| /* |
| * 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) 2017 by Contributors |
| * \file softmax.cc |
| * \brief CPU Implementation of softmax |
| */ |
| #include "./softmax-inl.h" |
| #include "../tensor/elemwise_unary_op.h" |
| #include "../tensor/elemwise_binary_op.h" |
| #include "../operator_common.h" |
| #if MXNET_USE_MKLDNN == 1 |
| #include "mkldnn/mkldnn_base-inl.h" |
| #include "mkldnn/mkldnn_ops-inl.h" |
| #endif |
| |
| namespace mxnet { |
| namespace op { |
| DMLC_REGISTER_PARAMETER(SoftmaxParam); |
| |
| #if MXNET_USE_MKLDNN == 1 |
| static void SoftmaxComputeExCPU(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs) { |
| // It seems MKLDNN softmax doesn't support training. |
| const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed); |
| if (SupportMKLDNN(inputs[0]) && !ctx.is_train && SupportMKLDNNSoftmax(param)) { |
| MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs); |
| MKLDNNSoftmaxForward(attrs, ctx, inputs[0], req[0], outputs[0]); |
| auto fn = SoftmaxCompute<cpu, mxnet_op::softmax_fwd>; |
| MKLDNN_OPCHECK_RUN(fn, attrs, ctx, inputs, req, outputs); |
| return; |
| } |
| FallBackCompute(SoftmaxCompute<cpu, mxnet_op::softmax_fwd>, attrs, ctx, |
| inputs, req, outputs); |
| } |
| |
| inline static bool SoftmaxStorageType(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); |
| |
| return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, |
| out_attrs); |
| } |
| #endif |
| |
| NNVM_REGISTER_OP(softmax) |
| .describe(R"code(Applies the softmax function. |
| |
| The resulting array contains elements in the range (0,1) and the elements along the given axis sum up to 1. |
| |
| .. math:: |
| softmax(\mathbf{z/t})_j = \frac{e^{z_j/t}}{\sum_{k=1}^K e^{z_k/t}} |
| |
| for :math:`j = 1, ..., K` |
| |
| t is the temperature parameter in softmax function. By default, t equals 1.0 |
| |
| Example:: |
| |
| x = [[ 1. 1. 1.] |
| [ 1. 1. 1.]] |
| |
| softmax(x,axis=0) = [[ 0.5 0.5 0.5] |
| [ 0.5 0.5 0.5]] |
| |
| softmax(x,axis=1) = [[ 0.33333334, 0.33333334, 0.33333334], |
| [ 0.33333334, 0.33333334, 0.33333334]] |
| |
| )code" ADD_FILELINE) |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<nnvm::FListOutputNames>("FListOutputNames", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::string>{"output"}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::softmax_fwd>) |
| #if MXNET_USE_MKLDNN == 1 |
| .set_attr<bool>("TIsMKLDNN", true) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", SoftmaxComputeExCPU) |
| .set_attr<FInferStorageType>("FInferStorageType", SoftmaxStorageType) |
| #endif |
| .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_softmax"}) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType) |
| .set_num_inputs(1) |
| .set_num_outputs(1) |
| .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", |
| [](const NodeAttrs& attrs){ |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| .add_argument("data", "NDArray-or-Symbol", "The input array.") |
| .add_arguments(SoftmaxParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_softmax) |
| .set_num_inputs(SoftmaxGradOpNumInputs) |
| .set_num_outputs(1) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames) |
| .set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption) |
| .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, op::mshadow_op::mul, |
| mxnet_op::softmax_bwd>); |
| |
| NNVM_REGISTER_OP(softmin) |
| .describe(R"code(Applies the softmin function. |
| |
| The resulting array contains elements in the range (0,1) and the elements along the given axis sum |
| up to 1. |
| |
| .. math:: |
| softmin(\mathbf{z/t})_j = \frac{e^{-z_j/t}}{\sum_{k=1}^K e^{-z_k/t}} |
| |
| for :math:`j = 1, ..., K` |
| |
| t is the temperature parameter in softmax function. By default, t equals 1.0 |
| |
| Example:: |
| |
| x = [[ 1. 2. 3.] |
| [ 3. 2. 1.]] |
| |
| softmin(x,axis=0) = [[ 0.88079703, 0.5, 0.11920292], |
| [ 0.11920292, 0.5, 0.88079703]] |
| |
| softmin(x,axis=1) = [[ 0.66524094, 0.24472848, 0.09003057], |
| [ 0.09003057, 0.24472848, 0.66524094]] |
| |
| )code" ADD_FILELINE) |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<nnvm::FListOutputNames>("FListOutputNames", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::string>{"output"}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::softmax_fwd, true>) |
| .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_softmin"}) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType) |
| .set_num_inputs(1) |
| .set_num_outputs(1) |
| .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", |
| [](const NodeAttrs& attrs){ |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| .add_argument("data", "NDArray-or-Symbol", "The input array.") |
| .add_arguments(SoftmaxParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_softmin) |
| .set_num_inputs(SoftmaxGradOpNumInputs) |
| .set_num_outputs(1) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames) |
| .set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption) |
| .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, op::mshadow_op::mul, |
| mxnet_op::softmax_bwd, true>); |
| |
| NNVM_REGISTER_OP(log_softmax) |
| .describe(R"code(Computes the log softmax of the input. |
| This is equivalent to computing softmax followed by log. |
| |
| Examples:: |
| |
| >>> x = mx.nd.array([1, 2, .1]) |
| >>> mx.nd.log_softmax(x).asnumpy() |
| array([-1.41702998, -0.41702995, -2.31702995], dtype=float32) |
| |
| >>> x = mx.nd.array( [[1, 2, .1],[.1, 2, 1]] ) |
| >>> mx.nd.log_softmax(x, axis=0).asnumpy() |
| array([[-0.34115392, -0.69314718, -1.24115396], |
| [-1.24115396, -0.69314718, -0.34115392]], dtype=float32) |
| |
| |
| )code") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>) |
| .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_log_softmax"}) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType) |
| .set_num_inputs(1) |
| .set_num_outputs(1) |
| .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", |
| [](const NodeAttrs& attrs){ |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| .add_argument("data", "NDArray-or-Symbol", "The input array.") |
| .add_arguments(SoftmaxParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_log_softmax) |
| .set_num_inputs(SoftmaxGradOpNumInputs) |
| .set_num_outputs(1) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames) |
| .set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption) |
| .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, mshadow_op::left, |
| mxnet_op::log_softmax_bwd>); |
| |
| } // namespace op |
| } // namespace mxnet |
