Source code for experimental_experiment.xoptim.patterns_ort.fused_conv

import inspect
from typing import List, Optional
from onnx import NodeProto, TensorProto
from ..patterns_api import MatchResult, PatternOptimization



[docs]
class FusedConvPattern(PatternOptimization):
    """
    Replaces the Conv + Relu into FusedConv.
    """

    def __init__(self, verbose: int = 0, priority: int = 2):
        super().__init__(verbose, priority)


[docs]
    def match(
        self,
        g: "GraphBuilderPatternOptimization",  # noqa: F821
        node: NodeProto,
        matched: List[MatchResult],
    ) -> Optional[MatchResult]:
        if node.op_type != "Conv" or node.domain != "":
            return self.none()

        next_nodes = g.next_nodes(node.output[0])
        if len(next_nodes) != 1:
            return self.none(node, inspect.currentframe().f_lineno)

        op_type = next_nodes[0].op_type
        if op_type != "Relu":
            return self.none(node, inspect.currentframe().f_lineno)

        # FusedConv only exists for float32.
        dtypes = [(g.get_type(i) if g.has_type(i) else None) for i in node.input]
        if TensorProto.FLOAT not in dtypes:
            return self.none(node, inspect.currentframe().f_lineno)

        return MatchResult(self, [node, next_nodes[0]], self.apply, insert_at=next_nodes[0])



[docs]
    def apply(
        self,
        g: "GraphBuilder",  # noqa: F821
        node: NodeProto,
        node_act: NodeProto,
    ) -> List[NodeProto]:
        fc = g.make_node(
            "FusedConv",
            node.input,
            node_act.output,
            domain="com.microsoft",
            activation=node_act.op_type,
            name=f"{self.__class__.__name__}--{node.name}",
        )
        fc.attribute.extend(node.attribute)
        return [fc]