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125 lines
3.3 KiB
125 lines
3.3 KiB
5 years ago
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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from __future__ import absolute_import
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from te import tvm
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from topi import generic
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import te.lang.cce
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from topi.cce import util
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from te.platform.fusion_manager import fusion_manager
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from mindspore.ops.op_info_register import op_info_register
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# shape size limit for aicore is 2**31
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SHAPE_SIZE_LIMIT = 200000000
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@fusion_manager.register("square")
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def square_compute(input_x, output_y, kernel_name="square"):
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"""
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algorithm: square
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calculating data's square,y= x*x
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Parameters
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----------
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input_x: TVM tensor
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the placeholder of input data
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output_y: dict
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shape and dtype of output, should be same shape and type as input
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kernel_name: str
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cce kernel name, default value is square
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Returns
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-------
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res : tvm.tensor
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the result of square
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"""
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res = te.lang.cce.vmul(input_x, input_x)
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return res
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@op_info_register("""{
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"op_name": "CusSquare",
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"imply_type": "TBE",
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"fusion_type": "OPAQUE",
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"async_flag": false,
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"binfile_name": "square.so",
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"compute_cost": 10,
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"kernel_name": "CusSquare",
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"partial_flag": true,
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"attr": [
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],
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"inputs": [
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{
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"index": 0,
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"dtype": [
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"float32"
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],
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"format": [
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"DefaultFormat"
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],
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"name": "x",
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"need_compile": false,
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"param_type": "required",
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"shape": "all"
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}
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],
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"outputs": [
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{
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"index": 0,
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"dtype": [
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"float32"
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],
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"format": [
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"DefaultFormat"
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],
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"name": "y",
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"need_compile": false,
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"param_type": "required",
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"shape": "all"
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}
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]
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}""")
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def CusSquare(input_x, output_y, kernel_name="square"):
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"""
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algorithm: square
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calculating data's square,y= x*x
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Parameters
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----------
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input_x : dict
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shape and dtype of input, only support float32
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output_y: dict
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shape and dtype of output, should be same shape and type as input
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kernel_name : str
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kernel name, default value is "square"
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Returns
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-------
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None
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"""
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shape = input_x.get("shape")
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dtype = input_x.get("dtype").lower()
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shape = util.shape_refine(shape)
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data = tvm.placeholder(shape, name="data", dtype=dtype.lower())
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with tvm.target.cce():
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res = square_compute(data, output_y, kernel_name)
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sch = generic.auto_schedule(res)
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config = {"print_ir": False,
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"name": kernel_name,
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"tensor_list": [data, res]}
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te.lang.cce.cce_build_code(sch, config)
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