xref: /aosp_15_r20/external/executorch/backends/qualcomm/builders/op_select_copy.py (revision 523fa7a60841cd1ecfb9cc4201f1ca8b03ed023a)
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.
6import math
7from typing import cast, Dict
8
9import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper
10
11import numpy as np
12import torch
13from executorch.backends.qualcomm.utils.constants import QCOM_DATA
14
15from .node_visitor import NodeVisitor, register_node_visitor
16from .qnn_constants import OpStridedSlice, QNN_OP_PACKAGE_NAME_QTI_AISW
17
18
19@register_node_visitor
20class SelectCopy(NodeVisitor):
21    target = ["aten.select_copy.int", "aten.select.int"]
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        output_tensor = self.get_tensor(node, node)
42        output_tensor_wrapper = self.define_tensor(
43            node,
44            output_tensor,
45            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
46            nodes_to_wrappers,
47            is_input_tensor=False,
48        )
49
50        dim = cast(int, node.args[1])
51        if dim < 0:
52            dim = dim % len(input_tensor.shape)
53        index = cast(int, node.args[2]) % input_tensor.shape[dim]
54
55        input_tensor_rank = len(input_tensor.shape)
56        ranges = []
57        for i in range(input_tensor_rank):
58            if i == dim:
59                ranges.extend([index, index, 1])
60            else:
61                ranges.extend([0, input_tensor.shape[i], 1])
62
63        range_shape = [input_tensor_rank, 3]
64
65        stride_slice_op = PyQnnWrapper.PyQnnOpWrapper(
66            node.name,
67            QNN_OP_PACKAGE_NAME_QTI_AISW,
68            OpStridedSlice.op_name,
69        )
70        stride_slice_op.AddInputTensors([input_tensor_wrapper])
71        stride_slice_op.AddOutputTensors([output_tensor_wrapper])
72
73        stride_slice_op.AddTensorParam(
74            OpStridedSlice.param_ranges,
75            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_INT_32,
76            len(range_shape),
77            range_shape,
78            np.array(ranges, dtype=np.int32),
79            True,
80        )
81
82        stride_slice_op.AddScalarParam(
83            OpStridedSlice.param_shrink_axes,
84            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
85            {QCOM_DATA: np.uint32(math.pow(2, dim))},
86        )
87
88        return stride_slice_op
89