# 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 OpPack, QNN_OP_PACKAGE_NAME_QTI_AISW @register_node_visitor class Stack(NodeVisitor): target = ["aten.stack.default"] 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_list = node.args[0] stack_input_tensors = [] for input_node in input_node_list: input_tensor = self.get_tensor(self.get_node(input_node), node) stack_inp_tensor_wrapper = self.define_tensor( input_node, node, input_tensor, PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, nodes_to_wrappers, ) stack_input_tensors.append(stack_inp_tensor_wrapper) output_tensor = self.get_tensor(node, node) output_tensor_wrapper = self.define_tensor( node, node, output_tensor, PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, nodes_to_wrappers, ) stack_output_tensors = [output_tensor_wrapper] dim = 0 if len(node.args) == 1 else cast(int, node.args[1]) if dim < 0: dim = dim % len(output_tensor.shape) if QCOM_AXIS_ORDER in node.meta: dim = node.meta[QCOM_AXIS_ORDER].index(dim) stack_op = PyQnnWrapper.PyQnnOpWrapper( node.name, QNN_OP_PACKAGE_NAME_QTI_AISW, OpPack.op_name, ) stack_op.AddInputTensors(stack_input_tensors) stack_op.AddOutputTensors(stack_output_tensors) stack_op.AddScalarParam( OpPack.param_axis, PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, {QCOM_DATA: np.uint32(dim)}, ) return stack_op