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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15from __future__ import absolute_import
16
17import te.lang.cce
18from te import tvm
19from te.platform.fusion_manager import fusion_manager
20from topi import generic
21from topi.cce import util
22
23from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
24
25@fusion_manager.register("square")
26def square_compute(input_x, output_y):
27    """
28    algorithm: square
29    calculating data's square,y= x*x
30
31    Parameters
32    ----------
33    input_x: TVM tensor
34        the placeholder of input data
35    output_y: dict
36        shape and dtype of output, should be same shape and type as input
37    kernel_name: str
38        cce kernel name, default value is square
39
40    Returns
41    -------
42    res : tvm.tensor
43        the result of square
44    """
45    res = te.lang.cce.vmul(input_x, input_x)
46    return res
47
48
49cus_square_op_info = TBERegOp("CusSquare") \
50    .fusion_type("OPAQUE") \
51    .async_flag(False) \
52    .binfile_name("square.so") \
53    .compute_cost(10) \
54    .kernel_name("CusSquareImpl") \
55    .partial_flag(True) \
56    .input(0, "x", False, "required", "all") \
57    .output(0, "y", False, "required", "all") \
58    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
59    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
60    .get_op_info()
61
62
63@op_info_register(cus_square_op_info)
64def CusSquareImpl(input_x, output_y, kernel_name="CusSquareImpl"):
65    """
66    algorithm: square
67    calculating data's square,y= x*x
68
69    Parameters
70    ----------
71    input_x : dict
72        shape and dtype of input, only support float32
73    output_y: dict
74        shape and dtype of output, should be same shape and type as input
75    kernel_name : str
76        kernel name, default value is "square"
77
78    Returns
79    -------
80    None
81    """
82    shape = input_x.get("shape")
83    dtype = input_x.get("dtype").lower()
84
85    shape = util.shape_refine(shape)
86    data = tvm.placeholder(shape, name="data", dtype=dtype.lower())
87
88    with tvm.target.cce():
89        res = square_compute(data, output_y)
90        sch = generic.auto_schedule(res)
91
92    config = {"print_ir": False,
93              "name": kernel_name,
94              "tensor_list": [data, res]}
95
96    te.lang.cce.cce_build_code(sch, config)
97