| /* |
| * 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 elemwise_scatter_op.cc |
| * \brief CPU implementation of elementwise scatter operators |
| */ |
| #include "./elemwise_binary_op-inl.h" |
| #include "./elemwise_binary_scalar_op.h" |
| #include "./elemwise_scatter_op.h" |
| |
| namespace mxnet { |
| namespace op { |
| |
| static bool StorageTypeRspOrDenseOutput(const NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int>* in_attrs, |
| std::vector<int>* out_attrs) { |
| CHECK_EQ(in_attrs->size(), 2U); |
| CHECK_EQ(out_attrs->size(), 1U); |
| const auto lhs_stype = static_cast<NDArrayStorageType>((*in_attrs)[0]); |
| if (common::ContainsOnlyStorage(*in_attrs, kDefaultStorage) |
| && common::ContainsOnlyStorage(*out_attrs, kDefaultStorage)) { |
| if (storage_type_assign(&out_attrs[0], kDefaultStorage, |
| dispatch_mode, DispatchMode::kFCompute)) { |
| return true; |
| } |
| } |
| if (lhs_stype == kRowSparseStorage) { |
| if (storage_type_assign(&out_attrs[0], kRowSparseStorage, |
| dispatch_mode, |
| DispatchMode::kFComputeEx)) { |
| return true; |
| } |
| } |
| return dispatch_fallback(out_attrs, dispatch_mode); |
| } |
| |
| static bool StorageTypeScatteredScalarOp(const NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int>* in_attrs, |
| std::vector<int>* out_attrs) { |
| // Supports kDefaultStorage, kRowSparseStorage and kCSRStorage |
| const auto stype = static_cast<NDArrayStorageType>((*in_attrs)[0]); |
| if (storage_type_assign(out_attrs, |
| stype, |
| dispatch_mode, |
| stype == kDefaultStorage ? DispatchMode::kFCompute |
| : DispatchMode::kFComputeEx)) { |
| return true; |
| } |
| return dispatch_fallback(out_attrs, dispatch_mode); |
| } |
| |
| /*! \brief _scatter_elemwise_div */ |
| MXNET_OPERATOR_REGISTER_BINARY(_scatter_elemwise_div) |
| .set_attr<FCompute>("FCompute<cpu>", ElemwiseScatterBinaryOp::Compute<cpu, op::mshadow_op::div>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", ElemwiseScatterBinaryOp::ComputeEx< |
| cpu, op::mshadow_op::div>) |
| .describe(R"code(Divides arguments element-wise. If the left-hand-side input is 'row_sparse', then |
| only the values which exist in the left-hand sparse array are computed. The 'missing' values |
| are ignored. |
| |
| The storage type of ``_scatter_elemwise_div`` output depends on storage types of inputs |
| |
| - _scatter_elemwise_div(row_sparse, row_sparse) = row_sparse |
| - _scatter_elemwise_div(row_sparse, dense) = row_sparse |
| - _scatter_elemwise_div(row_sparse, csr) = row_sparse |
| - otherwise, ``_scatter_elemwise_div`` behaves exactly like elemwise_div and generates output |
| with default storage |
| |
| )code") |
| .set_attr<FInferStorageType>("FInferStorageType", StorageTypeRspOrDenseOutput) |
| .set_attr<FResourceRequest>("FResourceRequest", |
| [](const NodeAttrs& attrs) { |
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; |
| }) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_div"}); |
| |
| /*! \brief _scatter_plus_scalar */ |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_scatter_plus_scalar) |
| .describe(R"code(Adds a scalar to a tensor element-wise. If the left-hand-side input is |
| 'row_sparse' or 'csr', then only the values which exist in the left-hand sparse array are computed. |
| The 'missing' values are ignored. |
| |
| The storage type of ``_scatter_plus_scalar`` output depends on storage types of inputs |
| |
| - _scatter_plus_scalar(row_sparse, scalar) = row_sparse |
| - _scatter_plus_scalar(csr, scalar) = csr |
| - otherwise, ``_scatter_plus_scalar`` behaves exactly like _plus_scalar and generates output |
| with default storage |
| |
| )code") |
| .set_attr<FInferStorageType>("FInferStorageType", StorageTypeScatteredScalarOp) |
| .set_attr<FCompute>("FCompute<cpu>", |
| ElemwiseScatterBinaryScalarOp::Compute<cpu, op::mshadow_op::plus>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", |
| ElemwiseScatterBinaryScalarOp::ComputeEx<cpu, op::mshadow_op::plus>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"}); |
| |
| /*! \brief _scatter_minus_scalar */ |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_scatter_minus_scalar) |
| .describe(R"code(Subtracts a scalar to a tensor element-wise. If the left-hand-side input is |
| 'row_sparse' or 'csr', then only the values which exist in the left-hand sparse array are computed. |
| The 'missing' values are ignored. |
| |
| The storage type of ``_scatter_minus_scalar`` output depends on storage types of inputs |
| |
| - _scatter_minus_scalar(row_sparse, scalar) = row_sparse |
| - _scatter_minus_scalar(csr, scalar) = csr |
| - otherwise, ``_scatter_minus_scalar`` behaves exactly like _minus_scalar and generates output |
| with default storage |
| |
| )code") |
| .set_attr<FInferStorageType>("FInferStorageType", StorageTypeScatteredScalarOp) |
| .set_attr<FCompute>("FCompute<cpu>", |
| ElemwiseScatterBinaryScalarOp::Compute<cpu, op::mshadow_op::minus>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", |
| ElemwiseScatterBinaryScalarOp::ComputeEx<cpu, op::mshadow_op::minus>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"}); |
| |
| } // namespace op |
| } // namespace mxnet |
