nir.ir.graph#
Module Contents#
Classes#
Neural Intermediate Representation (NIR) Graph containing a number of nodes and |
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Input Node. |
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Output Node. |
- class nir.ir.graph.NIRGraph#
Bases:
nir.ir.node.NIRNodeNeural Intermediate Representation (NIR) Graph containing a number of nodes and edges.
A graph of computational nodes and identity edges.
- property inputs#
- property outputs#
- nodes: nir.ir.typing.Nodes#
- edges: nir.ir.typing.Edges#
- input_type: Optional[Dict[str, numpy.ndarray]]#
- output_type: Optional[Dict[str, numpy.ndarray]]#
- metadata: Dict[str, Any]#
- static from_list(*nodes: nir.ir.node.NIRNode) NIRGraph#
Create a sequential graph from a list of nodes by labelling them after indices.
- Parameters
nodes (nir.ir.node.NIRNode) –
- Return type
- __post_init__()#
- to_dict() Dict[str, Any]#
Serialize into a dictionary.
- Return type
Dict[str, Any]
- classmethod from_dict(node: Dict[str, Any]) nir.ir.node.NIRNode#
- Parameters
node (Dict[str, Any]) –
- Return type
- infer_types()#
Infer the shapes of all nodes in this graph. Will modify the input_type and output_type of all nodes in the graph.
Assumes that either the input type or the output type of the graph is set. Assumes that if A->B, then A.output_type.values() = B.input_type.values()
- class nir.ir.graph.Input#
Bases:
nir.ir.node.NIRNodeInput Node.
This is a virtual node, which allows feeding in data into the graph.
- input_type: nir.ir.typing.Types#
- __post_init__()#
- to_dict() Dict[str, Any]#
Serialize into a dictionary.
- Return type
Dict[str, Any]
- classmethod from_dict(node: Dict[str, Any]) nir.ir.node.NIRNode#
- Parameters
node (Dict[str, Any]) –
- Return type
- class nir.ir.graph.Output#
Bases:
nir.ir.node.NIRNodeOutput Node.
Defines an output of the graph.
- output_type: nir.ir.typing.Types#
- __post_init__()#
- to_dict() Dict[str, Any]#
Serialize into a dictionary.
- Return type
Dict[str, Any]
- classmethod from_dict(node: Dict[str, Any]) nir.ir.node.NIRNode#
- Parameters
node (Dict[str, Any]) –
- Return type