# Copyright (c) Qualcomm Innovation Center, Inc. # All rights reserved # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. from typing import cast, Dict import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper import numpy as np import torch from executorch.backends.qualcomm.utils.constants import QCOM_AXIS_ORDER, QCOM_DATA from .node_visitor import NodeVisitor, register_node_visitor from .qnn_constants import OpUnpack, QNN_OP_PACKAGE_NAME_QTI_AISW @register_node_visitor class Unbind(NodeVisitor): target = ["aten.unbind.int"] def __init__(self, *args) -> None: super().__init__(*args) def define_node( self, node: torch.fx.Node, nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper], ) -> PyQnnWrapper.PyQnnOpWrapper: input_node = self.get_node(node.args[0]) input_tensor = self.get_tensor(input_node, node) input_tensor_wrapper = self.define_tensor( input_node, node, input_tensor, PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_STATIC, nodes_to_wrappers, ) unbind_input_tensors = [input_tensor_wrapper] unbind_output_tensors = [] for i in range(len(node.meta["val"])): output_tensor = self.get_tensor(node, node, i) output_tensor_wrapper = self.define_tensor( node, node, output_tensor, PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, nodes_to_wrappers, wrapper_idx=i, ) unbind_output_tensors.append(output_tensor_wrapper) dim = 0 if len(node.args) == 1 else cast(int, node.args[1]) if dim < 0: dim = dim % len(input_tensor.shape) if QCOM_AXIS_ORDER in node.meta: dim = node.meta[QCOM_AXIS_ORDER].index(dim) unbind_op = PyQnnWrapper.PyQnnOpWrapper( node.name, QNN_OP_PACKAGE_NAME_QTI_AISW, OpUnpack.op_name, ) unbind_op.AddInputTensors(unbind_input_tensors) unbind_op.AddOutputTensors(unbind_output_tensors) unbind_op.AddScalarParam( OpUnpack.param_axis, PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, {QCOM_DATA: np.uint32(dim)}, ) return unbind_op