onnxruntime/tools/python/util/qdq_helpers/qdq_model_utils.py

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

import onnx
from ..onnx_model_utils import get_producer_consumer_maps, iterate_graph_per_graph_func


def _duplicate_dq_nodes_with_multiple_consumers(graph: onnx.GraphProto, **kwargs):
    updated_graphs = kwargs['updated_graphs']
    node_to_consumers = kwargs['node_to_consumers']
    validate_updates = kwargs['validate_updates']

    nodes_to_update = []
    for node in filter(lambda node: node.op_type == 'DequantizeLinear', graph.node):
        # node providing graph output won't have consumer nodes
        consumers = node_to_consumers[node] if node in node_to_consumers else []
        if len(consumers) > 1:
            if not all(consumer in graph.node for consumer in consumers):
                # TODO: If this does ever occur, as long as it's only consumed in one subgraph we could leave that
                # value as is (no need to handle recursing into the subgraph) and update the consumers in this
                # graph only
                raise IndexError("DequantizeLinear node output is consumed by a subgraph. "
                                 "This is not currently supported.")

            nodes_to_update.append(node)

    if validate_updates:
        if nodes_to_update:
            # internal error. we somehow missed an update in the first pass when validate_upates was false
            raise ValueError('Graph still has DequantizeLinear nodes with multiple consumers.')

        return

    if nodes_to_update:
        dup_idx = 0
        new_graph = onnx.GraphProto()
        graph_outputs = set([output.name for output in graph.output])
        for node in graph.node:
            new_graph.node.append(node)
            if node in nodes_to_update:
                is_graph_output = node.output[0] in graph_outputs
                # create duplicate DQ nodes as needed so that there is one consumer per node.
                # this allows us to cleanly create a QDQ node group with no DQ nodes shared with other QDQ node groups.
                # if the node produces a graph output we need a duplicate DQ node for every consumer node.
                # if not, we can leave the first consumer as is and create duplicate nodes for the other consumers.
                start_idx = 0 if is_graph_output else 1
                consumers = list(node_to_consumers[node])[start_idx:]

                for idx, consumer in enumerate(consumers):
                    # create duplicate DQ node
                    duplicate = onnx.NodeProto()
                    duplicate.CopyFrom(node)
                    # update node name for debugging. use the global dup idx for node duplication
                    duplicate.name += f'/qdq_utils_dup_{dup_idx}'

                    # update output. use the local idx for value duplication
                    orig_output = node.output[0]
                    new_output = f'{orig_output}/qdq_utils_dup_{idx}'
                    duplicate.output[0] = new_output

                    # update input on the consumer node.
                    for input_idx, input_name in enumerate(consumer.input):
                        if input_name == orig_output:
                            consumer.input[input_idx] = new_output

                    new_graph.node.append(duplicate)
                    dup_idx += 1

        # replace nodes
        del graph.node[:]
        graph.node.extend(new_graph.node)
        updated_graphs.append(graph)


def fix_dq_nodes_with_multiple_consumers(model):
    '''
    Update a model if any DequantizeLinear nodes have multiple consumers.
    The QDQ node unit processing is overly complicated if this is the case, as the DQ node would be in multiple units,
    and the units may end up in different partitions at runtime.
    :param model: QDQ model to update
    '''
    node_to_producers, node_to_consumers = get_producer_consumer_maps(model.graph)

    updated_graphs = []  # list of GraphProto instances that were updated_graphs
    iterate_graph_per_graph_func(model.graph, _duplicate_dq_nodes_with_multiple_consumers,
                                 node_to_consumers=node_to_consumers, validate_updates=False,
                                 updated_graphs=updated_graphs)

    if updated_graphs:
        updated_graphs = []
        node_to_producers, node_to_consumers = get_producer_consumer_maps(model.graph)
        iterate_graph_per_graph_func(model.graph, _duplicate_dq_nodes_with_multiple_consumers,
                                     node_to_consumers=node_to_consumers, validate_updates=True,
                                     updated_graphs=updated_graphs)

        # validate with check and by running shape inference.
        onnx.checker.check_model(model)
        _ = onnx.shape_inference.infer_shapes(model)