| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| */ |
| |
| /*! |
| * \file np_random_op.cc |
| * \brief Operator for numpy sampling from normal distributions. |
| */ |
| #include "./np_normal_op.h" |
| |
| namespace mxnet { |
| namespace op { |
| |
| DMLC_REGISTER_PARAMETER(NumpyNormalParam); |
| |
| NNVM_REGISTER_OP(_npi_normal) |
| .describe("Numpy behavior normal") |
| .set_num_inputs([](const nnvm::NodeAttrs& attrs) { |
| const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed); |
| int num_inputs = 2; |
| if (param.loc.has_value()) |
| num_inputs -= 1; |
| if (param.scale.has_value()) |
| num_inputs -= 1; |
| return num_inputs; |
| }) |
| .set_num_outputs(2) |
| .set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs", |
| [](const NodeAttrs& attrs) { return 1; }) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", |
| [](const NodeAttrs& attrs) { |
| const NumpyNormalParam& param = |
| nnvm::get<NumpyNormalParam>(attrs.parsed); |
| int num_inputs = 2; |
| if (param.loc.has_value()) |
| num_inputs -= 1; |
| if (param.scale.has_value()) |
| num_inputs -= 1; |
| if (num_inputs == 0) |
| return std::vector<std::string>(); |
| if (num_inputs == 1) |
| return std::vector<std::string>{"input1"}; |
| return std::vector<std::string>{"input1", "input2"}; |
| }) |
| .set_attr_parser(ParamParser<NumpyNormalParam>) |
| .set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpShape<NumpyNormalParam>) |
| .set_attr<nnvm::FInferType>("FInferType", NumpyNormalOpType) |
| .set_attr<FResourceRequest>("FResourceRequest", |
| [](const nnvm::NodeAttrs& attrs) { |
| return std::vector<ResourceRequest>{ResourceRequest::kRandom, |
| ResourceRequest::kTempSpace}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", NumpyNormalForward<cpu>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_broadcast_normal"}) |
| .add_argument("input1", "NDArray-or-Symbol", "Source input") |
| .add_argument("input2", "NDArray-or-Symbol", "Source input") |
| .add_arguments(NumpyNormalParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_npi_normal_n) |
| .describe("Ndarray behavior normal") |
| .set_num_inputs([](const nnvm::NodeAttrs& attrs) { |
| const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed); |
| int num_inputs = 2; |
| if (param.loc.has_value()) |
| num_inputs -= 1; |
| if (param.scale.has_value()) |
| num_inputs -= 1; |
| return num_inputs; |
| }) |
| .set_num_outputs(2) |
| .set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs", |
| [](const NodeAttrs& attrs) { return 1; }) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", |
| [](const NodeAttrs& attrs) { |
| const NumpyNormalParam& param = |
| nnvm::get<NumpyNormalParam>(attrs.parsed); |
| int num_inputs = 2; |
| if (param.loc.has_value()) |
| num_inputs -= 1; |
| if (param.scale.has_value()) |
| num_inputs -= 1; |
| if (num_inputs == 0) |
| return std::vector<std::string>(); |
| if (num_inputs == 1) |
| return std::vector<std::string>{"input1"}; |
| return std::vector<std::string>{"input1", "input2"}; |
| }) |
| .set_attr_parser(ParamParser<NumpyNormalParam>) |
| .set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpConcatShape<NumpyNormalParam>) |
| .set_attr<nnvm::FInferType>("FInferType", NumpyNormalOpType) |
| .set_attr<FResourceRequest>("FResourceRequest", |
| [](const nnvm::NodeAttrs& attrs) { |
| return std::vector<ResourceRequest>{ResourceRequest::kRandom, |
| ResourceRequest::kTempSpace}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", NumpyNormalForward<cpu>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseInOut{"_backward_broadcast_normal"}) |
| .add_argument("input1", "NDArray-or-Symbol", "Source input") |
| .add_argument("input2", "NDArray-or-Symbol", "Source input") |
| .add_arguments(NumpyNormalParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_broadcast_normal) |
| .set_attr<nnvm::TIsBackward>("TIsBackward", true) |
| .set_attr_parser(ParamParser<NumpyNormalParam>) |
| .set_num_inputs([](const nnvm::NodeAttrs& attrs) { |
| const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed); |
| int num_inputs = 6; |
| if (param.loc.has_value()) |
| num_inputs -= 1; |
| if (param.scale.has_value()) |
| num_inputs -= 1; |
| return num_inputs; |
| }) |
| .set_num_outputs([](const nnvm::NodeAttrs& attrs) { |
| const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed); |
| int num_outputs = 2; |
| if (param.loc.has_value()) |
| num_outputs -= 1; |
| if (param.scale.has_value()) |
| num_outputs -= 1; |
| return num_outputs; |
| }) |
| .set_attr<FResourceRequest>("FResourceRequest", |
| [](const NodeAttrs& attrs) { |
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", NormalReparamBackward<cpu>) |
| .add_arguments(NumpyNormalParam::__FIELDS__()); |
| |
| } // namespace op |
| } // namespace mxnet |
