1# Copyright (c) Qualcomm Innovation Center, Inc. 2# All rights reserved 3# 4# This source code is licensed under the BSD-style license found in the 5# LICENSE file in the root directory of this source tree. 6from typing import Dict 7 8import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper 9 10import torch 11 12from .node_visitor import NodeVisitor, register_node_visitor 13from .qnn_constants import OpMatMul, QNN_OP_PACKAGE_NAME_QTI_AISW 14 15 16@register_node_visitor 17class BMM(NodeVisitor): 18 target = ["aten.bmm.default"] 19 20 def __init__(self, *args) -> None: 21 super().__init__(*args) 22 23 def define_node( 24 self, 25 node: torch.fx.Node, 26 nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper], 27 ) -> PyQnnWrapper.PyQnnOpWrapper: 28 bmm_input_tensors = [] 29 for index in range(2): 30 input_node = node.args[index] 31 input_tensor = self.get_tensor(input_node, node) 32 33 input_tensor_wrapper = self.define_tensor( 34 input_node, 35 input_tensor, 36 PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, 37 nodes_to_wrappers, 38 is_input_tensor=True, 39 ) 40 bmm_input_tensors.append(input_tensor_wrapper) 41 42 output_tensor = self.get_tensor(node, node) 43 output_tensor_wrapper = self.define_tensor( 44 node, 45 output_tensor, 46 PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, 47 nodes_to_wrappers, 48 is_input_tensor=False, 49 ) 50 bmm_output_tensors = [output_tensor_wrapper] 51 52 bmm_op = PyQnnWrapper.PyQnnOpWrapper( 53 node.name, QNN_OP_PACKAGE_NAME_QTI_AISW, OpMatMul.op_name 54 ) 55 bmm_op.AddInputTensors(bmm_input_tensors) 56 bmm_op.AddOutputTensors(bmm_output_tensors) 57 58 return bmm_op 59