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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.
6
7from typing import Dict
8
9import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper
10
11import numpy as np
12import torch
13
14from .node_visitor import NodeVisitor, register_node_visitor
15from .qnn_constants import OpGroupNorm, QNN_OP_PACKAGE_NAME_QTI_AISW
16from .utils import get_parameter
17
18
19@register_node_visitor
20class GroupNormVisitor(NodeVisitor):
21    target = ["aten.native_group_norm.default"]
22
23    def __init__(self, *args) -> None:
24        super().__init__(*args)
25
26    def define_node(
27        self,
28        node: torch.fx.Node,
29        nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
30    ) -> PyQnnWrapper.PyQnnOpWrapper:
31        input_node = node.args[0]
32        input_tensor = self.get_tensor(input_node, node)
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
41        weight_node = node.args[1]
42        weight_tensor = get_parameter(weight_node, self.edge_program)
43        weight_tensor_wrapper = self.define_tensor(
44            weight_node,
45            weight_tensor,
46            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_STATIC,
47            nodes_to_wrappers,
48            is_input_tensor=False,
49        )
50
51        bias_node = node.args[2]
52        bias_tensor = get_parameter(bias_node, self.edge_program)
53        bias_tensor_wrapper = self.define_tensor(
54            bias_node,
55            bias_tensor,
56            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_STATIC,
57            nodes_to_wrappers,
58            is_input_tensor=False,
59        )
60        group = node.args[6]
61        epsilon = node.args[7]
62
63        output_tensor = self.get_tensor(node, node, 0)
64        output_tensor_wrapper = self.define_tensor(
65            node,
66            output_tensor,
67            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
68            nodes_to_wrappers,
69            is_input_tensor=False,
70        )
71
72        group_norm_op = PyQnnWrapper.PyQnnOpWrapper(
73            node.name,
74            QNN_OP_PACKAGE_NAME_QTI_AISW,
75            OpGroupNorm.op_name,
76        )
77        group_norm_op.AddInputTensors(
78            [input_tensor_wrapper, weight_tensor_wrapper, bias_tensor_wrapper]
79        )
80        group_norm_op.AddOutputTensors([output_tensor_wrapper])
81        group_norm_op.AddScalarParam(
82            OpGroupNorm.param_epsilon,
83            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_FLOAT_32,
84            {"data": np.float32(epsilon)},
85        )
86        group_norm_op.AddScalarParam(
87            OpGroupNorm.param_group,
88            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
89            {"data": np.uint32(group)},
90        )
91
92        return group_norm_op
93