| /* |
| * 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. |
| */ |
| |
| /*! |
| * Copyright (c) 2016 by Contributors |
| * \file elemwise_binary_scalar_op.cc |
| * \brief CPU Implementation of unary function. |
| */ |
| #include "../../common/utils.h" |
| #include "./elemwise_binary_op.h" |
| #include "./elemwise_binary_scalar_op.h" |
| |
| #define MXNET_OPERATOR_REGISTER_BINARY_WITH_SCALAR_SUPPORT_WITH_DENSE_RESULT(name) \ |
| NNVM_REGISTER_OP(name) \ |
| .set_num_inputs(1) \ |
| .set_num_outputs(1) \ |
| .set_attr_parser([](NodeAttrs* attrs) { \ |
| attrs->parsed = std::stod(attrs->dict["scalar"]); \ |
| }) \ |
| .set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \ |
| .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>) \ |
| .set_attr<FInferStorageType>("FInferStorageType", \ |
| BinaryScalarStorageTypeWithDenseResultStorageType) \ |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", \ |
| [](const NodeAttrs& attrs){ \ |
| return std::vector<std::pair<int, int> >{{0, 0}}; \ |
| }) \ |
| .add_argument("data", "NDArray-or-Symbol", "source input") \ |
| .add_argument("scalar", "float", "scalar input") |
| |
| namespace mxnet { |
| namespace op { |
| |
| /*! |
| * \brief FInferStorageType for binary operator with scalar, |
| * csr -> csr and row_sparse -> row_sparse if the scalar is zero, |
| * otherwise the output is of default storage. |
| */ |
| static bool BinaryScalarStorageTypeWithDenseResultStorageType(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(), 1); |
| CHECK_EQ(out_attrs->size(), 1); |
| bool dispatched = false; |
| const bool invalid_ctx = dev_mask != kCPU; |
| const NDArrayStorageType instype = static_cast<NDArrayStorageType>(in_attrs->at(0)); |
| const auto dispatch_ex = invalid_ctx ? DispatchMode::kFComputeFallback |
| : DispatchMode::kFComputeEx; |
| const double alpha = nnvm::get<double>(attrs.parsed); |
| if (common::ContainsOnlyStorage(*in_attrs, kDefaultStorage)) { |
| dispatched = storage_type_assign(&out_attrs[0], |
| kDefaultStorage, dispatch_mode, DispatchMode::kFCompute); |
| } |
| if (!dispatched && (instype == kCSRStorage || instype == kRowSparseStorage)) { |
| dispatched = storage_type_assign(&out_attrs[0], alpha == 0 ? instype : kDefaultStorage, |
| dispatch_mode, dispatch_ex); |
| } |
| if (!dispatched) { |
| dispatched = dispatch_fallback(out_attrs, dispatch_mode); |
| } |
| return dispatched; |
| } |
| |
| static bool BinaryScalarStorageType(const nnvm::NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int> *in_attrs, |
| std::vector<int> *out_attrs) { |
| CHECK_EQ(in_attrs->size(), 1); |
| CHECK_EQ(out_attrs->size(), 1); |
| const auto in_stype = in_attrs->at(0); |
| auto &out_stype = out_attrs->at(0); |
| bool dispatched = false; |
| if (!dispatched && (in_stype == kDefaultStorage)) { |
| // dns -> dns |
| dispatched = storage_type_assign(&out_stype, kDefaultStorage, |
| dispatch_mode, DispatchMode::kFCompute); |
| } |
| if (!dispatched && in_stype == kRowSparseStorage) { |
| // rsp -> rsp |
| dispatched = storage_type_assign(&out_stype, kRowSparseStorage, |
| dispatch_mode, DispatchMode::kFComputeEx); |
| // FComputeEx can handle dns output on cpu, too |
| if (dev_mask == cpu::kDevMask && out_stype == kDefaultStorage) { |
| DISPATCH_MODE_ASSIGN_CHECK(dispatch_mode, 0, DispatchMode::kFComputeEx); |
| dispatched = true; |
| } |
| } |
| if (!dispatched && in_stype == kCSRStorage) { |
| // csr -> csr |
| dispatched = storage_type_assign(&out_stype, kCSRStorage, |
| dispatch_mode, DispatchMode::kFComputeEx); |
| // FComputeEx can handle dns output on cpu, too |
| if (dev_mask == cpu::kDevMask && out_stype == kDefaultStorage) { |
| DISPATCH_MODE_ASSIGN_CHECK(dispatch_mode, 0, DispatchMode::kFComputeEx); |
| dispatched = true; |
| } |
| } |
| if (!dispatched) { |
| dispatched = dispatch_fallback(out_attrs, dispatch_mode); |
| } |
| return dispatched; |
| } |
| |
| MXNET_OPERATOR_REGISTER_BINARY_WITH_SCALAR_SUPPORT_WITH_DENSE_RESULT(_plus_scalar) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::plus>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", BinaryScalarOp::ComputeEx<cpu, op::mshadow_op::plus>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"}) |
| .add_alias("_PlusScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_WITH_SCALAR_SUPPORT_WITH_DENSE_RESULT(_minus_scalar) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::minus>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", BinaryScalarOp::ComputeEx<cpu, op::mshadow_op::minus>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_copy"}) |
| .add_alias("_MinusScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_rminus_scalar) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rminus>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"negative"}) |
| .add_alias("_RMinusScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_mul_scalar) |
| .describe(R"doc(Multiply an array with a scalar. |
| |
| ``_mul_scalar`` only operates on data array of input if input is sparse. |
| |
| For example, if input of shape (100, 100) has only 2 non zero elements, |
| i.e. input.data = [5, 6], scalar = nan, |
| it will result output.data = [nan, nan] instead of 10000 nans. |
| |
| )doc" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", BinaryScalarStorageType) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::mul>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", BinaryScalarOp::ComputeEx<cpu, op::mshadow_op::mul>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_mul_scalar"}) |
| .add_alias("_MulScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_backward_mul_scalar) |
| .set_attr<nnvm::TIsBackward>("TIsBackward", true) |
| .set_attr<FInferStorageType>("FInferStorageType", BinaryScalarStorageType) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::mul>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", BinaryScalarOp::ComputeEx<cpu, op::mshadow_op::mul>); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_div_scalar) |
| .describe(R"doc(Divide an array with a scalar. |
| |
| ``_div_scalar`` only operates on data array of input if input is sparse. |
| |
| For example, if input of shape (100, 100) has only 2 non zero elements, |
| i.e. input.data = [5, 6], scalar = nan, |
| it will result output.data = [nan, nan] instead of 10000 nans. |
| |
| )doc" ADD_FILELINE) |
| .set_attr<FInferStorageType>("FInferStorageType", BinaryScalarStorageType) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::div>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", BinaryScalarOp::ComputeEx<cpu, op::mshadow_op::div>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_div_scalar"}) |
| .add_alias("_DivScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_backward_div_scalar) |
| .set_attr<nnvm::TIsBackward>("TIsBackward", true) |
| .set_attr<FInferStorageType>("FInferStorageType", BinaryScalarStorageType) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, op::mshadow_op::div>) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", BinaryScalarOp::ComputeEx<cpu, op::mshadow_op::div>); |
| |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_rdiv_scalar) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rdiv>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rdiv_scalar"}) |
| .add_alias("_RDivScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY(_backward_rdiv_scalar) |
| .add_argument("scalar", "float", "scalar value") |
| .set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); }) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward< |
| cpu, mshadow_op::rdiv_grad>); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_mod_scalar) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::mod>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_mod_scalar"}) |
| .add_alias("_ModScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY(_backward_mod_scalar) |
| .add_argument("scalar", "float", "scalar value") |
| .set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); }) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward< |
| cpu, mshadow_op::mod_grad>); |
| |
| MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_rmod_scalar) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Compute<cpu, mshadow_op::rmod>) |
| .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rmod_scalar"}) |
| .add_alias("_RModScalar"); |
| |
| MXNET_OPERATOR_REGISTER_BINARY(_backward_rmod_scalar) |
| .add_argument("scalar", "float", "scalar value") |
| .set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); }) |
| .set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward< |
| cpu, mshadow_op::rmod_grad>); |
| |
| } // namespace op |
| } // namespace mxnet |
