MTtranslateService/Lib/site-packages/onnxruntime/transformers/fusion_reshape.py

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

from logging import getLogger

import numpy as np
from fusion_base import Fusion
from onnx import TensorProto, helper, numpy_helper
from onnx_model import OnnxModel

logger = getLogger(__name__)


class FusionReshape(Fusion):
    def __init__(self, model: OnnxModel):
        super().__init__(model, "Reshape", "Reshape")
        self.prune_graph: bool = False

    def replace_reshape_node(self, shape, reshape_node, concat_node):
        shape_value = np.asarray(shape, dtype=np.int64)
        constant_shape_name = self.model.create_node_name("Constant", "constant_shape")
        new_node = helper.make_node(
            "Constant",
            inputs=[],
            outputs=[constant_shape_name],
            value=helper.make_tensor(
                name="const_tensor",
                data_type=TensorProto.INT64,
                dims=shape_value.shape,
                vals=bytes(shape_value),
                raw=True,
            ),
        )
        reshape_node.input[1] = constant_shape_name
        reshape_node.name = self.model.create_node_name("Reshape", "Reshape_Fuse")
        self.nodes_to_remove.extend([concat_node])
        self.nodes_to_add.append(new_node)
        self.node_name_to_graph_name[new_node.name] = self.this_graph_name

    def fuse(self, reshape_node, input_name_to_nodes, output_name_to_node):
        if reshape_node.input[1] not in output_name_to_node:
            return

        concat_node = output_name_to_node[reshape_node.input[1]]
        if concat_node.op_type != "Concat" or len(concat_node.input) < 3 or len(concat_node.input) > 4:
            return

        path0 = self.model.match_parent_path(
            concat_node,
            ["Unsqueeze", "Gather", "Shape"],
            [0, 0, 0],
            output_name_to_node,
        )
        if path0 is None:
            return

        (unsqueeze_0, gather_0, shape_0) = path0

        path1 = self.model.match_parent_path(
            concat_node,
            ["Unsqueeze", "Gather", "Shape"],
            [1, 0, 0],
            output_name_to_node,
        )
        if path1 is None:
            return
        (unsqueeze_1, gather_1, shape_1) = path1

        shape = []
        gather_value = self.model.get_constant_value(gather_0.input[1])
        if gather_value == 0:
            shape.append(0)

        gather_value = self.model.get_constant_value(gather_1.input[1])
        if gather_value == 1:
            shape.append(0)

        if len(shape) != 2:
            return

        path2 = []
        path3 = []
        shape_nodes = [shape_0, shape_1]
        if len(concat_node.input) == 3 and self.model.get_initializer(concat_node.input[2]) is None:
            path2 = self.model.match_parent_path(
                concat_node,
                ["Unsqueeze", "Mul", "Gather", "Shape"],
                [2, 0, 0, 0],
                output_name_to_node,
            )
            if path2 is None:
                path2 = self.model.match_parent_path(
                    concat_node,
                    ["Unsqueeze", "Mul", "Squeeze", "Slice", "Shape"],
                    [2, 0, 0, 0, 0],
                    output_name_to_node,
                )  # GPT2 exported by PyTorch 1.4 with opset_version=11
                if path2 is None:
                    return

            path3 = self.model.match_parent_path(
                concat_node,
                ["Unsqueeze", "Mul", "Gather", "Shape"],
                [2, 0, 1, 0],
                output_name_to_node,
            )
            if path3 is None:
                path3 = self.model.match_parent_path(
                    concat_node,
                    ["Unsqueeze", "Mul", "Squeeze", "Slice", "Shape"],
                    [2, 0, 1, 0, 0],
                    output_name_to_node,
                )  # GPT2 exported by PyTorch 1.4 with opset_version=11
                if path3 is None:
                    return

            shape_nodes.extend([path2[-1], path3[-1]])
            shape.append(-1)
        elif len(concat_node.input) > 2:
            concat_value = self.model.get_constant_value(concat_node.input[2])
            if concat_value is None:
                return
            if isinstance(concat_value, np.ndarray):
                shape.extend(concat_value.tolist())
            else:
                shape.append(concat_value)

        if len(concat_node.input) == 4 and self.model.get_constant_value(concat_node.input[3]) is None:
            if -1 in shape:
                return

            path2 = self.model.match_parent_path(
                concat_node,
                ["Unsqueeze", "Div", "Gather", "Shape"],
                [3, 0, 0, 0],
                output_name_to_node,
            )
            if path2 is None:
                path2 = self.model.match_parent_path(
                    concat_node,
                    ["Unsqueeze", "Div", "Squeeze", "Slice", "Shape"],
                    [3, 0, 0, 0, 0],
                    output_name_to_node,
                )  # GPT2 exported by PyTorch 1.4 with opset_version=11
                if path2 is None:
                    return
            shape_nodes.extend([path2[-1]])
            shape.append(-1)
        elif len(concat_node.input) > 3:
            concat_3 = self.model.get_initializer(concat_node.input[3])
            if concat_3 is None:
                return

            concat_value = numpy_helper.to_array(concat_3)
            if isinstance(concat_value, np.ndarray):
                shape.extend(concat_value.tolist())
            else:
                shape.append(concat_value)

        root_input = reshape_node.input[0]
        same_shape_input = True
        for shape_node in shape_nodes:
            if shape_node.input[0] != root_input:
                same_shape_input = False

        if not same_shape_input:
            return

        self.replace_reshape_node(shape, reshape_node, concat_node)

        # TODO(tlwu): Subgraph blocks pruning un-used nodes. Add code to remove un-used nodes safely.
        self.prune_graph = True