haojin2 commented on a change in pull request #17302: [numpy]add op random.logistic URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r366765196
########## File path: src/operator/numpy/random/np_logistic_op.h ########## @@ -0,0 +1,331 @@ +/* + * 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) 2019 by Contributors + * \file np_logistic_op.h + * \brief Operator for numpy sampling from logistic distributions + */ +#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_ +#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_ + +#include <mxnet/operator_util.h> +#include <cstdio> +#include <algorithm> +#include <string> +#include <vector> +#include <cmath> +#include "../../elemwise_op_common.h" +#include "../../mshadow_op.h" +#include "../../mxnet_op.h" +#include "../../operator_common.h" +#include "../../tensor/elemwise_binary_broadcast_op.h" +#include "./dist_common.h" + +namespace mxnet { +namespace op { + +struct NumpyLogisticParam : public dmlc::Parameter<NumpyLogisticParam> { + dmlc::optional<float> loc; + dmlc::optional<float> scale; + dmlc::optional<mxnet::Tuple<int>> size; + DMLC_DECLARE_PARAMETER(NumpyLogisticParam) { + DMLC_DECLARE_FIELD(loc); + DMLC_DECLARE_FIELD(scale); + DMLC_DECLARE_FIELD(size) + .set_default(dmlc::optional<mxnet::Tuple<int>>()) + .describe( + "Output shape. If the given shape is, " + "e.g., (m, n, k), then m * n * k samples are drawn. " + "Default is None, in which case a single value is returned."); + } +}; + +inline bool NumpyLogisticOpType(const nnvm::NodeAttrs &attrs, + std::vector<int> *in_attrs, + std::vector<int> *out_attrs) { + + (*out_attrs)[0] = mshadow::kFloat32; + (*out_attrs)[1] = mshadow::kFloat32; + return true; +} + +namespace mxnet_op { + +template <typename DType> +struct logistic_two_scalar_kernel { + MSHADOW_XINLINE static void Map(index_t i, float loc, float scale, + float *noise, DType *out) { + noise[i] = log(noise[i]) - log(1 - noise[i]); + out[i] = loc + noise[i] * scale; + } +}; + +template <typename IType> +struct check_legal_scale_kernel { + MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) { + if (scalar[i] < 0) { + *flag = -1.0; + } + } +}; + +template <int ndim, typename IType, typename OType> +struct logistic_one_scalar_kernel { + MSHADOW_XINLINE static void Map(index_t i, int scalar_pos, + const Shape<ndim> &stride, + const Shape<ndim> &oshape, IType *array, + float scalar, float *noise, OType *out) { + Shape<ndim> coord = unravel(i, oshape); + auto idx = static_cast<index_t>(dot(coord, stride)); + IType loc_value; + IType scale_value; + if (scalar_pos == 0) { + loc_value = scalar; + scale_value = array[idx]; + } else { + loc_value = array[idx]; + scale_value = scalar; + } + noise[i] = log(noise[i]) - log(1 - noise[i]); + out[i] = loc_value + noise[i] * scale_value; + } +}; + +template <int ndim, typename IType, typename OType> +struct logistic_kernel { + MSHADOW_XINLINE static void Map(index_t i, const Shape<ndim> &lstride, + const Shape<ndim> &hstride, + const Shape<ndim> &oshape, IType *loc, + IType *scale, float *noise, OType *out) { + Shape<ndim> coord = unravel(i, oshape); + auto lidx = static_cast<index_t>(dot(coord, lstride)); + auto hidx = static_cast<index_t>(dot(coord, hstride)); + IType loc_value = loc[lidx]; + IType scale_value = scale[hidx]; + noise[i] = log(noise[i]) - log(1 - noise[i]); + out[i] = loc_value + noise[i] * scale_value; + } +}; + +} // namespace mxnet_op + +template <typename xpu> +void NumpyLogisticForward(const nnvm::NodeAttrs &attrs, + const OpContext &ctx, + const std::vector<TBlob> &inputs, + const std::vector<OpReqType> &req, + const std::vector<TBlob> &outputs) { + using namespace mshadow; + using namespace mxnet_op; + const NumpyLogisticParam ¶m = nnvm::get<NumpyLogisticParam>(attrs.parsed); + Stream<xpu> *s = ctx.get_stream<xpu>(); + // Generate base random number. + Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s); + Tensor<xpu, 1, float> workspace = + ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(1), s); + Tensor<xpu, 1, float> logistic_tensor = outputs[1].FlatTo1D<xpu, float>(s); + Tensor<xpu, 1, float> indicator_device = workspace; + float indicator_host = 1.0; + float *indicator_device_ptr = indicator_device.dptr_; + Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr); + prnd->SampleUniform(&logistic_tensor, 0.0, 1.0); + mxnet::TShape new_lshape, new_hshape, new_oshape; + // [scalar scalar] case + if (inputs.size() == 0U) { + CHECK_GE(param.scale.value(), 0.0) << "ValueError: scale < 0"; + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, { + Kernel<logistic_two_scalar_kernel<DType>, xpu>::Launch( + s, outputs[0].Size(), param.loc.value(), param.scale.value(), + logistic_tensor.dptr_, outputs[0].dptr<DType>()); + }); + } else if (inputs.size() == 1U) { + // [scalar tensor], [tensor scalar] case + int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_, + &new_lshape, &new_lshape, &new_oshape); + int scalar_pos; + float scalar_value; + if (param.loc.has_value()) { + scalar_pos = 0; + scalar_value = param.loc.value(); + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + Kernel<check_legal_scale_kernel<IType>, xpu>::Launch( + s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr); + }); + _copy<xpu>(s, &indicator_host, indicator_device_ptr); + CHECK_GE(indicator_host, 0.0) << "ValueError: scale < 0"; + } else { + scalar_pos = 1; + scalar_value = param.scale.value(); + CHECK_GE(scalar_value, 0.0) << "ValueError: scale < 0"; + } + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { + BROADCAST_NDIM_SWITCH(ndim, NDim, { + Shape<NDim> oshape = new_oshape.get<NDim>(); + Shape<NDim> stride = calc_stride(new_lshape.get<NDim>()); + Kernel<logistic_one_scalar_kernel<NDim, IType, OType>, xpu>::Launch( + s, outputs[0].Size(), scalar_pos, stride, oshape, + inputs[0].dptr<IType>(), scalar_value, logistic_tensor.dptr_, + outputs[0].dptr<OType>()); + }); + }); + }); + } else if (inputs.size() == 2U) { + // [tensor tensor] case + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + Kernel<check_legal_scale_kernel<IType>, xpu>::Launch( + s, inputs[1].Size(), inputs[1].dptr<IType>(), indicator_device_ptr); + }); + _copy<xpu>(s, &indicator_host, indicator_device_ptr); + CHECK_GE(indicator_host, 0.0) << "ValueError: scale < 0"; + int ndim = FillShape(inputs[0].shape_, inputs[1].shape_, outputs[0].shape_, + &new_lshape, &new_hshape, &new_oshape); + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { + BROADCAST_NDIM_SWITCH(ndim, NDim, { + Shape<NDim> oshape = new_oshape.get<NDim>(); + Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>()); + Shape<NDim> hstride = calc_stride(new_hshape.get<NDim>()); + Kernel<logistic_kernel<NDim, IType, OType>, xpu>::Launch( + s, outputs[0].Size(), lstride, hstride, oshape, + inputs[0].dptr<IType>(), inputs[1].dptr<IType>(), + logistic_tensor.dptr_, outputs[0].dptr<OType>()); + }); + }); + }); + } +} + +template<typename xpu, int ndim, typename DType> +inline void LogisticReparamBackwardImpl(const OpContext& ctx, + const std::vector<TBlob>& inputs, + const std::vector<OpReqType>& req, + const std::vector<TBlob>& outputs, + const mxnet::TShape& new_lshape, + const mxnet::TShape& new_rshape, + const mxnet::TShape& new_oshape) { + using namespace mshadow; + using namespace mshadow::expr; + using namespace broadcast; + Stream<xpu> *s = ctx.get_stream<xpu>(); + const TBlob lgrad = outputs[0].reshape(new_lshape); + const TBlob rgrad = outputs[1].reshape(new_rshape); + const TBlob ograd = inputs[0].reshape(new_oshape); + // Mean + const TBlob lhs = inputs[2].reshape(new_lshape); + // Variance + const TBlob rhs = inputs[3].reshape(new_rshape); + const TBlob samples = inputs[4].reshape(new_oshape); + const TBlob noise = inputs[5].reshape(new_oshape); + size_t workspace_size_l = ReduceWorkspaceSize<ndim, DType>( + s, lgrad.shape_, req[0], ograd.shape_, lhs.shape_, rhs.shape_); + size_t workspace_size_r = ReduceWorkspaceSize<ndim, DType>( + s, rgrad.shape_, req[1], ograd.shape_, lhs.shape_, rhs.shape_); + size_t workspace_size = std::max(workspace_size_l, workspace_size_r); + Tensor<xpu, 1, char> workspace = + ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), s); + Reduce<red::sum, ndim, DType, op::mshadow_op::identity>(s, + lgrad, req[0], workspace, ograd); + Reduce<red::sum, ndim, DType, op::mshadow_op::mul, op::mshadow_op::left>( + s, rgrad, req[1], workspace, ograd, noise, rhs); +} + +template<typename xpu, int ndim, typename DType> +inline void ScalarLogisticReparamBackwardImpl(const OpContext& ctx, + const std::vector<TBlob>& inputs, Review comment: alignment ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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