|
| 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 | +import warnings |
| 7 | +from typing import cast, Dict, List |
| 8 | + |
| 9 | +import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper |
| 10 | +import numpy as np |
| 11 | + |
| 12 | +import torch |
| 13 | +from executorch.backends.qualcomm.utils.constants import QCOM_DATA |
| 14 | + |
| 15 | +from .node_visitor import NodeVisitor |
| 16 | +from .node_visitor_manager import register_node_visitor |
| 17 | +from .qnn_constants import OpPoolMax2d, QNN_OP_PACKAGE_NAME_QTI_AISW |
| 18 | + |
| 19 | + |
| 20 | +@register_node_visitor |
| 21 | +class AdaptiveMaxPool2D(NodeVisitor): |
| 22 | + target = ["aten.adaptive_max_pool2d.default"] |
| 23 | + |
| 24 | + def __init__(self, *args) -> None: |
| 25 | + super().__init__(*args) |
| 26 | + |
| 27 | + def define_node( |
| 28 | + self, |
| 29 | + node: torch.fx.Node, |
| 30 | + nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper], |
| 31 | + ) -> PyQnnWrapper.PyQnnOpWrapper: |
| 32 | + input_node = self.get_node(node.args[0]) |
| 33 | + input_tensor = self.get_tensor(input_node, node) |
| 34 | + input_tensor_wrapper = self.define_tensor( |
| 35 | + input_node, |
| 36 | + node, |
| 37 | + input_tensor, |
| 38 | + PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, |
| 39 | + nodes_to_wrappers, |
| 40 | + ) |
| 41 | + users = list(node.users.keys()) |
| 42 | + for user in users: |
| 43 | + if user.target.__name__ == "getitem": |
| 44 | + getitem_index = user.args[1] |
| 45 | + if getitem_index != 0: |
| 46 | + warnings.warn( |
| 47 | + f"[QNN Delegate Op Builder]: Expected second argument of getitem node for {node.target.__name__ } to be 0, got {getitem_index}", |
| 48 | + stacklevel=1, |
| 49 | + ) |
| 50 | + return |
| 51 | + |
| 52 | + if len(node.args) > 2: |
| 53 | + warnings.warn( |
| 54 | + "[QNN Delegate Op Builder]: The return_indices is not supported, fallback op", |
| 55 | + stacklevel=1, |
| 56 | + ) |
| 57 | + return |
| 58 | + |
| 59 | + input_height = input_tensor.shape[1] |
| 60 | + input_width = input_tensor.shape[2] |
| 61 | + # output cases |
| 62 | + out_wh = cast(List[int], node.args[1]) |
| 63 | + if len(out_wh) == 1: |
| 64 | + output_height = node.args[1][0] |
| 65 | + output_width = node.args[1][0] |
| 66 | + else: |
| 67 | + output_height = node.args[1][0] |
| 68 | + output_width = node.args[1][1] |
| 69 | + if output_height is None: |
| 70 | + output_height = input_height |
| 71 | + if output_width is None: |
| 72 | + output_width = input_width |
| 73 | + # NOTE: Here we need not to emphasize on mode, cuz the output shape is decided by user. |
| 74 | + mode = OpPoolMax2d.RoundingMode.FLOOR |
| 75 | + |
| 76 | + # floor division |
| 77 | + stride_height = input_height // output_height |
| 78 | + filter_height = input_height - (output_height - 1) * stride_height |
| 79 | + stride_width = input_width // output_width |
| 80 | + filter_width = input_width - (output_width - 1) * stride_width |
| 81 | + |
| 82 | + filter = [filter_height, filter_width] |
| 83 | + filter_shape = [len(filter)] |
| 84 | + |
| 85 | + stride = [stride_height, stride_width] |
| 86 | + stride_shape = [len(stride)] |
| 87 | + |
| 88 | + padding = [0, 0] |
| 89 | + padding_shape = [len(padding), len(padding)] |
| 90 | + |
| 91 | + out_tensor = self.get_tensor(node, node, 0) |
| 92 | + output_tensor_wrapper = self.define_tensor( |
| 93 | + node, |
| 94 | + node, |
| 95 | + out_tensor, |
| 96 | + PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, |
| 97 | + nodes_to_wrappers, |
| 98 | + ) |
| 99 | + |
| 100 | + adaptive_max_pool2d_op = PyQnnWrapper.PyQnnOpWrapper( |
| 101 | + node.name, |
| 102 | + QNN_OP_PACKAGE_NAME_QTI_AISW, |
| 103 | + OpPoolMax2d.op_name, |
| 104 | + ) |
| 105 | + |
| 106 | + adaptive_max_pool2d_op.AddInputTensors([input_tensor_wrapper]) |
| 107 | + adaptive_max_pool2d_op.AddOutputTensors([output_tensor_wrapper]) |
| 108 | + |
| 109 | + adaptive_max_pool2d_op.AddTensorParam( |
| 110 | + OpPoolMax2d.param_filter_size, |
| 111 | + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, |
| 112 | + len(filter_shape), |
| 113 | + filter_shape, |
| 114 | + np.array( |
| 115 | + filter, |
| 116 | + dtype=np.uint32, |
| 117 | + ), |
| 118 | + True, |
| 119 | + ) |
| 120 | + |
| 121 | + adaptive_max_pool2d_op.AddTensorParam( |
| 122 | + OpPoolMax2d.param_stride, |
| 123 | + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, |
| 124 | + len(stride_shape), |
| 125 | + stride_shape, |
| 126 | + np.array( |
| 127 | + stride, |
| 128 | + dtype=np.uint32, |
| 129 | + ), |
| 130 | + True, |
| 131 | + ) |
| 132 | + |
| 133 | + adaptive_max_pool2d_op.AddTensorParam( |
| 134 | + OpPoolMax2d.param_pad_amount, |
| 135 | + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, |
| 136 | + len(padding_shape), |
| 137 | + padding_shape, |
| 138 | + np.array( |
| 139 | + [[padding[0], padding[0]], [padding[1], padding[1]]], |
| 140 | + dtype=np.uint32, |
| 141 | + ), |
| 142 | + True, |
| 143 | + ) |
| 144 | + |
| 145 | + adaptive_max_pool2d_op.AddScalarParam( |
| 146 | + OpPoolMax2d.param_rounding_mode, |
| 147 | + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, |
| 148 | + {QCOM_DATA: np.uint32(mode)}, |
| 149 | + ) |
| 150 | + |
| 151 | + return adaptive_max_pool2d_op |
0 commit comments