xidulu commented on a change in pull request #17316: [NumPy] add op random.laplace URL: https://github.com/apache/incubator-mxnet/pull/17316#discussion_r392265525
########## File path: src/operator/numpy/random/np_laplace_op.h ########## @@ -0,0 +1,232 @@ +/* + * 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_laplace_op.h + * \brief Operator for numpy sampling from Laplace distributions + */ +#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LAPLACE_OP_H_ +#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LAPLACE_OP_H_ + +#include <mxnet/operator_util.h> +#include <algorithm> +#include <string> +#include <vector> +#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 NumpyLaplaceParam : public dmlc::Parameter<NumpyLaplaceParam> { + dmlc::optional<float> loc; + dmlc::optional<float> scale; + std::string ctx; + int dtype; + dmlc::optional<mxnet::Tuple<int>> size; + DMLC_DECLARE_PARAMETER(NumpyLaplaceParam) { + 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."); + DMLC_DECLARE_FIELD(ctx).set_default("cpu").describe( + "Context of output, in format [cpu|gpu|cpu_pinned](n)." + " Only used for imperative calls."); + DMLC_DECLARE_FIELD(dtype) + .add_enum("float32", mshadow::kFloat32) + .add_enum("float64", mshadow::kFloat64) + .add_enum("float16", mshadow::kFloat16) + .set_default(mshadow::kFloat32) + .describe( + "DType of the output in case this can't be inferred. " + "Defaults to float32 if not defined (dtype=None)."); + } + + void SetAttrDict(std::unordered_map<std::string, std::string>* dict) { + std::ostringstream loc_s, scale_s, size_s, dtype_s; + loc_s << loc; + scale_s << scale; + size_s << size; + dtype_s << dtype; + (*dict)["loc"] = loc_s.str(); + (*dict)["scale"] = scale_s.str(); + (*dict)["size"] = size_s.str(); + (*dict)["dtype"] = dtype_s.str(); + // We do not set ctx, because ctx has been set in dict instead of InitOpParam. + // Setting ctx here results in an error. + } +}; + +inline bool NumpyLaplaceOpType(const nnvm::NodeAttrs &attrs, + std::vector<int> *in_attrs, + std::vector<int> *out_attrs) { + const NumpyLaplaceParam ¶m = nnvm::get<NumpyLaplaceParam>(attrs.parsed); + int otype = param.dtype; + if (otype != -1) { + (*out_attrs)[0] = otype; + } else { + (*out_attrs)[0] = mshadow::kFloat32; + } + return true; +} + +namespace mxnet_op { +template <int ndim, typename IType, typename OType> +struct laplace_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 *uniforms, 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]; + if (uniforms[i] < 0.5) { + out[i] = loc_value + scale_value * log(2 * uniforms[i]); + } else { + out[i] = loc_value - scale_value * log(2 * (1 - uniforms[i])); + } + } +}; + +template <int ndim, typename IType, typename OType> +struct laplace_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 *uniforms, 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; + } + if (uniforms[i] < 0.5) { + out[i] = loc_value + scale_value * log(2 * uniforms[i]); + } else { + out[i] = loc_value - scale_value * log(2 * (1 - uniforms[i])); + } + } +}; + +template <typename OType> +struct laplace_two_scalar_kernel { + MSHADOW_XINLINE static void Map(index_t i, float loc, float scale, + float *uniforms, OType *out) { + if (uniforms[i] < 0.5) { + out[i] = loc + scale * log(2 * uniforms[i]); + } else { + out[i] = loc - scale * log(2 * (1 - uniforms[i])); + } + } +}; +} // namespace mxnet_op + +template <typename xpu> +void NumpyLaplaceForward(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 NumpyLaplaceParam ¶m = nnvm::get<NumpyLaplaceParam>(attrs.parsed); + CHECK_EQ(outputs.size(), 1); + 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> laplace_tensor = + ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(outputs[0].Size()), + s); + prnd->SampleUniform(&laplace_tensor, 0, 1); + mxnet::TShape new_lshape, new_hshape, new_oshape; + + // [scalar scalar] case + if (inputs.size() == 0U) { + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { + Kernel<laplace_two_scalar_kernel<OType>, xpu>::Launch( + s, outputs[0].Size(), param.loc.value(), param.scale.value(), + laplace_tensor.dptr_, outputs[0].dptr<OType>()); + }); + } 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; + // int type_flag = param.t; Review comment: remove unused code ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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