe.ir.defines.Quantization, requant_method: afe.ir.defines.RequantMethod, requant: ml_kernels.requantization.BaseRequantization[numpy.ndarray]) → afe.ir.node.AwesomeNode

Create requantization node that will be used for converting data from int32 to int16 or int8 type.

Parameters:

• input_name – Input node name

• name_counter – Node index

• input_type – TensorType of the input tensor

• input_quant – Quantization of input tensor

• output_quant – Quantization of output tensor

• requant_method – Requantization method

• requant – Requantization to perform

Returns:

AwesomeNode

afe.ir.build_node.create_cast_node(input_name: afe.ir.defines.NodeName, name_counter: int, shape: Tuple[int, Ellipsis], input_type: afe.ir.tensor_type.ScalarType, output_type: afe.ir.tensor_type.ScalarType) → afe.ir.node.AwesomeNode

Create a node that casts tensors from one scalar type to another. Casting does not requantize, but only converts the data to a different numeric type.

Parameters:

• input_name – Input of the new cast node

• name_counter – Node index

• shape – Shape of the input and output

• input_type – Scalar type of the input

• output_type – Scalar type of the output

Returns:

AwesomeNode