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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- from __future__ import absolute_import
-
- import te.lang.cce
- from te import tvm
- from te.platform.fusion_manager import fusion_manager
- from topi import generic
- from topi.cce import util
-
- from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
-
- @fusion_manager.register("square")
- def square_compute(input_x, output_y):
- """
- algorithm: square
- calculating data's square,y= x*x
-
- Parameters
- ----------
- input_x: TVM tensor
- the placeholder of input data
- output_y: dict
- shape and dtype of output, should be same shape and type as input
- kernel_name: str
- cce kernel name, default value is square
-
- Returns
- -------
- res : tvm.tensor
- the result of square
- """
- res = te.lang.cce.vmul(input_x, input_x)
- return res
-
-
- cus_square_op_info = TBERegOp("CusSquare") \
- .fusion_type("OPAQUE") \
- .async_flag(False) \
- .binfile_name("square.so") \
- .compute_cost(10) \
- .kernel_name("CusSquareImpl") \
- .partial_flag(True) \
- .input(0, "x", False, "required", "all") \
- .output(0, "y", False, "required", "all") \
- .dtype_format(DataType.F32_Default, DataType.F32_Default) \
- .dtype_format(DataType.F16_Default, DataType.F16_Default) \
- .get_op_info()
-
-
- @op_info_register(cus_square_op_info)
- def CusSquareImpl(input_x, output_y, kernel_name="CusSquareImpl"):
- """
- algorithm: square
- calculating data's square,y= x*x
-
- Parameters
- ----------
- input_x : dict
- shape and dtype of input, only support float32
- output_y: dict
- shape and dtype of output, should be same shape and type as input
- kernel_name : str
- kernel name, default value is "square"
-
- Returns
- -------
- None
- """
- shape = input_x.get("shape")
- dtype = input_x.get("dtype").lower()
-
- shape = util.shape_refine(shape)
- data = tvm.placeholder(shape, name="data", dtype=dtype.lower())
-
- with tvm.target.cce():
- res = square_compute(data, output_y)
- sch = generic.auto_schedule(res)
-
- config = {"print_ir": False,
- "name": kernel_name,
- "tensor_list": [data, res]}
-
- te.lang.cce.cce_build_code(sch, config)
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