| @@ -0,0 +1,40 @@ | |||||
| # 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. | |||||
| """logical_not""" | |||||
| import _akg.tvm | |||||
| from _akg.ops.math import logical_not | |||||
| from _akg.topi.generic import schedule_elemwise | |||||
| def LogicalNot(x): | |||||
| """LogicalNot.""" | |||||
| return logical_not.logical_not(x) | |||||
| def gpu_schedule_LogicalNot(outs): | |||||
| """ | |||||
| GPU schedule for LogicalNot. | |||||
| Args: | |||||
| outs (tvm.tensor.Tensor): outputs of compute. | |||||
| Returns: | |||||
| sch (schedule.Schedule): The created schedule. | |||||
| """ | |||||
| device = 'cuda' | |||||
| ctx = _akg.tvm.context(device, 0) | |||||
| if not ctx.exist: | |||||
| raise SystemError("Skip because %s is not enabled" % device) | |||||
| with _akg.tvm.target.create(device): | |||||
| sch = schedule_elemwise(outs) | |||||
| return sch | |||||
| @@ -0,0 +1,40 @@ | |||||
| # 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. | |||||
| """sub""" | |||||
| import _akg.tvm | |||||
| from _akg.ops.math import sub | |||||
| from _akg.topi.generic import schedule_elemwise | |||||
| def Sub(x, y): | |||||
| """Sub.""" | |||||
| return sub.sub(x, y) | |||||
| def gpu_schedule_Sub(outs): | |||||
| """ | |||||
| GPU schedule for Sub. | |||||
| Args: | |||||
| outs (tvm.tensor.Tensor): outputs of compute. | |||||
| Returns: | |||||
| sch (schedule.Schedule): The created schedule. | |||||
| """ | |||||
| device = 'cuda' | |||||
| ctx = _akg.tvm.context(device, 0) | |||||
| if not ctx.exist: | |||||
| raise SystemError("Skip because %s is not enabled" % device) | |||||
| with _akg.tvm.target.create(device): | |||||
| sch = schedule_elemwise(outs) | |||||
| return sch | |||||
| @@ -0,0 +1,32 @@ | |||||
| # Copyright 2019 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. | |||||
| """operator dsl function: logical_not""" | |||||
| import _akg.tvm | |||||
| import _akg.topi | |||||
| from _akg.utils import validation_check as vc_util | |||||
| @vc_util.check_input_type(_akg.tvm.tensor.Tensor) | |||||
| def logical_not(input1): | |||||
| """ | |||||
| Compute logical_not of input1. | |||||
| Args: | |||||
| input1 (tvm.tensor.Tensor): Tensor. | |||||
| Returns: | |||||
| tvm.tensor.Tensor. | |||||
| """ | |||||
| res = _akg.topi.logical_not(input1) | |||||
| return res | |||||
| @@ -0,0 +1,28 @@ | |||||
| # 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. | |||||
| """LogicalNot op""" | |||||
| from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType | |||||
| logical_not_op_info = AkgRegOp("LogicalNot") \ | |||||
| .fusion_type("OPAQUE") \ | |||||
| .input(0, "x") \ | |||||
| .output(0, "output") \ | |||||
| .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \ | |||||
| .get_op_info() | |||||
| @op_info_register(logical_not_op_info) | |||||
| def _logical_not_akg(): | |||||
| """LogicalNot AutoDiff register""" | |||||
| return | |||||
| @@ -0,0 +1,31 @@ | |||||
| # 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. | |||||
| """Sub op""" | |||||
| from mindspore.ops.op_info_register import op_info_register, AkgRegOp, DataType | |||||
| sub_op_info = AkgRegOp("Sub") \ | |||||
| .fusion_type("OPAQUE") \ | |||||
| .input(0, "x") \ | |||||
| .input(1, "y") \ | |||||
| .output(0, "output") \ | |||||
| .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \ | |||||
| .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \ | |||||
| .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \ | |||||
| .get_op_info() | |||||
| @op_info_register(sub_op_info) | |||||
| def _sub_akg(): | |||||
| """Sub AutoDiff register""" | |||||
| return | |||||